That the regulation of Artificial intelligence is a hot topic is hardly surprising. AI is being adopted at speed, news reports frequently appear about high-profile AI decision-making, and the sheer volume of guidance and regulatory proposals for interested parties to digest can seem challenging. What can we expect in terms of future regulation? And what might compliance with "ethical" AI entail? High-level ethical AI principles were made by the OECD, EU and G20 in 2019.

Legal experts and the Trades Union Congress (TUC) have warned that gaps in employment law will lead to staff "hired and fired by algorithm". A report, commissioned by the TUC and carried out by leading employment rights lawyers Robin Allen QC and Dee Masters from the AI Law Consultancy, claims there are "huge gaps" in British law. "The TUC is right to call for urgent legislative changes to ensure that workers and companies can both enjoy the benefits of AI," the lawyers say. "[Employment law gaps] must be plugged quickly to stop workers from being discriminated against and mistreated." The report warns that employment law is failing to keep pace with the rapid adoption of AI in the workplace and workers will become powerless to challenge "inhuman" forms of AI performance management.

We propose a novel method, termed SuMo-net, that uses partially monotonic neural networks to learn a time-to-event distribution from a sample of covariates and right-censored times. SuMo-net models the survival function and the density jointly, and optimizes the likelihood for right-censored data instead of the often used partial likelihood. The method does not make assumptions about the true survival distribution and avoids computationally expensive integration of the hazard function. We evaluate the performance of the method on a range of datasets and find competitive performance across different metrics and improved computational time of making new predictions.

For artificial intelligence to be beneficial to humans the behaviour of AI agents needs to be aligned with what humans want. In this paper we discuss some behavioural issues for language agents, arising from accidental misspecification by the system designer. We highlight some ways that misspecification can occur and discuss some behavioural issues that could arise from misspecification, including deceptive or manipulative language, and review some approaches for avoiding these issues.

When members of the scientific community gathered at the AAAS Annual Meeting in February, they did so in front of laptops and tablets from their home offices and dining tables. They presented over Zoom, submitted questions via chat, and caught up with colleagues over social media. The 2021 AAAS Annual Meeting was unlike any other in the meeting's 187-year history, but the fully virtual setting did not dampen enthusiasm for sharing science in keeping with the “Understanding Diverse Ecosystems” meeting theme. Dozens of scientific sessions shared new research in areas ranging from microbiomes to space travel. More than 40 workshops offered attendees the opportunity to discuss strategies for working in the ecosystems of academia and science policy. Plenary and topical lecturers covered timely topics, including Ruha Benjamin on how technology can deepen inequities, Anthony Fauci on the next steps for COVID-19 response, Mary Gray on research ethics, and Yalidy Matos on immigration policies. “The quality of the speakers was absolutely undeniable, and the diversity of the speakers—across gender, race, region—was just extraordinary,” said Sudip Parikh, chief executive officer of AAAS and executive publisher of the Science family of journals. “That is what our vision of the world looks like in a place where science is done with creativity and innovation and excellence.” Selecting a diverse meeting program is grounded in AAAS's values, but it is not without concerted effort, according to Claire Fraser. Fraser, who served as AAAS president through February and now serves as chair of the AAAS Board of Directors, selected the meeting theme and led the AAAS Meeting Scientific Program Committee, which oversees selection of the meeting's speakers. “The diversity doesn't happen by accident. I think it reflects the very strong commitment on the part of the Scientific Program Committee to make sure that not only is the science presented timely and excellent, but the diversity of speakers and participants is as broad as it possibly can be,” said Fraser, director of the Institute for Genome Sciences at the University of Maryland School of Medicine. Diversity isn't an afterthought—it's a deliberate part of the very first review of potential scientific sessions, according to Andrew Black, chief of staff