Collaborating Authors


MLSys 2021: Bridging the divide between machine learning and systems


Machine learning MLSys 2021: Bridging the divide between machine learning and systems Amazon distinguished scientist and conference general chair Alex Smola on what makes MLSys unique -- both thematically and culturally. Email Alex Smola, Amazon vice president and distinguished scientist The Conference on Machine Learning and Systems ( MLSys), which starts next week, is only four years old, but Amazon scientists already have a rich history of involvement with it. Amazon Scholar Michael I. Jordan is on the steering committee; vice president and distinguished scientist Inderjit Dhillon is on the board and was general chair last year; and vice president and distinguished scientist Alex Smola, who is also on the steering committee, is this year's general chair. As the deep-learning revolution spread, MLSys was founded to bridge two communities that had much to offer each other but that were often working independently: machine learning researchers and system developers. Registration for the conference is still open, with the very low fees of $25 for students and $100 for academics and professionals. "If you look at the big machine learning conferences, they mostly focus on, 'Okay, here's a cool algorithm, and here are the amazing things that it can do. And by the way, it now recognizes cats even better than before,'" Smola says. "They're conferences where people mostly show an increase in capability. At the same time, there are systems conferences, and they mostly care about file systems, databases, high availability, fault tolerance, and all of that. "Now, why do you need something in-between? Well, because quite often in machine learning, approximate is good enough. You don't necessarily need such good guarantees from your systems.

Top Master's Programs In Machine Learning In The US


Organisations, regardless of size, are adopting emerging technologies like machine learning, data science, and AI to gain meaningful insights from large chunks of data in a bid to accelerate their growth. According to the Analytics and Data Science India Industry study 2020, advanced analytics, predictive modelling, and data science together account for 16% of the analytics revenues across enterprises. The rapid digital adoption has opened the skill gap wide. Many institutions across the world are now offering courses -- both online and offline -- to plug this gap. Here are the top ten Master's in Machine Learning in the US.

Combinatorial Bandits under Strategic Manipulations Artificial Intelligence

We study the problem of combinatorial multi-armed bandits (CMAB) under strategic manipulations of rewards, where each arm can modify the emitted reward signals for its own interest. Our setting elaborates a more realistic model of adaptive arms that imposes relaxed assumptions compared to adversarial corruptions and adversarial attacks. Algorithms designed under strategic arms gain robustness in real applications while avoiding being overcautious and hampering the performance. We bridge the gap between strategic manipulations and adversarial attacks by investigating the optimal colluding strategy among arms under the MAB problem. We then propose a strategic variant of the combinatorial UCB algorithm, which has a regret of at most $O(m\log T + m B_{max})$ under strategic manipulations, where $T$ is the time horizon, $m$ is the number of arms, and $B_{max}$ is the maximum budget. We further provide lower bounds on the strategic budgets for attackers to incur certain regret of the bandit algorithm. Extensive experiments corroborate our theoretical findings on robustness and regret bounds, in a variety of regimes of manipulation budgets.

