The deployment of future intelligent transportation systems is contingent upon seamless and reliable operation of connected and autonomous vehicles (CAVs). One key challenge in developing CAVs is the design of an autonomous controller that can accurately execute near real-time control decisions, such as a quick acceleration when merging to a highway and frequent speed changes in a stop-and-go traffic. However, the use of conventional feedback controllers or traditional learning-based controllers, solely trained by each CAV's local data, cannot guarantee a robust controller performance over a wide range of road conditions and traffic dynamics. In this paper, a new federated learning (FL) framework enabled by large-scale wireless connectivity is proposed for designing the autonomous controller of CAVs. In this framework, the learning models used by the controllers are collaboratively trained among a group of CAVs. To capture the varying CAV participation in the FL training process and the diverse local data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, as well as the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal autonomous controller. In particular, the impacts of varying CAV participation in the FL process and diverse CAV data quality on the convergence of the proposed DFP algorithm are explicitly analyzed. A preliminary version of this work has been submitted to the proceeding of IEEE Conference on Decision and Control (CDC), 2021 [1]. This research was supported by the U.S. National Science Foundation under Grants CNS-1739642, CNS-1941348, and CNS-2008646, and by the Academy of Finland Project CARMA, by the Academy of Finland Project MISSION, by the Academy of Finland Project SMARTER, as well as by the INFOTECH Project NOOR. T. Zeng and W. Saad are with Wireless@VT, Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061 USA.

Modern radio telescopes produce unprecedented amounts of data, which are passed through many processing pipelines before the delivery of scientific results. Hyperparameters of these pipelines need to be tuned by hand to produce optimal results. Because many thousands of observations are taken during a lifetime of a telescope and because each observation will have its unique settings, the fine tuning of pipelines is a tedious task. In order to automate this process of hyperparameter selection in data calibration pipelines, we introduce the use of reinforcement learning. We use a reinforcement learning technique called twin delayed deep deterministic policy gradient (TD3) to train an autonomous agent to perform this fine tuning. For the sake of generalization, we consider the pipeline to be a black-box system where only an interpreted state of the pipeline is used by the agent. The autonomous agent trained in this manner is able to determine optimal settings for diverse observations and is therefore able to perform 'smart' calibration, minimizing the need for human intervention.

Machine learning systems are often trained using data collected from historical decisions. If past decisions were biased, then automated systems that learn from historical data will also be biased. We propose a black-box approach to identify and remove biased training data. Machine learning models trained on such debiased data (a subset of the original training data) have low individual discrimination, often 0%. These models also have greater accuracy and lower statistical disparity than models trained on the full historical data. We evaluated our methodology in experiments using 6 real-world datasets. Our approach outperformed seven previous approaches in terms of individual discrimination and accuracy.

In this paper, we study the problem of deceptive reinforcement learning to preserve the privacy of a reward function. Reinforcement learning is the problem of finding a behaviour policy based on rewards received from exploratory behaviour. A key ingredient in reinforcement learning is a reward function, which determines how much reward (negative or positive) is given and when. However, in some situations, we may want to keep a reward function private; that is, to make it difficult for an observer to determine the reward function used. We define the problem of privacy-preserving reinforcement learning, and present two models for solving it. These models are based on dissimulation -- a form of deception that `hides the truth'. We evaluate our models both computationally and via human behavioural experiments. Results show that the resulting policies are indeed deceptive, and that participants can determine the true reward function less reliably than that of an honest agent.