and chief public affairs officer. When hundreds of volunteer reviewers evaluate the quality of the submissions before sending the best for consideration by the Scientific Program Committee, they are also looking for diversity across many dimensions, Black said. Among those dimensions are diversity of scientific discipline—befitting AAAS's multidisciplinary membership—but also gender, race and ethnicity, geographic diversity, career stage, and type of institution, including all types and sizes of universities, industry, and government. “Who do you see, who do you hear, and what kind of voices are in dialogue with each other? That's part of our assessment process,” said Agustín Fuentes, professor of anthropology at Princeton University and a member of the Scientific Program Committee. The review process offers opportunities for applicants to diversify their sessions. Applicants are often encouraged to look beyond their own networks to add a range of voices to their presentation to best communicate their ideas to the broader scientific community, Fuentes said. “We need to think very carefully in this moment in time about how do we not only redress past biases and discriminatory practices but how do we create a space, a voice, and a suite of presenters that is very inviting to a diverse audience,” Fuentes said. Added Fraser, “What you end up with is even better because you have such broad perspectives represented.” The committee also emphasized the importance of ensuring that a diverse group of decision-makers have a seat at the table. Members of the Scientific Program Committee, who are nominated from across AAAS and its 26 disciplinary sections and approved by the AAAS Board, represent a broad range of groups and perspectives, Fraser said. “What I firmly believe is that you can't come up with a diverse program like we had this year and like we've had in previous years without that diversity in the program committee,” Fraser said. Commitment to diversity across many axes is part of AAAS Annual Meeting history. In the 1950s, AAAS refused to hold meetings in the segregated South. In 1976, under one of AAAS's first female presidents, Margaret Mead, the Annual Meeting was fully accessible to people with disabilities for the first time. According to the AAAS Project on Science, Technology, and Disability, wheelchair ramps were added to the conference hall, programs were made accessible for hearing-impaired and visually impaired attendees, and Mead's presidential address was simultaneously interpreted in sign language. In 1978, AAAS's Board of Directors voted to move the following year's Annual Meeting out of Chicago because Illinois had not ratified the Equal Rights Amendment. In 1993, AAAS moved its 1999 meeting from Denver after Colorado voters adopted a constitutional amendment to deny residents protection from discrimination based on sexual orientation. Leaders at AAAS note that there is always more work to be done in the present and future—both at the Annual Meeting and year-round. AAAS continues to focus on its own systemic transformation in areas of diversity, equity, and inclusion and on the breadth of initiatives in its new Inclusive STEM Ecosystems for Equity & Diversity program, all to ensure that the scientific enterprise reflects the full range of talent. That goal resonated with many 2021 AAAS Annual Meeting speakers, too. A more diverse group of scientists creating artificial intelligence systems can improve those systems, said Ayanna Howard, a roboticist who leads The Ohio State University's College of Engineering, during her topical lecture, “Demystifying AI Through the Lens of Fairness and Bias.” Said Howard, “We as people are diverse and we're different and it makes us unique and beautiful, and our AI systems should be designed in such a way.” Nalini Nadkarni, a University of Utah biologist who delivered a topical lecture on “Forests, the Earth, and Ourselves: Understanding Dynamic Systems Through an Interdisciplinary Lens,” shared how she reaches young girls to let them know that science—and her own scientific specialty—is a space where they can thrive. She and her students created and distributed “Treetop Barbie,” dressing a doll in fieldwork clothes and creating a doll-sized booklet about canopy plants. The Annual Meeting offers a chance to show that science is best when it is for everyone, regardless of background or perspective, whether they're a kid or just a kid at heart. Said Parikh, “The AAAS Annual Meeting is where the pages of Science literally come alive. It's a place where scientists, no matter what discipline or industry they decided to pursue, can pull back and just fall in love with the idea of science again—like we did when we were kids.”