Complicated legacies: The human genome at 20


Millions of people today have access to their personal genomic information. Direct-to-consumer services and integration with other “big data” increasingly commoditize what was rightly celebrated as a singular achievement in February 2001 when the first draft human genomes were published. But such remarkable technical and scientific progress has not been without its share of missteps and growing pains. Science invited the experts below to help explore how we got here and where we should (or ought not) be going. —Brad Wible By Kathryn Maxson Jones and Robert Cook-Deegan Sharing data can save lives. The “Bermuda Principles” for public data disclosure are a fundamental legacy of producing the first human reference DNA sequence during the Human Genome Project (HGP) ([ 1 ][1]). Since the 1990s, these principles have become a touchstone for open science. In February 1996, the leaders of the HGP gathered in Bermuda to discuss how to scale up production for a human reference DNA sequence. With some caveats, the consortium agreed that all sequencing centers would release their data online within 24 hours. Other examples of sharing data before publication existed, but most—such as the Protein Data Bank—restricted sharing of prepublication data to a small community of users, sometimes withholding data even after the related papers were published ([ 2 ][2]). At the time, the Bermuda Principles were distinctive in their aspiration that all HGP-funded sequences be released to anyone online within a day. Yet implementing this policy was hardly simple; the challenges that the HGP faced inform data sharing today ([ 3 ][3]). The Bermuda Principles required advocacy. This came from John Sulston and Robert Waterston, whose experiences with data sharing in Caenorhabditis elegans biology were the practical precedent for a radical idea. Context also mattered, and data release within 24 hours remained an aspirational ethos rather than a strict requirement. Flexibility allowed smaller centers to participate while also allowing the project to accommodate then-incompatible policies in Germany, France, Japan, and the United States. Finally, the policy required enforcement. Administrators from the HGP's largest patrons sent stern letters intended to make funders' policies conform to the Bermuda Principles, threatening expulsion from the international sequencing consortium. The Bermuda Principles have since been adapted to different communities and have served as an inspiration for many others ([ 4 ][4]). For example, rapid data sharing has been crucial in the current coronavirus crisis. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome was identified quickly and its sequence released on 10 January 2020, starting the clock on the development of vaccines and diagnostic tests. The COVID-19 Host Genetics Initiative disseminated data rapidly and openly, building on precedents such as the Global Initiative on Sharing All Influenza Data ([ 5 ][5], [ 6 ][6]). Of course, unfettered data sharing is not, and should not be, universal. Identifiable individual medical data, for instance, cannot be treated the same way as samples contributed to build a reference genome sequence. Many communities have adopted prepublication sharing strategies with considerable success, such as the various consortia for Alzheimer's research, the “open science” experiments at the Montreal Neurological Institute and the Mario Negri Institute, and the advances enabled by the Structural Genomics Consortium. The HGP set a high bar. Its core values of open science and rapid data flow persist, fomented by the urgency of rapid data sharing in biomedicine. By Charles N. Rotimi, Shawneequa L. Callier, Amy R. Bentley The long-term global impact of human genomics will be compromised, and our understanding of human history and biology hindered, if we continue to focus predominantly on individuals of European ancestry ([ 7 ][7]). Although we all share a recent common origin in Africa, and the genetic difference between any two individuals is small (0.1%), this translates to about 3 million points where individual genomes can vary, and the distribution of these human genetic variants (HGVs) is not random. It has long been understood that genomes (and exposures to key nongenetic factors) differ across ancestral and geographical backgrounds; nonetheless, genomics has largely focused on European-ancestry genomes. Presumably this is attributable to the availability of large, well-characterized datasets of European-ancestry individuals, academic and research networks that exclude and disadvantage underrepresented scholars ([ 8 ][8]), and the absence of publishing or funding motivation for large-scale genomics of diverse individuals. But diversity and representation are now being elevated from the purview of specialized research to a broad awareness across genomics. As this awareness develops, the field must grapple with understanding and communicating the implications: (i) Any two sub-Saharan Africans are more likely to be genetically different from each other than from an individual of European or Asian ancestry; (ii) a subset of HGVs can only be found in Africans because the small number of humans that left Africa about 100,000 years ago to populate the rest of the world carried a fraction of the variation that existed then; (iii) the African ecological environment has left its mark on human genomes (e.g., gene variants found to increase vulnerability to kidney failure) that are seen worldwide only in persons with ancestry from specific regions of Africa ([ 9 ][9]). Similarly, there are HGVs of health and historical importance that are rare or absent in African populations. For example, genomic regions harboring ancient DNA—the result of interbreeding with archaic human relatives (such as Neanderthals) in Asia, Europe, and the Americas—have biological functions , such as susceptibility to diabetes and viruses ([ 10 ][10]). For genomics-driven technologies and clinical and public health approaches to be deployed globally without exacerbating health inequalities, we must include individuals from diverse ancestral and geographical backgrounds. Growing prioritization of diverse populations in genomics research has begun to respond to these gaps. Programs such as TOPMed, All of Us, International Common Disease Alliance, Human Heredity and Health in Africa (H3Africa), Million Veteran Program, GenomeAsia, and the COVID global consortium contribute to advances in diversity and inclusion among research participants. The diversity of genomics researchers also merits continuing attention. The H3Africa initiative, for example, includes investments in training and infrastructure in each project, providing a blueprint for prioritizing capacity-building. The genomics community needs to value diverse samples in analyses and conclusions, as well as to focus resources on capacity-building and removing barriers to create a diverse workforce ([ 11 ][11]). By Hallam Stevens Over a few frenzied weeks in the middle of 2000, icing his wrists between coding sessions, Jim Kent, a graduate student at the University of California, Santa Cruz, created the first genome assembler software. GigAssembler pieced together the millions of fragments of DNA sequence generated at labs around the globe, literally making the human genome. At almost the same time, Celera Genomics acquired Paracel, a company that primarily designed software for intelligence gathering. Paracel owned specially designed text-matching hardware and software (the TRW Fast Data Finder) that was rapidly adapted for sniffing out genes within the vast spaces of the genome. Untangling the jumble of genomic letters required rapidly and accurately searching for a specified sequence within a very large space. This demanded new forms of training and disciplinary expertise. Physicists, mathematicians, and computer scientists brought methods such as linear programming, hashing, and hidden Markov models into biology. Since 2005, the Moore's Law–like growth of next-generation sequencing has generated everincreasing troves of data and required even faster algorithms for indexing and searching. Biology has borrowed “big data” methods from industry (e.g., Hadoop) but has also contributed to pushing the frontiers of computer science research (e.g., the Burrows-Wheeler transform) ([ 12 ][12]). The coalescence of bioinformatics and computational biology around algorithms has also given rise to new institutional forms and new markets for biomedicine. Statistically powered “data-driven biology” has configured an emerging medical-industrial complex that promises personalized and “precision” forms of diagnosis and treatment. Algorithmic pipelines that compare an individual's genotype to reference data generate a range of predictions about future health and risk. Direct-to-consumer genomics companies such as 23andMe now promise us healthier, happier, and longer ways of living via algorithms. This presents substantial challenges for privacy, data ownership, and algorithmic bias ([ 13 ][13]–[ 15 ][14]) that must be addressed if genomics is to avoid becoming a handmaiden of “surveillance capitalism” ([ 16 ][15]). Many tech companies have begun to look toward using machine learning to combine more and more biological data with other forms of personal data—where we go, what we buy, whom we associate with, what we like. The hopes for genomics have long been tempered by fears that the genome could reveal too much about ourselves, exposing us to new forms of discrimination, social division, or control. Algorithmic biology is depicting and predicting our bodies with growing accuracy, but it is also drawing biomedicine more closely into the orbits of corporate tech giants that are aggregating and attempting to monetize data. By Kathryn A. Phillips, Jeroen P. Jansen, Christopher F. Weyant Debates about precision medicine (PM), which uses genetic information to target interventions, commonly focus on whether we can “afford” PM ([ 17 ][16]), but focusing only on affordability, not also value, risks rejecting technologies that might make health care more efficient. Affordability is a question of whether we can pay for an intervention given its impact on budgets, whereas value can be measured by the health outcomes achieved per dollar spent for an intervention. Ideally, a PM intervention both saves money and improves outcomes; however, most health care interventions produce better outcomes at higher cost, and PM is no exception. By better distinguishing affordability and value, and by considering how we can address both, we can further the agenda of achieving affordable and valuable PM. The literature has generally not shown that PM is unaffordable or of low value; however, it has also not shown that PM is a panacea for reducing health care expenditures or always results in high-value care ([ 17 ][16]). Understanding PM affordability and value requires evidence on total costs and outcomes as well as potential cost offsets, but these data are difficult to capture because costs often occur up front while beneficial outcomes accrue over time ([ 18 ][17]). Also, PM could result in substantial downstream implications because of follow-up interventions, not only for patients but also for family