One important characteristic of coronavirus epidemiology is the occurrence of superspreading events. These are marked by a disproportionate number of cases originating from often-times asymptomatic individuals. Using a rich sequence dataset from the early stages of the Boston outbreak, Lemieux et al. identified superspreading events in specific settings and analyzed them phylogenetically (see the Perspective by Alizon). Using ancestral trait inference, the authors identified several importation events, further investigated the context and contribution of particular superspreading events to the establishment of local and wider SARS-CoV-2 transmission, and used viral phylogenies to describe sustained transmission. Science , this issue p. [eabe3261][1]; see also p. [574][2] ### INTRODUCTION We used genomic epidemiology to investigate the introduction and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the Boston area across the first wave of the pandemic, from March through May 2020, including high-density sampling early in this period. Our analysis provides a window into the amplification of transmission in an urban setting, including the impact of superspreading events on local, national, and international spread. ### RATIONALE Superspreading is recognized as an important driver of SARS-CoV-2 transmission, but the determinants of superspreading—why apparently similar circumstances can lead to very different outcomes—are poorly understood. The broader impact of such events, both on local transmission and on the overall trajectory of the pandemic, can also be difficult to determine. Our dataset includes hundreds of cases that resulted from superspreading events with different epidemiological features, which allowed us to investigate the nature and effect of superspreading events in the first wave of the pandemic in the Boston area and to track their broader impact. ### RESULTS Our data suggest that there were more than 120 introductions of SARS-CoV-2 into the Boston area, but that only a few of these were responsible for most local transmission: 29% of the introductions accounted for 85% of the cases. At least some of this variation results from superspreading events amplifying some lineages and not others. Analysis of two superspreading events in our dataset illustrate how some introductions can be amplified by superspreading. One occurred in a skilled nursing facility, where multiple introductions of SARS-CoV-2 were detected in a short time period. Only one of these led to rapid and extensive spread within the facility, and significant mortality in this vulnerable population, but there was little onward transmission. A second superspreading event, at an international business conference, led to sustained community transmission, including outbreaks in homeless and other higher-risk communities, and was exported domestically and internationally, ultimately resulting in hundreds of thousands of cases. The two events also differed substantially in the genetic variation they generated, possibly suggesting varying transmission dynamics in superspreading events. Our results also show how genomic data can be used to support cluster investigations in real time—in this case, ruling out connections between contemporaneous cases at Massachusetts General Hospital, where nosocomial transmission was suspected. ### CONCLUSION Our results provide powerful evidence of the importance of superspreading events in shaping the course of this pandemic and illustrate how some introductions, when amplified under unfortunate circumstances, can have an outsized effect with devastating consequences that extend far beyond the initial events themselves. Our findings further highlight the close relationships between seemingly disconnected groups and populations during a pandemic: Viruses introduced at an international business conference seeded major outbreaks among individuals experiencing homelessness; spread throughout the Boston area, including to other higher-risk communities; and were exported extensively to other domestic and international sites. They also illustrate an important reality: Although superspreading among vulnerable populations has a larger immediate impact on mortality, the cost to society is greater for superspreading events that involve younger, healthier, and more mobile populations because of the increased risk of subsequent transmission. This is relevant to ongoing efforts to control the spread of SARS-CoV-2, particularly if vaccines prove to be more effective at preventing disease than blocking transmission. ![Figure][3] Schematic outline of this genomic epidemiology study. Illustrated are the numerous introductions of SARS-CoV-2 into the Boston area; the minimal spread of most introductions; and the local, national, and international impact of the amplification of one introduction by a large superspreading event. Analysis of 772 complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from early in the Boston-area epidemic revealed numerous introductions of the virus, a small number of which led to most cases. The data revealed two superspreading events. One, in a skilled nursing facility, led to rapid transmission and significant mortality in this vulnerable population but little broader spread, whereas other introductions into the facility had little effect. The second, at an international business conference, produced sustained community transmission and was exported, resulting in extensive regional, national, and international spread. The two events also differed substantially in the genetic variation they generated, suggesting varying transmission dynamics in superspreading events. Our results show how genomic epidemiology can help to understand the link between individual clusters and wider community spread. [1]: /lookup/doi/10.1126/science.abe3261 [2]: /lookup/doi/10.1126/science.abg0100 [3]: pending:yes

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. 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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. 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The expansion of artificial intelligence (AI) and autonomous systems has shown the potential to generate enormous social good while also raising serious ethical and safety concerns. AI technology is increasingly adopted in transportation. A survey of various in-vehicle technologies found that approximately 64% of the respondents used a smartphone application to assist with their travel. The top-used applications were navigation and real-time traffic information systems. Among those who used smartphones during their commutes, the top-used applications were navigation and entertainment. There is a pressing need to address relevant social concerns to allow for the development of systems of intelligent agents that are informed and cognizant of ethical standards. Doing so will facilitate the responsible integration of these systems in society. To this end, we have applied Multi-Criteria Decision Analysis (MCDA) to develop a formal Multi-Attribute Impact Assessment (MAIA) questionnaire for examining the social and ethical issues associated with the uptake of AI. We have focused on the domain of autonomous vehicles (AVs) because of their imminent expansion. However, AVs could serve as a stand-in for any domain where intelligent, autonomous agents interact with humans, either on an individual level (e.g., pedestrians, passengers) or a societal level.

Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these. However, conversational question-answering remains challenging due to the lack of realistic, domain-specific training data. Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes. We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity. Specifically, we design ChainCQG as a two-stage architecture that learns question-answer representations across multiple dialogue turns using a flow propagation training strategy.ChainCQG significantly outperforms both answer-aware and answer-unaware SOTA baselines (e.g., up to 48% BLEU-1 improvement). Additionally, our model is able to generate different types of questions, with improved fluidity and coreference alignment.

Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-time. Current interactive reinforcement learning research has been limited to interactions that offer relevant advice to the current state only. Additionally, the information provided by each interaction is not retained and instead discarded by the agent after a single-use. In this work, we propose a persistent rule-based interactive reinforcement learning approach, i.e., a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Our experimental results show persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer. Moreover, rule-based advice shows similar performance impact as state-based advice, but with a substantially reduced interaction count.

I am going to begin with some practical things that I have taken away from NeurIPS this year. Careful setup and proper tuning of your models can make a big difference in performance. An amazing example of this are Steffen Rendle's currently SOTA results for recommender ratings predictions on the Movielens 10M dataset, where a well tuned baseline is able to beat years of research on the topic. Of course, setup and proper tuning is hard, so knowing current tricks for particular architectures, as well as how to combine them all together, is super useful. There are several bag of tricks summary papers that I know of for CNNs 1 2, and I would love to hear about more. I found two more promising methods at NeurIPS this year that I will definitely try out. Curriulum learning is the idea of breaking a learning task down into units of progressively increasing difficulty, which is pretty much how humans learn. Curriculum by Smoothing 3 by Sinha et al proposes to apply this idea to CNN training. During early training, filters learned by the CNN will include high frequency data in the produced feature maps. These are details at very small scales. To illustrate the type of information we are talking about, let's take a brief look at JPG compression: Human vision has a drop-off at higher frequencies, and de-emphasizing (or even removing completely) higher frequency data from an image will give an image that appears very different to a computer, but looks very close to the original to a human. The quantization stage uses this fact to remove high frequency information, which results in a smaller representation of the image.