Air pollution from fine particulate matter (PM2.5) is increasingly driving the global burden of disease ([ 1 ][1]), and diesel-powered vehicles are substantial contributors. Recognizing the public health impacts of diesel PM2.5 (DPM) ([ 2 ][2]), many countries have reduced emissions of DPM from both on- and off-road mobile sources over the past three decades. The previous US federal administration, however, changed course by eliminating or weakening policies and standards that govern these emissions. In contrast, the State of California has continued to reduce mobile-source DPM emissions using the state's long-standing authority under the Clean Air Act (CAA) to regulate air pollution more stringently than the federal government. Our analysis of mobile-source DPM emissions suggests that many California sector-based policies have been highly effective relative to the rest of the US. To improve health in communities disproportionately affected by these emissions, we point to opportunities to further reduce DPM emissions in California, in the US more broadly, and in parts of the world where countries have less aggressive vehicle emissions policies than the US ([ 3 ][3]). The US has targeted emissions of nitrogen oxides (NO x ) and DPM from diesel trucks and buses, railway locomotives, marine vessels, and off-road engines used in construction and agriculture through successively tighter emissions standards phased in since 1994 (table S1). These standards require low- and ultralow-sulfur diesel fuels (LSDF and ULSDF), establish emissions limits, and institute systems for portable emissions measurement and onboard diagnostics (table S1). The US Environmental Protection Agency (EPA) estimated that full implementation of Obama-era US emissions standards by 2030 would prevent some 12,000 premature deaths annually ([ 4 ][4]). Despite this, EPA leadership disbanded the PM review panel ahead of the scheduled 2020 update of federal PM standards; it also rolled back, or attempted to roll back, 85 federal air pollution policies ([ 5 ][5]) and moved to restrict the ability of states to set more stringent emissions standards ([ 6 ][6]). California, whose economy would rank fifth largest in the world if it were a sovereign nation, hosts the country's two largest ports and moves 60% of its container cargo (see supplementary materials). With the associated truck and rail traffic, California stands out as the largest emitter of DPM in the country. At the same time, California has also led the nation with the largest overall reduction in metric tons of DPM emissions from mobile sources. Over the past three decades, California's policies have systematically targeted high-emitting sectors, reducing mobile-source DPM emissions by, for example, substituting electric for diesel power where feasible, tightening emissions limits for new and existing diesel engines, and requiring ULSDF, which emits substantially less PM2.5 than higher-sulfur fuels upon combustion and can be combined with particle filters to further reduce emissions. To understand the impact of California's portfolio of policies, we used DPM emissions data from the EPA National Emissions Inventory (NEI), which assembles a comprehensive estimate of air pollution emissions using data reported by states, combined with modeled and measured inputs. We compared mobile-source DPM emissions in California versus the rest of the US for the period 1990 to 2014, the earliest and most recent year for which consistent NEI data are available ([ 7 ][7]). During that time, California reduced overall mobile-source DPM emissions by 78% while the rest of the US saw only a 51% reduction. These reductions came despite a concurrent steady rise in diesel fuel consumption: 20% in California and 28% in the rest of the US (data S1). Emissions reductions from heavy-duty diesel vehicles (HDDVs)—commercial trucks and buses—caused most of this decline, accounting for 67% of DPM emissions reductions in California and 57% in the rest of the US. Although the federal phase-in of ULSDF, off-road emissions standards, and the Heavy-Duty Engine and Vehicle Rule has reduced HDDV emissions across the US, California's reductions from HDDVs have been steeper and contribute even more to the overall reductions than would be predicted from the sector's size. Analyses of DPM emissions over time and the relative contributions made by each sector point to the effectiveness of California's policies that require diesel engine retrofits (adding emissions controls to existing HDDVs) and early replacement of older engines with newer, cleaner engines. Our analysis identifies three distinct phases in mobile-source DPM emissions between 1990 and 2014. Emissions fell overall from 1990 to 2001 in California and from 1990 to 2005 in the rest of the country. Reduced emissions from HDDVs contributed the largest share of the overall drop (see the figure and data S1). These changes are attributable to the introduction of LSDF nationwide, and to California's new requirements for vehicle inspections (table S2). Then, from 2001 to 2005 in California and from 2005 to 2008 in the rest of the country, emissions rose during an economic boom, driven primarily by increasing emissions from HDDVs and marine sources. Finally, overall DPM emissions once again