members who may have inherited the same genetic condition. Emerging PM tests could be used for screening large populations and could include genome sequencing of all newborns, liquid biopsy testing to screen for cancers in routine primary care visits, and predictive testing for Alzheimer's disease in adults. These interventions may provide large benefits, but they are likely to require large up-front expenditures. Another complication is that many PM interventions measure multiple genes relevant to multiple conditions and provide myriad types of value, such as the personal value of this information to patients ([ 19 ][18]). Various methods have been developed for integrating affordability and value, but cost-effectiveness analyses often do not examine the budget impact, which can result in incomplete or contradictory conclusions ([ 20 ][19]). However, assessments that consider affordability and value simultaneously, such as those by the Institute for Clinical and Economic Review, are becoming more accepted by decisionmakers ([ 21 ][20]). The growing consideration of both affordability and value is less a result of methodological advances than of an increased focus on how to ensure sustainable and efficient health care (and the corresponding political will to do so). A positive consequence of this is an increase in research on how to best define and quantify affordability and value given the available data. PM is here to stay. However, it can only achieve its potential if it is both affordable and of high value. By Dorothy E. Roberts In the aftermath of the first publication of the human genome, researchers confirmed what many scholars had recognized for decades: that race is a social construct, not a natural division of human beings written in our genes ([ 22 ][21], [ 23 ][22]). Yet rather than hammer the final nail in the coffin, the human genome map sparked renewed interest in race-based genetic difference. The posting of recent genetic studies on white supremacist websites led the American Society of Human Genetics in 2018 to issue yet another statement denouncing genetics-based claims of racial purity as “scientifically meaningless,” while many geneticists failed to see how the biological concept of race was itself invented to support racism. None of this history has restrained the search for genetic differences between races and genetic explanations for various racial disparities (e.g., in COVID-19 outcomes), which in turn generates persistent public confusion about race and genetics. It is time to end the entanglement of race and genetics and to work toward a radically new understanding of human unity and diversity. There are two general approaches that can help guide innovative research questions and methods that no longer rely on invented racial classifications as if they were biological. First, genetic researchers should stop using race as a biological variable that can explain differences in health, disease, or responses to therapies ([ 24 ][23]). Treating race as a biological risk factor obscures how structural racism has biological effects and produces health disparities in racialized populations. Epigenetics offers promising models to investigate one pathway through which unequal social conditions get “embodied” or “under the skin” to generate disparate health outcomes. Still, researchers must use caution to avoid making deleterious epigenetic processes seem self-perpetuating and inevitable, taking attention away from structural inequities that caused the problem in the first place ([ 25 ][24]). Second, genetic researchers should stop using a white, European standard for human genetics and instead study a fuller range of human genetic variation. Projects dedicated to expanding genetic databases with DNA from groups on the African continent, for example, have shown that these populations are the most genetically diverse on Earth and refute the myth that there is a genetically distinguishable Black race ([ 26 ][25]). The aim of diversifying biomedical research should not be to find innate genetic differences between racial groups; rather, it should be to give persons from racialized populations equal access to the benefits of participating in highquality and ethical research (including clinical trials) and to give scientists a richer resource to understand human biology. In this way, genetic research can contribute to more individualized diagnoses and therapies that no longer rely on crude medical decisions based categorically on a patient's race. By Dina Zielinski and Yaniv Erlich In 2007, only two individuals had their full genome sequenced: Craig Venter and Jim Watson. Today, more than 30 million individuals have access to their detailed genomic datasets. This democratization of genomic data has helped to reunite families, fight racism, and promote genetic literacy ([ 27 ][26], [ 28 ][27]), but it has also enabled surveillance on a massive scale. The correlation of DNA variants between distant relatives means that relatively small databases can identify large parts of the population, including people who are not in the database ([ 29 ][28]). The high dimensionality of DNA data and linkage disequilibrium mean that efforts to obscure individual-level data, by pooling genomes or censoring parts of the genome, can fail unexpectedly ([ 30 ][29]). And with the advent of consumer genomics and third-party websites that allow participants to upload their genome data, it is increasingly easy to collect and access DNA data ([ 31 ][30]). We envision that the COVID-19 pandemic will accelerate genetic surveillance. People will likely see infectious disease surveillance, swabbing upon arrival, at border crossings, including airports. Governments can harness pandemic control infrastructure to build a DNA database of all arrivals. Such databases can identify a substantial portion of the visitor's home-country population because genetic re-identification is magnified through familial connections. But massive surveillance will not be restricted to government efforts. With the growing size of third-party genetic databases, essentially everyone with the right technical skills will be able to identify individuals. What are the implications of ubiquitous genetic surveillance? On the plus side, law enforcement agencies will be able to solve virtually all sexual assault cases. Screening at airports can help to reveal fraudulent identities, which is central in fighting human trafficking and espionage. However, the same technology can be used to target minorities or political opponents. The convergence of these applications underscores the importance of treading lightly with these new forensic superpowers. On the technical side, one theoretical mitigation option to limit such re-identification could include creating a trail that leads a genealogical tracing attempt to a fake identity. But this and other methods have yet to be investigated in a principled approach. Beyond technological countermeasures, the field needs guidelines concerning the use of genetic surveillance technologies. An important step is the interim policy laid out by the U.S. Department of Justice restricting forensic investigators' usage of third-party genetic databases to investigations of violent crimes, and only with sites that receive informed consent from users for such searches ([ 32 ][31]). Open public discussion is vital to further shape policies and expectations so as to harness the power of the genomic revolution for the benefit of the public. By Nanibaa' A. Garrison and Stephanie Russo Carroll Despite considerable advances in genomics research over the past two decades, Indigenous Peoples are incredibly underrepresented. Biological materials from Indigenous Peoples have been collected to study diseases, medical traits, and the origins of human populations, yet many studies have not benefited the participants or their communities. Some research has even created harms such as exacerbation of derogatory and detrimental stereotypes or challenges to cultural beliefs. Without productive relationships, Indigenous communities may not benefit from research in areas such as precision medicine and pharmacogenomics, and health disparities may remain unaddressed. Thus, many Indigenous Peoples are hesitant to participate in genomics research without extensive discussions and agreements to ensure that the results have individual and collective benefits, as well as to learn what happens to samples and how they are used ([ 33 ][32]). Indigenous scholars are developing guidance to address concerns and pave pathways for more equitable and beneficial research that aligns with the rights and interests of Indigenous Peoples ([ 34 ][33]). Culturally aligned research can increase Indigenous Peoples' participation in genomics research. The Summer Internship for Indigenous Peoples in Genomics (SING) trains and builds capacity for scientists and community members to shape research priorities of interest in their communities, and it has prompted the SING Consortium to develop a framework for ethical research engagement ([ 35 ][34]). The Center for the Ethics of Indigenous Genomic Research supports Indigenous-led research in biobanking and precision medicine that integrates sovereignty rights and Indigenous communities' ethical and cultural preferences. In Canada, Silent Genomes is creating an Indigenous Background Variant Library through close engagement with community and cultural advisors. Finally, in New Zealand, the Māori-developed Te Ara Tika framework integrates relationships, research design, cultural and social responsibility, justice, and equity as core interests for ethical genomic research with Māori people ([ 36 ][35]). Recognizing the need to foster self-determination and collective rights within open science and secondary use, the Global Indigenous Data Alliance's CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, and Ethics) complement the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) that make data machine-readable and usable in multiple contexts ([ 37 ][36], [ 38 ][37]). When operationalized together, CARE and FAIR enhance Indigenous leadership and innovation, leading to participatory governance and enabling opportunities for trust-building and accountability by incorporating Indigenous values and rights. For example, the creation of data standards and the use of Indigenous community-defined metadata can protect data while allowing them to be useful. The metadata become durable and persistent components of genomic information that provide guidance on future use, such as who has the authority to sanction that use, for what purposes, and to benefit whom ([ 34 ][33], [ 37 ][36]). An increased focus on rights and interests combined with enhanced engagement and capacity has the potential to reduce bias and produce more relevant and beneficial research for all. By Pilar N. Ossorio Polygenic risk scores (PRSs) are a rapidly emerging technology for aggregating the small effects of multiple polymorphisms across a person's genome into a single score. A PRS can be calculated for any phenotype for which genome-wide association data are available, usually by summing the weighted effect sizes of alleles ([ 39 ][38]). In medicine and public health, PRSs could be used for selecting therapies, initiating