fell, beginning in California in 2005 and in the rest of the US in 2008. The recession played a role in the early part of this drop ([ 8 ][8]), but emissions reductions continued through 2014 despite the economic recovery and the corresponding upturn in diesel use. During this final phase, California's 67% drop in DPM emissions outpaced the 40% reduction seen in the rest of the country (see the figure and data S1). Our analysis of individual sectors and each state's HDDV emissions suggests that California policies specifically targeting emissions from HDDVs and marine sources drove this decline. The later phases of California's emissions reductions correspond to the implementation of two overarching plans by the California Air Resources Board (CARB): the Diesel Risk Reduction Plan and the Emission Reduction Plan for Ports and Goods Movement (Goods Movement Plan), both of which encompassed multiple policies governing emissions from trucks and buses, ports, and off-road engines (table S2). Key policies targeting on-road HDDVs took effect in 2006 and 2007, further lowering the sulfur content of diesel fuel to 15 ppm (table S2) and tightening DPM emissions standards by 90% for new HDDVs (table S2). Beginning in 2010, with a rolling compliance period starting in 2015, all on-road HDDVs that operate in California were required to either retrofit existing engines with particle filters or replace engines older than the 2007 model year (table S2). By comparison, federal policies do not require retrofit or replacement of old diesel engines to meet emission standards, and HDDV engines typically operate for almost two decades, or about a million miles, before retirement. Our state-level analysis shows that by 2014 California HDDVs were emitting 139 metric tons of DPM for every billion vehicle-miles traveled (VMT), far less than the next-closest state (Oklahoma, 250 metric tons DPM per billion VMT) and the average in the rest of the country (345 metric tons DPM per billion VMT) (data S1). Although HDDVs remain California's largest source of DPM emissions, regulatory actions by CARB (over and above federal standards) have reduced HDDV emissions by 85% since 1990. If California's HDDV sector had followed the trajectory of other US states and DC, HDDV emissions in the state would have dropped only 58% (95% confidence interval, 52 to 64%) in that period (data S1). Also notable is the impact of two key CARB policies targeting marine sources. The 2007 At-Berth rule requires that oceangoing vessels switch to electric shore power while in port or use alternative control technologies to reduce emissions by an equivalent amount (table S2). The Cleaner Ocean Vessel fuel policy, finalized in 2008, requires that ships within 24 nautical miles of California's shoreline replace heavy fuel oil in their main engines with lower-sulfur fuels (table S2). Between 2008 and 2014, marine DPM emissions in the state dropped 51% overall (see the figure and data S1), and by 2018 emissions measured at the Port of Los Angeles had declined by 37% (fig. S3, A and B, and data S1). ![Figure][9] California versus the rest of the United States: Mobile-source DPM emissions declined differently Mobile-source diesel PM2.5 (DPM) emissions by sector in California versus the rest of the US from 1990 to 2014. HDDV, heavy-duty diesel vehicle; LDDV, light-duty diesel vehicle. All percentage changes reflect values relative to 1990 values. GRAPHIC: N. CARY/ SCIENCE By contrast, California has struggled to target diesel emissions from agriculture (table S2). The sector is responsible for up to 18% of the state's total DPM emissions from mobile sources, but it accounted for less than 1% of the total emissions reductions in California between 1990 and 2014. Although these figures do not reflect gains from voluntary tractor engine replacements that are reported differently, opportunities remain to reduce off-road farm emissions in the nation's leading agricultural state. Voluntary programs have further reduced DPM emissions beyond California's regulatory requirements. Incentives to bring engines and equipment to a standard cleaner than required by law are estimated to have reduced DPM emissions by more than 6000 metric tons since 2001 (table S2). A program established in 2006 has provided $1 billion in grants to update trucks, locomotives, and ships at berth, eliminating an estimated 2200 metric tons of DPM emissions (table S2). Like other policies targeting emissions along goods-movement corridors, this program particularly benefits neighboring communities, which tend to be lower-income communities of color (table S4). Taken together, CARB's policies reduced emissions to the extent that by 2014 California was emitting less than half the DPM that would be expected had the state followed the same trajectory as the rest of the US (fig. S2 and data S1). Correspondingly, we estimate that more than twice as many Californians would have died from DPM-attributable cardiopulmonary disease in 2014 alone if the state had not so markedly reduced emissions (data S1). The impact of targeted emissions regulation is also evident nationally, but it has come later and never as meaningfully as in California. Farming and construction emissions fell following the 2007 EPA Heavy Duty Engine and Vehicle Rule and the 2008–2015 phase-in of Tier 4 standards targeting off-road emissions from farm and construction equipment (table S1). Federal requirements for LSDF in the 1990s and ULSDF beginning in 2006 reduced HDDV emissions from both nonroad and on-road sources (table S1). In the marine sector, US coastal areas caught up to California's fuel standards in 2012 when ULSDF was required for smaller marine engines (table S1) and in 2015 for the largest vessels when requirements for lower-sulfur marine diesel came into effect in the North American Emissions Control Area established by the International Maritime Organization (table S1). By contrast, California has taken not only earlier action on marine emissions but also aggressive steps to target emissions from the many engines that pollute the air near ports, including marine auxiliary engines, short-haul trucks, cargo-handling cranes, and yard trucks (table S2). Individual states that have reduced HDDV emissions more than the national average are more likely to have adopted California's standards, as permitted under the CAA (table S5 and data S1), and the rest of the US could do the same. Coordination across states and between state and federal agencies means that methodological differences in data collection are unlikely to account for the observed differences in DPM emissions between California and the rest of the US (see supplementary materials). But how do we know that emission inventories are accurate and, furthermore, that CARB policies are responsible for the observed reductions? Field studies measuring changes in concentrations of DPM serve to ground-truth emissions inventories and substantiate the link between policy interventions and observed outcomes (table S4). For example, following the suite of interventions under the 2006 Goods Movement Plan, California communities in close proximity to goods-movement corridors saw significantly greater air quality improvements relative to non–goods-movement corridors and control areas monitored during the same time period (table S4). These findings show specific, local impacts of regulations targeting high-emitting sectors, distinguishing those changes from secular trends in air pollution and demonstrating their potential to advance environmental justice. The 2007 CARB regulation requiring retrofit or replacement of older HDDV engines for short-haul “drayage trucks” that operate at ports and railyards corresponded to a 70% reduction in black carbon emissions (a DPM proxy) and a 75% reduction in PM mass specific to drayage trucks measured in and around the ports of Oakland and Los Angeles between 2009 and 2011 (table S4). These changes mirror the emissions reductions measured in laboratory testing of the low-sulfur fuels and retrofit technologies used to meet the drayage truck standards (table S3). Likewise, the 2009 CARB requirement for low-sulfur fuels in oceangoing vessel engines operating within 24 nautical miles of the California coastline was associated with a measured 64% drop in San Francisco Bay Area concentrations of vanadium, a marker for combustion of heavy fuel oil (table S4). Sampling conducted by aircraft flying in the exhaust plume of a container ship approaching the coast showed that the fuel switch, combined with a required speed reduction, dropped DPM emissions by 90% (table S4). That these changes all occurred in the setting of continued growth in California's population, gross state product, and diesel consumption (figs. S4 and S5) further supports the assertion that the observed reductions track to the policies targeting DPM emissions. Observed emissions reductions are further corroborated by epidemiological data that link specific CARB policies to regional reductions in children's exposure to particle pollution and show corresponding improvements in both lung function and development in children with and without asthma ([ 9 ][10]). Finally, comparing HDDV sector emissions in California to the rest of the country likely underestimates the actual impact of CARB policies, which apply not only to the nearly half-million trucks and buses registered in California but also to the same number of out-of-state HDDVs estimated to drive California's highways each year ([ 10 ][11]). This requirement reduces emissions outside of California as well, although those reductions are attributed to federal policy. In California, cleaner air has not come at the expense of the state's economy, which in recent years has grown at double the average national rate ([ 11 ][12]). CARB estimates that every dollar the state has spent controlling air pollution has generated $38 in benefits attributable to reduced air pollution–related illness, premature death, and lost productivity. California's overall economic gain from health benefits linked to air pollution reduction, including CARB rules and programs, is estimated to have exceeded $250 billion between 1973 and 2014 ([ 12 ][13]). The link between PM2.5 exposure and increased risk of hospitalization and death from COVID-19 ([ 13 ][14]) further underscores the public health importance of cleaner air, particularly for communities of color that are disproportionately affected by both. California could benefit from additional measures to reduce emissions from off-road sectors, such as construction and