additional risk screening, or motivating behavior change. Whether they will be used in medicine depends on factors such as the degree to which they provide actionable risk information beyond that provided by clinical algorithms, the availability of information technology for calculating PRSs in clinical settings, and the availability of decision support tools. To date, PRSs have demonstrated moderate utility for complex medical phenotypes, including blood pressure, obesity, diabetes, depression, schizophrenia, and coronary heart disease. PRSs also highlight the complex intersection of race and ancestry in genomics. Substantiating and extending earlier work, a recent analysis showed that in 26 previous studies, PRSs performed significantly worse for people with predominantly African or South Asian ancestry than for people with predominantly European ancestry ([ 40 ][39], [ 41 ][40]). There was not enough data to assess performance for many groups (e.g., South East Asians, Pacific Islanders). Researchers have attributed this result to underrepresentation of non-European individuals and racial/ethnic minorities in datasets used to develop PRSs. Relative to people who are included in most genomic datasets, racial/ethnic minorities tend to have a greater portion of recent ancestry from places other than Europe. In response to the differential predictive power of PRSs, researchers have developed some PRSs specifically for people of predominantly African ancestry, and genome scientists are considering whether “ancestry-specific PRS are needed for every ethnic group…” ([ 42 ][41]). These developments occur as scholars of race call for an end to many uses of “race correction” in medicine ([ 43 ][42]). Appropriate attention to genetic ancestry's effects on PRSs can easily collapse into an ill-informed focus on race, without considering how social inequalities shape health and how race is an imperfect proxy for ancestry. Society needs a multidisciplinary approach for developing and implementing PRSs for diverse communities. Otherwise, ancestry-specific PRSs could reinvigorate people's misconceptions about human races as genetically distinct groups and encourage mistaken views that trait distribution between racial/ethnic groups is primarily caused by genetics ([ 39 ][38]). Such beliefs are central to white supremacy and racist medical practices. Injustice in science can occur because some groups of people are not included ([ 44 ][43]), but injustice can also result from inappropriate inclusion. By Yves Moreau and Maya Wang The use of DNA profiling for individual cases of law enforcement has helped to identify suspects and to exonerate the innocent. But retaining genetic materials in the form of national DNA databases, which have proliferated globally in the past two decades, raises important human rights questions. Landmark court decisions in Europe and in the United States set some limits on data collection and retention in DNA databases, such as restricting long-term retention of DNA profiles to people arrested for or convicted of a crime. But these decisions are far from the comprehensive regulations we need. Privacy rights are fundamental human rights. Around the world, the unregulated collection, use, and retention of DNA has become a form of genomic surveillance. Kuwait passed a now-repealed law mandating the DNA profiling of the entire population. In China, the police systematically collected blood samples from the Xinjiang population under the guise of a health program, and the authorities are working to establish a Y-chromosome DNA database covering the country's male population. Thailand authorities are establishing a targeted genetic database of Muslim minorities ([ 45 ][44]). Under policies set by the previous administration, the U.S. government has been indiscriminately collecting the genetic materials of migrants, including refugees, at the Mexican border. As the technology gets cheaper, and as the adoption of surveillance gets ever broader, there is an acute risk of pervasive genomic surveillance, not only by authoritarian regimes but also in democracies with weakening rights. But such a loss of autonomy and freedom is not inevitable. Governments should reform surveillance laws and draft comprehensive privacy protections that tightly regulate the collection, use, and retention of DNA and other biometric identifiers ([ 46 ][45]). They should ban such activities when they do not meet international human rights standards of lawfulness, proportionality, and necessity. They should develop a coordinated global regime of export control legislation, as well as sanctions akin to the U.S. Magnitsky Act, to hold businesses accountable that recklessly supply or market this technology for genomic surveillance. Journal editors and publishers should reassess hundreds of ethically suspect DNA-profiling publications—for example, publications co-authored by police forces involved in the persecution of the minorities studied ([ 47 ][46]) or lacking proper consent or ethical approval ([ 48 ][47]). Although there have been a few retractions ([ 47 ][46], [ 48 ][47]), such assessments should not be limited to the bureaucratic verification of informed consent and ethical approval documents; they also need to consider the basic ethical principles of beneficence, nonmaleficence, autonomy, justice, and faithfulness. The scientific community should also refuse to cooperate with law enforcement anywhere in the world that is proven to be violating human rights standards, in particular the Chinese police and military. 1. [↵][48]1. E. S. Lander et al ., Nature 409, 860 (2001). [OpenUrl][49][CrossRef][50][PubMed][51][Web of Science][52] 2. [↵][53]1. B. J. Strasser , Isis 102, 60 (2011). [OpenUrl][54][CrossRef][55][PubMed][56][Web of Science][57] 3. [↵][58]1. K. M. Jones, 2. R. A. Ankeny, 3. R. Cook-Deegan , J. Hist. Biol. 51, 693 (2018). [OpenUrl][59] 4. [↵][60]National Human Genome Research Institute, NHGRI Genomic Data Sharing (GDS) Policy: Data Standards (2020). 5. [↵][61]1. N. L. Yozwiak, 2. S. F. Schaffner, 3. P. C. Sabeti , Nature 518, 477 (2015). [OpenUrl][62][CrossRef][63][PubMed][64] 6. [↵][65]“Benefits of sharing” [editorial], Nature 530, 129 (2016). [OpenUrl][66] 7. [↵][67]1. A. R. Bentley, 2. S. Callier, 3. C. N. Rotimi , J. Community Genet. 8, 255 (2017). [OpenUrl][68][CrossRef][69] 8. [↵][70]1. T. A. Hoppe et al ., Sci. Adv. 5, eaaw7238 (2019). [OpenUrl][71][FREE Full Text][72] 9. [↵][73]1. G. N. Nadkarni et al ., N. Engl. J. Med. 379, 2571 (2018). [OpenUrl][74][PubMed][75] 10. [↵][76]1. Y. Luo , Nature 587, 552 (2020). [OpenUrl][77] 11. [↵][78]1. E. D. Green et al ., Nature 586, 683 (2020). [OpenUrl][79][CrossRef][80] 12. [↵][81]1. H. Stevens , Biosocieties 11, 352 (2016). [OpenUrl][82] 13. [↵][83]1. D. Grishin, 2. K. Obbad, 3. G. M. Church , Nat. Biotechnol. 37, 1115 (2019). [OpenUrl][84] 14. 1. L. Bonomi, 2. Y. Huang, 3. L. Ohno-Machado , Nat. Genet. 52, 646 (2020). [OpenUrl][85] 15. [↵][86]1. K. Ferryman, 2. M. Pitcan , Fairness in Precision Medicine (Data & Society, 2019). 16. [↵][87]1. S. Zuboff , The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier (Profile, 2019). 17. [↵][88]1. D. M. Cutler , JAMA 323, 109 (2020). [OpenUrl][89] 18. [↵][90]1. K. A. Phillips, 2. M. P. Douglas, 3. D. A. Marshall , JAMA 324, 2029 (2020). [OpenUrl][91] 19. [↵][92]1. E. Faulkner et al ., Value Health 23, 529 (2020). [OpenUrl][93] 20. [↵][94]1. A. Towse, 2. J. A. Mauskopf , Value Health 21, 249 (2018). [OpenUrl][95] 21. [↵][96]1. S. D. Pearson , Value Health 21, 258 (2018). [OpenUrl][97][CrossRef][98][PubMed][99] 22. [↵][100]1. J. L. Graves Jr. , The Emperor's New Clothes: Biological Theories of Race at the Millennium (Rutgers Univ. Press, 2001). 23. [↵][101]1. D. Roberts , Fatal Invention: How Science, Politics, and Big Business Re-Create Race in the Twenty-First Century (New Press, 2012). 24. [↵][102]1. M. Yudell, 2. D. Roberts, 3. R. DeSalle, 4. S. Tishkoff , Science 351, 564 (2016). [OpenUrl][103][Abstract/FREE Full Text][104] 25. [↵][105]1. D. E. Roberts, 2. O. Rollins , Annu. Rev. Sociol. 46, 195 (2020). [OpenUrl][106] 26. [↵][107]1. M. C. Campbell, 2. S. A. Tishkoff , Annu. Rev. Genomics Hum. Genet. 9, 403 (2008). [OpenUrl][108][CrossRef][109][PubMed][110][Web of Science][111] 27. [↵][112]1. A. Panofsky, 2. J. Donovan , Soc. Stud. Sci. 49, 653 (2019). [OpenUrl][113] 28. [↵][114]1. J. S. Roberts et al ., Publ. Health Genomics 20, 36 (2017). [OpenUrl][115] 29. [↵][116]1. Y. Erlich, 2. T. Shor, 3. I. Pe'er, 4. S. Carmi , Science 362, 690 (2018). [OpenUrl][117][Abstract/FREE Full Text][118] 30. [↵][119]1. D. R. Nyholt, 2. C.-E. Yu, 3. P. M. Visscher , Eur. J. Hum. Genet. 17, 147 (2009). 31. [↵][120]1. S. C. Nelson, 2. D. J. Bowen, 3. S. M. Fullerton , Am. J. Hum. Genet. 105, 122 (2019). [OpenUrl][121][CrossRef][122] 32. [↵][123]1. J. Kaiser , Science 10.1126/science.aaz6336 (25 September 2019). 33. [↵][124]1. R. Taitingfong et al ., J. Am. Med. Inform. Assoc. 27, 1987 (2020). [OpenUrl][125] 34. [↵][126]1. M. Hudson et al ., Nat. Rev. Genet. 21, 377 (2020). [OpenUrl][127] 35. [↵][128]1. K. G. Claw et al ., Nat. Commun. 9, 2957 (2018). [OpenUrl][129][CrossRef][130][PubMed][131] 36. [↵][132]1. M. Hudson et al ., He Tangata Kei Tua—Guidelines for Biobanking with Maori (Te Mata Hautu Taketake—Māori & Indigenous Governance Centre, Univ. of Waikato, 2016). 37. [↵][133]1. S. R. Carroll et al ., Data Sci. J. 19, 43 (2020). [OpenUrl][134] 38. [↵][135]1. M. D. Wilkinson et al ., Sci. Data 3, 160018 (2016). [OpenUrl][136] 39. [↵][137]1. N. A. Rosenberg et al ., Evol. Med. Public Health 2019, 26 (2019). [OpenUrl][138][CrossRef][139][PubMed][140] 40. [↵][141]1. L. Duncan et al ., Nat. Commun. 10, 3328 (2019). [OpenUrl][142][PubMed][140] 41. [↵][143]1. A. R. Martin et al ., Am. J. Hum. Genet. 100, 635 (2017). [OpenUrl][144][CrossRef][145][PubMed][146] 42. [↵][147]National Human Genome Research Institute, Genomic Medicine XII: Genomics and Risk Prediction, Executive Summary (2019); [\_Executive\_Summary.pdf][148]. 43. [↵][149]1. D. E. Roberts , Lancet 397, 17 (2021). [OpenUrl][150] 44. [↵][151]1. A. R. Martin et al ., Nat. Genet. 51, 584 (2019). [OpenUrl][152][CrossRef][153][PubMed][154] 45. [↵][155]UN Committee on the Elimination of Racial Discrimination, Letter to the Permanent Representative of Thailand to the United Nations Office (15 May 2015); [][156]. 46. [↵][157]Forensic Genetics Policy Initiative, Establishing Best Practices for Forensic DNA Databases (2017); [][158]. 47. [↵][159]1. D. Zhang et al ., Int. J. Legal Med. 10.1007/s00414-019-02049-6 \[retracted\] (2019). 48. [↵][160]1. X. Pan et al ., Int. J. Legal Med. 134, 2079 \[retracted\] (2020). [OpenUrl][161] Acknowledgments: K.M.J. and R.C.-D. were funded by National Cancer Institute (NCI) grant R01 CA237118. C.N.R., S.L.C., and A.R.B. were supported in part by the Intramural Research Program of NIH through the Center for Research on Genomics and Global Health (CRGGH) at the National Human Genome Research Institute (NHGRI). The CRGGH is also supported by the National Institute of Diabetes and Digestive and Kidney Diseases. K.A.P. is a consultant to Illumina Inc. and was funded by NCI grant R01 CA221870 and NHGRI grant U01 HG009599. J.P.J. is a part-time salaried employee of the Precision Medicine Group. D.Z. and Y.E. hold equity in DNA.Land, a third-party genetic service. D.Z. is an employee of Cibiltech; Y.E. is an employee of Eleven Therapeutics, MyHeritage, and consultant to ArcBio. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policies or positions of any of their employers. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-15 [15]: #ref-16 [16]: #ref-17 [17]: #ref-18 [18]: #ref-19 [19]: #ref-20 [20]: #ref-21 [21]: #ref-22 [22]: #ref-23 [23]: #ref-24 [24]: #ref-25 [25]: #ref-26 [26]: #ref-27 [27]: #ref-28 [28]: #ref-29 [29]: #ref-30 [30]: #ref-31 [31]: #ref-32 [32]: #ref-33 [33]: #ref-34 [34]: #ref-35 [35]: #ref-36 [36]: #ref-37 [37]: #ref-38 [38]: #ref-39 [39]: #ref-40 [40]: #ref-41 [41]: #ref-42 [42]: #ref-43 [43]: #ref-44 [44]: #ref-45 [45]: #ref-46 [46]: #ref-47 [47]: #ref-48 [48]: #xref-ref-1-1 "View reference 1 in text" [49]: {openurl}?query=rft.jtitle%253DNature%26rft.stitle%253DNature%26rft.aulast%253DLander%26rft.auinit1%253DE.%2BS.%26rft.volume%253D409%26rft.issue%253D6822%26rft.spage%253D860%26rft.epage%253D921%26rft.atitle%253DInitial%2Bsequencing%2Band%2Banalysis%2Bof%2Bthe%2Bhuman%2Bgenome.%26rft_id%253Dinfo%253Adoi%252F10.1038%252F35057062%26rft_id%253Dinfo%253Apmid%252F11237011%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [50]: /lookup/external-ref?access_num=10.1038/35057062&link_type=DOI [51]: /lookup/external-ref?access_num=11237011&link_type=MED&atom=%2Fsci%2F371%2F6529%2F564.atom [52]: /lookup/external-ref?access_num=000166938800058&link_type=ISI [53]: #xref-ref-2-1 "View reference 2 in text" [54]: {openurl}?query=rft.jtitle%253DIsis%26rft.stitle%253DIsis%26rft.aulast%253DStrasser%26rft.auinit1%253DB.%2BJ.%26rft.volume%253D102%26rft.issue%253D1%26rft.spage%253D60%26rft.epage%253D96%26rft.atitle%253DThe%2Bexperimenter%2527s%2Bmuseum%253A%2BGenBank%252C%2Bnatural%2Bhistory%252C%2Band%2Bthe%2Bmoral%2Beconomies%2Bof%2Bbiomedicine.%26rft_id%253Dinfo%253Adoi%252F10.1086%252F658657%26rft_id%253Dinfo%253Apmid%252F21667776%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [55]: /lookup/external-ref?access_num=10.1086/658657&link_type=DOI [56]: /lookup/external-ref?access_num=21667776&link_type=MED&atom=%2Fsci%2F371%2F6529%2F564.atom [57]: /lookup/external-ref?access_num=000289868600003&link_type=ISI [58]: #xref-ref-3-1 "View reference 3 in text" [59]: {openurl}?query=rft.jtitle%253DJ.%2BHist.%2BBiol.%26rft.volume%253D51%26rft.spage%253D693%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [60]: #xref-ref-4-1 "View reference 4 in text" [61]: #xref-ref-5-1 "View reference 5 in text" [62]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D518%26rft.spage%253D477%26rft_id%253Dinfo%253Adoi%252F10.1038%252F518477a%26rft_id%253Dinfo%253Apmid%252F25719649%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [63]: /lookup/external-ref?access_num=10.1038/518477a&link_type=DOI [64]: /lookup/external-ref?access_num=25719649&link_type=MED&atom=%2Fsci%2F371%2F6529%2F564.atom [65]: #xref-ref-6-1 "View reference 6 in text" [66]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D530%26rft.spage%253D129%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [67]: #xref-ref-7-1 "View reference 7 in text" [68]: {openurl}?query=rft.jtitle%253DJ.%2BCommunity%2BGenet.%26rft.volume%253D8%26rft.spage%253D255%26rft_id%253Dinfo%253Adoi%252F10.1007%252Fs12687-017-0316-6%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [69]: /lookup/external-ref?access_num=10.1007/s12687-017-0316-6&link_type=DOI [70]: #xref-ref-8-1 "View reference 8 in text" [71]: {openurl}?query=rft.jtitle%253DScience%2BAdvances%26rft.stitle%253DSci%2BAdv%26rft.aulast%253DHoppe%26rft.auinit1%253DT.%2BA.%26rft.volume%253D5%26rft.issue%253D10%26rft.spage%253Deaaw7238%26rft.epage%253Deaaw7238%26rft.atitle%253DTopic%2Bchoice%2Bcontributes%2Bto%2Bthe%2Blower%2Brate%2Bof%2BNIH%2Bawards%2Bto%2BAfrican-American%252Fblack%2Bscientists%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fsciadv.aaw7238%26rft_id%253Dinfo%253Apmid%252F31633016%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [72]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6MzoiUERGIjtzOjExOiJqb3VybmFsQ29kZSI7czo4OiJhZHZhbmNlcyI7czo1OiJyZXNpZCI7czoxMzoiNS8xMC9lYWF3NzIzOCI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3MS82NTI5LzU2NC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [73]: #xref-ref-9-1 "View reference 9 in text" [74]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D379%26rft.spage%253D2571%26rft_id%253Dinfo%253Apmid%252F30586505%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [75]: /lookup/external-ref?access_num=30586505&link_type=MED&atom=%2Fsci%2F371%2F6529%2F564.atom [76]: #xref-ref-10-1 "View reference 10 in text" [77]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D587%26rft.spage%253D552%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [78]: #xref-ref-11-1 "View reference 11 in text" [79]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D586%26rft.spage%253D683%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41586-020-2817-4%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [80]: /lookup/external-ref?access_num=10.1038/s41586-020-2817-4&link_type=DOI [81]: #xref-ref-12-1 "View reference 12 in text" [82]: {openurl}?query=rft.jtitle%253DBiosocieties%26rft.volume%253D11%26rft.spage%253D352%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [83]: #xref-ref-13-1 "View reference 13 in text" [84]: {openurl}?query=rft.jtitle%253DNat.%2BBiotechnol.%26rft.volume%253D37%26rft.spage%253D1115%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [85]: {openurl}?query=rft.jtitle%253DNat.%2BGenet.%26rft.volume%253D52%26rft.spage%253D646%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [86]: #xref-ref-15-1 "View reference 15 in text" [87]: #xref-ref-16-1 "View reference 16 in text" [88]: #xref-ref-17-1 "View reference 17 in text" [89]: {openurl}?query=rft.jtitle%253DJAMA%26rft.volume%253D323%26rft.spage%253D109%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [90]: #xref-ref-18-1 "View reference 18 in text" [91]: {openurl}?query=rft.jtitle%253DJAMA%26rft.volume%253D324%26rft.spage%253D2029%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [92]: #xref-ref-19-1 "View reference 19 in text" [93]: {openurl}?query=rft.jtitle%253DValue%2BHealth%26rft.volume%253D23%26rft.spage%253D529%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [94]: #xref-ref-20-1 "View reference 20 in text" [95]: {openurl}?query=rft.jtitle%253DValue%2BHealth%26rft.volume%253D21%26rft.spage%253D249%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [96]: #xref-ref-21-1 "View reference 21 in text" [97]: {openurl}?query=rft.jtitle%253DValue%2BHealth%26rft.volume%253D21%26rft.spage%253D258%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.jval.2017.12.017%26rft_id%253Dinfo%253Apmid%252F29566831%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [98]: /lookup/external-ref?access_num=10.1016/j.jval.2017.12.017&link_type=DOI [99]: /lookup/external-ref?access_num=29566831&link_type=MED&atom=%2Fsci%2F371%2F6529%2F564.atom [100]: #xref-ref-22-1 "View reference 22 in text" [101]: #xref-ref-23-1 "View reference 23 in text" [102]: #xref-ref-24-1 "View reference 24 in text" [103]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DYudell%26rft.auinit1%253DM.%26rft.volume%253D351%26rft.issue%253D6273%26rft.spage%253D564%26rft.epage%253D565%26rft.atitle%253DTaking%2Brace%2Bout%2Bof%2Bhuman%2Bgenetics%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aac4951%26rft_id%253Dinfo%253Apmid%252F26912690%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [104]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNTEvNjI3My81NjQiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUyOS81NjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [105]: #xref-ref-25-1 "View reference 25 in text" [106]: {openurl}?query=rft.jtitle%253DAnnu.%2BRev.%2BSociol.%26rft.volume%253D46%26rft.spage%253D195%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [107]: #xref-ref-26-1 "View reference 26 in text" [108]: {openurl}?query=rft.jtitle%253DAnnual%2Breview%2Bof%2Bgenomics%2Band%2Bhuman%2Bgenetics%26rft.stitle%253DAnnu%2BRev%2BGenomics%2BHum%2BGenet%26rft.aulast%253DCampbell%26rft.auinit1%253DM.%2BC.%26rft.volume%253D9%26rft.spage%253D403%26rft.epage%253D433%26rft.atitle%253DAfrican%2Bgenetic%2Bdiversity%253A%2Bimplications%2Bfor%2Bhuman%2Bdemographic%2Bhistory%252C%2Bmodern%2Bhuman%2Borigins%252C%2Band%2Bcomplex%2Bdisease%2Bmapping.%26rft_id%253Dinfo%253Adoi%252F10.1146%252Fannurev.genom.9.081307.164258%26rft_id%253Dinfo%253Apmid%252F18593304%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [109]: /lookup/external-ref?access_num=10.1146/annurev.genom.9.081307.164258&link_type=DOI [110]: /lookup/external-ref?access_num=18593304&link_type=MED&atom=%2Fsci%2F371%2F6529%2F564.atom [111]: /lookup/external-ref?access_num=000259629000021&link_type=ISI [112]: #xref-ref-27-1 "View reference 27 in text" [113]: {openurl}?query=rft.jtitle%253DSoc.%2BStud.%2BSci.%26rft.volume%253D49%26rft.spage%253D653%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [114]: #xref-ref-28-1 "View reference 28 in text" [115]: {openurl}?query=rft.jtitle%253DPubl.%2BHealth%2BGenomics%26rft.volume%253D20%26rft.spage%253D36%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [116]: #xref-ref-29-1 "View reference 29 in text" [117]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aau4832%26rft_id%253Dinfo%253Apmid%252F30309907%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [118]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNjIvNjQxNS82OTAiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUyOS81NjQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [119]: #xref-ref-30-1 "View reference 30 in text" [120]: #xref-ref-31-1 "View reference 31 in text" [121]: 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"View reference 37 in text" [134]: {openurl}?query=rft.jtitle%253DData%2BSci.%2BJ.%26rft.volume%253D19%26rft.spage%253D43%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [135]: #xref-ref-38-1 "View reference 38 in text" [136]: {openurl}?query=rft.jtitle%253DSci.%2BData%26rft.volume%253D3%26rft.spage%253D160018%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [137]: #xref-ref-39-1 "View reference 39 in text" [138]: 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#xref-ref-42-1 "View reference 42 in text" [148]: [149]: #xref-ref-43-1 "View reference 43 in text" [150]: {openurl}?query=rft.jtitle%253DLancet%26rft.volume%253D397%26rft.spage%253D17%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [151]: #xref-ref-44-1 "View reference 44 in text" [152]: {openurl}?query=rft.jtitle%253DNat.%2BGenet.%26rft.volume%253D51%26rft.spage%253D584%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41588-019-0379-x%26rft_id%253Dinfo%253Apmid%252F30926966%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [153]: /lookup/external-ref?access_num=10.1038/s41588-019-0379-x&link_type=DOI [154]: /lookup/external-ref?access_num=30926966&link_type=MED&atom=%2Fsci%2F371%2F6529%2F564.atom [155]: #xref-ref-45-1 "View reference 45 in text" [156]: [157]: #xref-ref-46-1 "View reference 46 in text" [158]: [159]: #xref-ref-47-1 "View reference 47 in text" [160]: #xref-ref-48-1 "View reference 48 in text" [161]: {openurl}?query=rft.jtitle%253DInt.%2BJ.%2BLegal%2BMed.%26rft.volume%253D134%26rft.spage%253D2079%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx

Neural Contextual Bandits with Deep Representation and Shallow Exploration Machine Learning

Multi-armed bandits (MAB) (Auer et al., 2002; Audibert et al., 2009; Lattimore and Szepesvári, 2020) are a class of online decision-making problems where an agent needs to learn to maximize its expected cumulative reward while repeatedly interacting with a partially known environment. Based on a bandit algorithm (also called a strategy or policy), in each round, the agent adaptively chooses an arm, and then observes and receives a reward associated with that arm. Since only the reward of the chosen arm will be observed (bandit information feedback), a good bandit algorithm has to deal with the exploration-exploitation dilemma: tradeoff between pulling the best arm based on existing knowledge/history data (exploitation) and trying the arms that have not been fully explored (exploration). In many real-world applications, the agent will also be able to access detailed contexts associated with the arms. For example, when a company wants to choose an advertisement to present to a user, the recommendation will be much more accurate if the company takes into consideration the contents, specifications, and other features of the advertisements in the arm set as well as the profile of the user. To encode the contextual information, contextual bandit models and algorithms have been developed, and widely studied both in theory and in practice (Dani et al., 2008; Rusmevichientong

A Survey on Data Pricing: from Economics to Data Science Artificial Intelligence

How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.

Innovation Trailblazers Webinar Mini Series - Big Data, Analytics & The Future of Insurance - Silicon Valley Insurance Accelerator


In this Webinar Series leading innovators from Startups, solution providers and insurance business lines share their vision for how data and analytics will shape the future of insurance. This includes the on product and business models, customer engagement, distribution, underwriting and claims. Thought leaders and innovators share their vision and examples of the emerging data sources and data, analytic, AI & Machine Learning models and capabilities and how those will shape the future of insurance within and across business lines. Thought leaders and innovators discuss how the use of data and analytics is enabling the new insurance business models and shifting the insurance paradigm to value added personalized services that help customers better achieve life and business objectives. Thought leaders and innovators discuss the data driven future of customer engagement and distribution and how it will change insurance.

Predict Data Science Salaries with Data Science


Among those 12,360 data science job hires in this study (data source: picklesueat and Glassdoor), Data Analyst hires top the chart by 27%. Data Engineers are the second largest group, occupying 23% of all positions. Only 17% are Business Analysts and 10% are Data Scientists. All analyst-type hires combined dominate the total hires by 67%. However, from the graph below, we see the overall salary distribution of Data Scientists locates much more to the right comparing with those of Data Engineers and Analysts, showing Data Scientists' salaries are obviously higher.