agriculture, which CARB has not tackled as aggressively ([ 14 ][15]). Indeed, the nation as a whole could reduce mobile-source DPM emissions by requiring ships at berth to use shore power, and by requiring replacement or retrofit of existing on-road and off-road HDDVs in advance of fleet turnover. Given the long service life of older, dirty diesel engines, the current federal policy of mandating engine upgrades only with vehicle turnover is simply too slow. As the US initiates new federal rule-making on the proposed Cleaner Trucks Initiative to reduce NO x emissions from HDDVs, industry and environmental groups are calling on EPA to address NO x and DPM emissions in tandem and to create consistent “50-state” standards ([ 15 ][16]). In doing so, the EPA should align with CARB rules. EPA should also remove federal preemption of state emissions limits for off-road engines used in construction and agriculture. Even absent more aggressive federal policy, states' authority to set and implement their own stricter emissions standards must be protected. [science.sciencemag.org/content/371/6536/1314/suppl/DC1][17] 1. [↵][18]GBD 2017 Risk Factor Collaborators, Lancet 392, 1923 (2018). [OpenUrl][19][CrossRef][20][PubMed][21] 2. [↵][22]California Air Resources Board, “Overview: Diesel Exhaust & Health”; [ww2.arb.ca.gov/resources/overview-diesel-exhaust-and-health][23]. 3. [↵][24]European Union Directorate-General for Internal Policies, Comparative Study on the Differences Between the EU and US Legislation on Emissions in the Automotive Sector (2016); [www.europarl.europa.eu/RegData/etudes/STUD/2016/587331/IPOL\_STU(2016)587331\_EN.pdf][25]. 4. [↵][26]DieselNet, “Emission Standards, United States”; [www.dieselnet.com/standards/us/index.php][27]. 5. [↵][28]1. J. M. Samet, 2. T. A. Burke , Annu. Rev. Public Health 41, 347 (2020). [OpenUrl][29] 6. [↵][30]1. C. Davenport , “Trump to Revoke California's Authority to Set Stricter Auto Emissions Rules.” New York Times, 17 September 2019; [www.nytimes.com/2019/09/17/climate/trump-california-emissions-waiver.html][31]. 7. [↵][32]US Environmental Protection Agency, “National Emissions Inventory (NEI)”; [www.epa.gov/air-emissions-inventories/national-emissions-inventory-nei][33]. 8. [↵][34]1. D. Q. Tong et al ., Atmos. Environ. 107, 70 (2015). [OpenUrl][35] 9. [↵][36]1. F. Gilliland et al ., “The Effects of Policy-Driven Air Quality Improvements on Children's Respiratory Health” (2017); [www.healtheffects.org/system/files/GillilandRR190.pdf][37]. 10. [↵][38]California Air Resources Board, “Staff Report: Initial Statement of Reasons for Proposed Rulemaking: Proposed Regulation for In-Use On-Road Diesel Vehicles” (2008); . 11. [↵][39]Next10, 2017 California Green Innovation Index (2017); [www.next10.org/publications/2017-gii][40]. 12. [↵][41]California Air Resources Board, Fifty Year Air Quality Trends and Health Benefits; [ww3.arb.ca.gov/board/books/2018/020818/18-1-2pres.pdf][42]. 13. [↵][43]1. X. Wu, 2. R. C. Nethery, 3. M. B. Sabath, 4. D. Braun, 5. F. Dominici , Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Sci. Adv. 6, eabd4049 (2020). 10.1126/sciadv.abd4049pmid:33148655 [OpenUrl][44][FREE Full Text][45] 14. [↵][46]California's construction emissions declined markedly from 2008 to 2011. Although industry likely lowered emissions in anticipation of deadlines in the 2008 In-Use Off-Road Diesel-Fueled Fleet Regulation (table S2), the majority of the decline is likely attributable to CARB's 2011 construction inventory revision prompted by the regulated industry. In that year, the regulation was also amended to delay implementation by 4 years and to lower required emission reductions. 15. [↵][47]US Environmental Protection Agency, “Control of Air Pollution From New Motor Vehicles: Heavy-Duty Engine Standards” [proposed rule]; [www.federalregister.gov/documents/2020/01/21/2020-00542/control-of-air-pollution-from-new-motor-vehicles-heavy-duty-engine-standards#citation-4-p3307][48]. Acknowledgments: We thank K. Peterson (University of California, Berkeley) for data visualization; K. Karparos, C. Parmer, and B. Holmes-Gen (CARB) for manuscript review; M. Houyoux, J. Godfrey, and M. Aldrich (EPA) for assistance with NEI data; and J. Austin, R. Boyd, T. Brasil, J. Cao, M. Diaz, R. Furey, J. Herner, S. Huber, M. Komlenic, R. Krieger, T. Kuwayama, N. Lowery, N. Motallebi, S. Pournazeri, S. Yoon, S. Zelinka, and L. Zhou (CARB) for assistance with CARB regulations and data. This research was supported in part by California Breast Cancer Research Program grant 23QB-1881. J.B. serves as the Physician Member of CARB. A.A. is a former employee of CARB. 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Artificial Intelligence is transforming the world we live in. Its impact on society, right from employee satisfaction to enabling new technologies, has been unpredictably valuable. While everyone talks about the commercial application of artificial intelligence, what is often overlooked is how artificial intelligence is helping fight unjust. Whether it is the on-going COVID-19 pandemic or atrocious acts like human trafficking, artificial intelligence systems are helping the authorities track the responsible faster. Let's take a look at this disruptive technology that is adding value to make the world a better place.