A reading list for uncertain times


From an incisive ethnography of predictive policing to a compelling indictment of technology-enabled learning tools, the books on this year's fall reading list offer valuable context to the myriad challenges currently facing humanity. Dive deep into a public health disaster shrouded in secrecy, sit with the uncomfortable questions raised by a fictional foray into the future of intimacy, confront the challenges to sustainable development posed by environmental racism, and learn what a QR-coded chicken in rural China portends about the future of agriculture. When you are through, sit back and marvel at the odds stacked against humanity from the start with an entertaining romp through evolution and then leave your earthly worries behind with an ambitious tour of the Solar System. —Valerie Thompson Reviewed by Ivor Knight 1 Through a series of chance events, the pathogen we now know as severe acute respiratory syndrome coronavirus 2 emerged in 2019 and infected millions of humans within a span of 6 months. But chance has driven more than just the planet's latest pandemic. In his new book, A Series of Fortunate Events: Chance and the Making of the Planet, Life, and You , Sean B. Carroll takes readers on an entertaining tour of biological discovery that emphasizes the dominant role played by chance in shaping the conditions for life on Earth. Along the way, he provides insights and humor that make the book a quick, lively read that both educates and entertains. Carroll begins with one of the most consequential chance events to have occurred in the history of our planet: the Cretaceous-Paleogene asteroid impact on the Yucatán Peninsula that resulted in the extinction of the dinosaurs and expansion of mammals. Given Earth's rotational speed, if the asteroid had hit 30 minutes earlier or later, scientists believe it would have made a much less consequential impact, landing in either the Atlantic or Pacific Ocean. If that had happened, there might still be dinosaurs today, but no humans. As he does throughout the book, Carroll compares the example from science with an example from popular culture, describing the comedian Seth MacFarlane's good fortune to have narrowly missed (by 30 minutes) one of the flights that was hijacked on 11 September 2001. Fundamental topics such as the roles that mutation and natural selection play in the evolution of diverse life-forms, the genetics of human reproduction, cellular mechanisms of acquired immunity, and the development of cancer are all treated within a framework where chance dominates. Carroll explains in detail how chance creates the genetic diversity upon which natural selection acts and results in the richness of species on Earth, as well as how random combinations among just 163 gene segments make possible a human immune system that can produce up to 10 billion different antibodies. Readers will likely be particularly interested to learn that their genome is only one of the 70 trillion possibilities that could have been produced by their parents. Written in a conversational style, the book reads like an updated version of Jacques Monod's 1970 Chance and Necessity that speaks directly to the reader, making complex subject matter more accessible. There is also a suggested reading list and an extensive bibliography included for further exploration. Carroll's central argument, that we are all here by luck, is certainly clear and compelling. What we choose to do with that luck, however, is where things really get interesting. Books such as this remind us to make our unlikely time here count. Reviewed by Gillian Bowser 2 Does a hurricane discriminate between the wealthy and the poor? Do earthquakes target specific victims? How does systemic racism influence development goals? In academic explorations of sustainable development and environmental responsibilities, our assumptions about the relationship between income and energy consumption remain largely rooted in the idea that social inequalities decrease as countries develop, thus reducing environmental inequality. No such relationship appears to actually exist. In his sobering but essential new book, Unsustainable Inequalities , economist Lucas Chancel explores the intersections of social justice and environmental sustainability with a focus on global goals established at the 2012 United Nations Conference on Sustainable Development, which informed the underlying philosophy of the 2015 Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC) ([ 1 ][1]). Framing his narrative through the lens of intragenerational economic inequalities, he identifies social inequality as a core driver of environmental unsustainability that leads to a vicious circle wherein the rich consume more and the poor lose access to environmental resources and become increasingly vulnerable to environmental shocks. In 1987, the World Commission on Environment and Development issued a report called “Our Common Future” that defined sustainable development as “development that meets the need of the present without compromising the ability of future generations to meet their own needs” ([ 2 ][2]). The idea of intergenerational environmental equity became a cornerstone concept, shifting climate policy toward the common but differentiated responsibilities enshrined in the UNFCCC. Yet questions about intergenerational responsibility and the equitable impacts of climate change and environmental degradation remain. Environmental racism, wherein communities of color are disproportionately exposed to environmental risks, is inseparable from social justice, Chancel argues, and the attainment of sustainable development that also protects the environment across generations is “extremely difficult” without first addressing economic inequality within a single generation. The notion that we may be able to attain sustainable development and achieve equal responsibility for environmental degradation feels more unreachable than ever in a world upended by a global pandemic. In prepandemic times, many nations had already failed to implement or participate in local and global environmental justice efforts, and taxation schemes to level responsibilities for environmental pollution have proven wildly unpopular. And while Chancel argues that common indicator frameworks such as the United Nations' Sustainable Development Goals encourage nations to learn from one another, the continued rise of social inequality is a stark reminder of the difficult road ahead. Reviewed by Kanwal Singh 3 As the pandemic forces so many school systems and learning institutions to move online, the desire to educate students well using online tools and platforms is more pressing than ever. But as Justin Reich illustrates in his new book, Failure to Disrupt , there are no easy solutions or one-size-fits-all tools that can aid in this transition, and many recent technologies that were expected to radically change schooling have instead been used in ways that perpetuate existing systems and their attendant inequalities. The first half of the book discusses the brief histories, limited successes, and challenges of three types of large-scale technology-driven learning environments: instructor-guided, such as lectures taught through massive open online courses (MOOCs); algorithm-guided (e.g., Khan Academy); and peer-guided (e.g., the online coding community known as Scratch). Reich gives a solid accounting of the conditions needed for success with these models, the difficulties and limitations involved in adopting them in K–12 schooling, and the challenges that arise when we attempt to compare different approaches to one another. He argues that although we might think that the availability of a technology is its biggest limiter, the truth is that educational systems are simply not constructed to allow for experimentation and new ways of learning. Reich describes himself as committed to “methodological pluralism.” He supports the use of an array of learning tools and mechanisms, although he confesses to a particular admiration for peer-guided environments. He argues, however, that the incentive structures in formal education do not encourage the more innovative and deeper learning that can blossom in these environments. If we insist on maintaining current methods of assessment and ranking, which center on individual achievement, then peer-guided instruction will remain relegated to the sidelines. The second part of the book expands on the challenges of implementing educational technologies. Reich's main argument here is that educational systems are inherently conservative and that change will happen, albeit slowly and incrementally, only if technology designers, teachers, and administrators work in partnership to understand the desired learning goals and the parameters that define and constrain the learning environments. One of the most intractable pieces of the educational technology puzzle is the need to effectively conduct large-scale assessment, especially when the skills being assessed are not things that computers can do. Here, Reich cites a humorous example of an automated grading system giving high marks to an essay that begins with the technically grammatically correct sentence: “Educatee on an assassination will always be a part of mankind.” At the end of the book, Reich offers four questions that he finds especially useful to consider when examining a new large-scale educational technology. Perhaps the most useful question is the first: “What's new?” Despite what “edtech evangelists” might claim, new technologies often have closely related ancestors that can help predict their success, he argues. In the end, however, new technologies alone are unlikely to have a substantial impact on schooling. We must also be open to changing educational goals and expectations according to the possibilities offered by emergent technologies. Reviewed by Arti Garg 4 In Blockchain Chicken Farm , Xiaowei Wang reveals the myriad ways that technology is transforming our lives. They unveil, for example, the unexpected connections that exist between industrial oyster farming in rural China, livestream-fueled multilevel marketing schemes in the United States, and the app-enabled gig economy in which Chinese influencers participate. Following the threads of places and people woven together by new technologies, Wang helps readers trace the patterns emerging in the tapestry of our tech-infused world. Each chapter provides a view into not just how we use technology but why and to what end. Emphasizing the often-hidden human engine that powers our app-driven economy, Wang exposes the flaw in our tendency to conflate societal and cultural aspirations with the promises of technology and challenges us to honestly measure what value technology delivers. In the 21st century, they argue, we demand that technologists solve the problems that our governments and communities have not. In doing so, we inadvertently empower companies to exploit and amplify those same problems. Most of Wang's vignettes relate to Chinese agriculture. This decision, which roots the narrative in the visceral language of human sustenance, grounds the heady subject matter. The titular example takes readers to the GoGoChicken farm in Sanqiao, a “dreamlike” village that sits in one of the poorest regions in China. Here, Wang introduces the straw-hatted “Farmer Jiang,” who has partnered with his village government and a blockchain company to sell free-range chickens via an e-commerce site. Jiang's chickens sell for RMB 300 (∼$35) each, an amount equal to 6% of the average annual household income in that part of China. Wang explains that high-profile failures of regulatory oversight have left many Chinese with a deep distrust of the food supply chain and that upper-class Chinese urbanites will pay a premium for reassurance about food safety, which, in this case, takes the form of a vacuum-sealed chicken that comes with a QR code revealing blockchain-logged details of its life on the farm. Wang suggests that Americans, driven by concerns over animal welfare, may desire similar reassurance about their food's provenance. In both China and America, they observe, technology allows the upper class to buy its way around governmental and societal shortcomings at prices that are out of reach for most people. Technology does not correct the intrinsic problems, and most cannot reap the benefits of the technological “solutions.” Without resorting to an overly romanticized notion of rural wisdom, Wang treats individuals like Jiang, whose future remains uncertain owing to the vagaries of e-commerce supply chains, with respect and empathy. Because of this, they largely succeed in their goal of reframing our understanding of technology as neither the cause of nor the solution to our problems but rather as a force reshaping the human experience in fundamental ways. Reviewed by Heather Bloemhard 5 The Secret Lives of Planets by Paul Murdin includes a plethora of information about our Solar System. Murdin covers planets, asteroids, moons, dwarf planets, and more, approximately one per chapter. Even exoplanets—the planets that orbit a star other than our Sun—are referenced frequently, although not in their own chapter. Using only a few images, Murdin illustrates the historical and physical concepts that surround each of these elements in prose peppered with anecdotes from his own career as an astronomer. While the book's tone is pleasant and conversational, the discussions are often technical in nature, and I worry that some readers may be frustrated by its many tangents and loose organizational structure. For example, in his discussion of the formation of Mercury, Murdin references the formation of exoplanets, the discovery of 'Oumuamua, and Earth's fossil record. The same chapter also refers to Earth and Venus to help explain orbital eccentricity and precession, but this analogy may fall short for lay readers. I was also disappointed that Murdin relied almost exclusively on the accomplishments of European men to tell the story of how our understanding of the Solar System emerged over time. He writes, for example, of Nicolaus Copernicus's revelations about the geometry of our solar system but neglects the work of Muslim astronomers who developed models of heliocentric orbits hundreds of years earlier. Murdin is far from alone in this misstep, but it is well worth striving to do better. Despite these criticisms, every reader will learn something from this ambitious book. Did you know, for example, that some scientists once believed there were oases of vegetation on Mars, or that others believed that martians might try to colonize Earth? From the exchange of planetary material by way of meteorites to the formation of asteroids, Murdin covers a wide range of astronomical topics, including the aurora of Jupiter, the mysteries of Uranus, and the potential of the moons of Jupiter and Saturn to support recognizable life. I found Murdin's personal recollections to be the most compelling feature of The Secret Lives of Planets . He tells the story of how, as a student, he observed the shadows cast by the tops of clouds of different heights on Venus using a telescope similar to the one used by Galileo and uses this anecdote as a starting point to explain what the Italian astronomer discovered about the planet. Recounting the time he observed the launch of Cassini-Huygens, a probe sent to Saturn's moon Titan, Murdin explains what scientists had hoped to learn from this mission and what they ended up discovering. He also discusses attending the 2006 International Astronomical Union conference, where a debate was held about the definition of a planet, and reveals what it was like to cast a vote on the final decision. In the end, there is much to recommend The Secret Lives of Planets as an introductory text on our solar system. Reviewed by Peter Reczek 6 Modern cancer therapies are often the result of years of targeted research and development, making it easy to forget that many of the field's early breakthroughs had as much to do with chance as they did with preparation. In The Great Secret , Jennet Conant recounts one such breakthrough, which was made in the wake of a deadly disaster. Conant's engrossing story is set in the Italian port town of Bari, which was used as an important staging area for the distribution of supplies supporting Allied troops as they pushed north through Italy during World War II. On 2 December 1943, a day that would later be referred to as “a little Pearl Harbor,” German military aircraft sank more than 20 Allied ships anchored in Bari, leading to the loss of more than 1000 Allied servicemen and Italian civilians. Lieutenant Colonel Stewart Alexander, a medical officer attached to General Eisenhower's headquarters in North Africa, was sent to coordinate medical relief efforts. In Bari, Alexander found “a nightmarish scene.” In the aftermath of the air raid, “The walking wounded staggered in [to the hospital] unaided, suffering from shock, burns, and exposure after having been in the cold water for hours before being rescued. Others had to be supported, as they cradled fractured arms in improvised slings or dragged mangled limbs…Almost all of them were covered in thick, black crude oil,” writes Conant. In addition to the acutely injured, Alexander discovered victims whose injuries had emerged days after the attack and could not be attributed to the percussive effects of the bombing. After analyzing the positions of the ailing seamen, Alexander reported that an American Liberty ship, the John Harvey , was the source of the problem, speculating that it likely contained a secret cache of nitrogen mustard (i.e., mustard gas). Both the American and British governments denied any such cache, but Conant reveals that Alexander persisted, and his controversial report—which, crucially, documented a decrease in white blood cell counts in the victims—was accepted by the Allied High Command with a classification of “Secret.” After the war, Colonel C. P. “Dusty” Rhoads, who had been Alexander's superior during the Bari investigation, reasoned that an agent that reduced white blood cells might be useful in treating some forms of leukemia. While serving as the first director of the Sloan Kettering Institute, Rhoads oversaw a clinical trial to test nitrogen mustards as potential therapeutic agents for the treatment of neoplastic disease. The results exceeded expectations. “In their first attempt to treat patients with inoperable lung cancer with nitrogen mustard, the Memorial team reported that of the thirty-five patients, 74 percent showed some clinical improvement” writes Conant. Many similar compounds, collectively known as alkylating agents, are still the foundation of the combination chemotherapy used to treat some forms of leukemia. Drawing largely from archival research, Conant relies on a loose conversational style to convey a fast-paced medical detective story that demonstrates how careful scientific observation can yield unexpected benefits and serves as a reminder of the difficult choices made by governments to balance public health and secrecy in matters of security. Reviewed by Esha Mathew 7 In quantum physics, entanglement is a property wherein two particles are inextricably linked. Put another way, entangled particles are never truly independent of each other, no matter the distance between them. It is fitting then that Entanglements is an anthology of short stories about inextricably linked people and the impact of emerging technologies on their relationships. A talented set of authors, with deft editing by Sheila Williams, explore the full spectrum of intimacy and technology to great effect. As an added visual treat, illustrations by Tatiana Plakhova punctuate each story with a blend of science, mathematics, and art that complements the subject matter. Even with the length limitations of a short story, the world-building in this compilation is frequently full and often insidiously terrifying, particularly in those stories that use the familiar as breadcrumbs to lure the reader in. The very first tale, “Invisible People” by Nancy Kress, begins with a mundane morning routine and carefully layers in a story about two parents reeling from an unsanctioned genetic experiment on their child. In “Don't Mind Me,” Suzanne Palmer uses the shuffle between high school classes as a foundation on which to build a story about how one generation uses technology to enshrine its biases and inflict them on the next. The ethical implications in these stories offer fodder enough for plenty of late-night discussions. It is also chilling how entirely possible many of the fictional futures seem. But looking forward need not always be bleak. This volume balances darker-themed stories with those in which technology and people collide in uplifting and charming ways. In Mary Robinette Kowal's “A Little Wisdom,” for example, a museum curator, aided by her robotic therapy dog–cum–medical provider, finds the courage within herself to inspire courage in others and save the day. Meanwhile, in Cadwell Turnbull's “Mediation,” a scientist reeling from a terrible loss finally accepts her personal AI's assistance to start the healing process. And in arguably the cheekiest tale in this compilation, “The Monogamy Hormone,” Annalee Newitz tells of a woman who ingests synthetic vole hormones to choose between two lovers, delivering a classic tale of relationship woes with a bioengineered twist. With such a dizzying array of technologies discussed in relation to a range of human emotion and behavior, readers may experience cognitive whiplash as they move from one story to the next. But it is definitely worth the risk. The 10 very different thought experiments presented in this volume make for a fun ride, revealing that human relationships will continue to be as complicated and affirming in the future as they are today. I would recommend the Netflix approach to this highly readable collection: Binge it in one go, preferably with a friend. Reviewed by Joseph B. Keller 8 The U.S. police system is experiencing a reckoning. Protesters across the country (and around the world) have taken to the streets, arguing that police brutality disproportionately harms minority communities, and the current value of policing is being debated by city councils, lawmakers, and members of the news media. Into this tumultuous context enters Sarah Brayne's book, Predict & Surveil: Data, Discretion, and the Future of Policing . A sociologist by training, Brayne synthesizes interview data and field notes from 5 years of observation within the Los Angeles Police Department, employing a firsthand ethnographic approach to reveal how big data are currently used in tech-forward police departments in America. She chronicles both consequential and mundane interactions between officers, civilians, and data. For example, she documents officers uploading license plate numbers, field interview notes, traffic citations, and potential gang affiliations onto a private industry data platform, as well as their active surveillance of hotspots in Los Angeles predicted to be criminogenic. This fly-on-the-wall perspective captures the human aspect of a police force grappling with automated systems and machine-learning decisions in real time, juxtaposing the experiences of individual officers with institutional directives being handed down from administrators and lawmakers. Many police departments contend that the adoption of predictive analytics can improve objectivity and transparency, reduce bias, and increase accountability. Yet Brayne's book reveals how few of these metrics actually improve with predictive policing and exposes the scant evidence that supports the idea that it reduces crime rates. On the contrary, she insists, predictive policing raises glaring civil rights concerns and reinforces harmful racial biases. We all leave digital traces throughout our daily lives, and innocent people can be caught in the dragnet and cataloged in a digital criminal justice system, where a case can be built from benign data. Police unions, Brayne notes, often vehemently oppose the tracking of their own officers. She records incidents of officers turning off their car locator signals, for example, as well as other tactics used to thwart tech-infused managerial oversight. Many officers view policing as an art form rather than a scientific system that can be optimized. To some, big data policing threatens their sense of police instincts and identity. “They worry that they will become nothing more than line workers and insist that their years of accumulated experiential knowledge is irreplaceable,” observes Brayne. Brayne's book raises timely issues relevant to mass surveillance and policing amid a growing debate about facial recognition systems, which makes their omission from this work notable. Although banned in several major American cities, these systems remain a common tool for identifying potential offenders, despite abundant evidence of dangerous inconsistencies. Predictive policing can drive societal inequalities, but Brayne suggests that reducing instances of general police contact may mitigate disparities. In addition to offering immediate recommendations for changing law enforcement in the digital age, she asserts that effective programmatic reforms are typically influenced by external social organizing and guided by communities. (The likelihood of real transformation from within the police system is small, she believes.) For judicial and policing institutions genuinely seeking reform, this book provides powerful observations and analysis that suggest how we can begin. 1. [↵][3]Paris Agreement to the United Nations Framework Convention on Climate Change, 12 December 2015, TIAS No. 16-1104. 2. [↵][4]World Commission on Environment and Development, Our Common Future (Oxford Univ. Press, 1987). [1]: #ref-1 [2]: #ref-2 [3]: #xref-ref-1-1 "View reference 1 in text" [4]: #xref-ref-2-1 "View reference 2 in text"

Global Big Data Conference


Pachyderm Inc., a San Francisco startup that provides a data science platform for easing artificial intelligence development, today disclosed that it has closed a $16 million funding round led by Microsoft Corp.'s M12 venture arm. M12 was joined by existing backers Y Combinator and Benchmark. The investment also included the participation of other backers including Decibel Ventures, a venture capital firm with close ties to Cisco Systems Inc. that mainly backs early-stage startups. Pachyderm provides an open-source data science platform that runs on Kubernetes. The platform focuses on a narrow but important use case: making it easier for software engineers to manage the data that they use in AI development projects.