In a legal system, judgment consistency is regarded as one of the most important manifestations of fairness. However, due to the complexity of factual elements that impact sentencing in real-world scenarios, few works have been done on quantitatively measuring judgment consistency towards real-world data. In this paper, we propose an evaluation metric for judgment inconsistency, Legal Inconsistency Coefficient (LInCo), which aims to evaluate inconsistency between data groups divided by specific features (e.g., gender, region, race). We propose to simulate judges from different groups with legal judgment prediction (LJP) models and measure the judicial inconsistency with the disagreement of the judgment results given by LJP models trained on different groups. Experimental results on the synthetic data verify the effectiveness of LInCo. We further employ LInCo to explore the inconsistency in real cases and come to the following observations: (1) Both regional and gender inconsistency exist in the legal system, but gender inconsistency is much less than regional inconsistency; (2) The level of regional inconsistency varies little across different time periods; (3) In general, judicial inconsistency is negatively correlated with the severity of the criminal charges. Besides, we use LInCo to evaluate the performance of several de-bias methods, such as adversarial learning, and find that these mechanisms can effectively help LJP models to avoid suffering from data bias.

Due to the wide use of highly-valuable and large-scale deep neural networks (DNNs), it becomes crucial to protect the intellectual property of DNNs so that the ownership of disputed or stolen DNNs can be verified. Most existing solutions embed backdoors in DNN model training such that DNN ownership can be verified by triggering distinguishable model behaviors with a set of secret inputs. However, such solutions are vulnerable to model fine-tuning and pruning. They also suffer from fraudulent ownership claim as attackers can discover adversarial samples and use them as secret inputs to trigger distinguishable behaviors from stolen models. To address these problems, we propose a novel DNN watermarking solution, named HufuNet, for protecting the ownership of DNN models. We evaluate HufuNet rigorously on four benchmark datasets with five popular DNN models, including convolutional neural network (CNN) and recurrent neural network (RNN). The experiments demonstrate HufuNet is highly robust against model fine-tuning/pruning, kernels cutoff/supplement, functionality-equivalent attack, and fraudulent ownership claims, thus highly promising to protect large-scale DNN models in the real-world.

Researchers from University of Minnesota, New York University, University of Pennsylvania, BI Norwegian Business School, University of Michigan, National Bureau of Economic Research, and University of North Carolina published a new paper in the Journal of Marketing that examines how advances in machine learning (ML) and blockchain can address inherent frictions in omnichannel marketing and raises many questions for practice and research. The study, forthcoming in the Journal of Marketing, is titled "Informational Challenges in Omnichannel Marketing Remedies and Future Research" and is authored by Koen Pauwels, Haitao (Tony) Cui, Catherine Tucker, Raghu Iyengar, S. Sriram, Anindya Ghose, Sriraman Venkataraman, and Hanna Halaburda. In this new study in the Journal of Marketing, researchers define omnichannel marketing as the "synergistic management of all customer touch points and channels both internal and external to the firm that ensures that the customer experience across channels and firm-side marketing activity, including marketing-mix and marketing communication (owned, paid, and earned), is optimized." Often viewed as the panacea for one-to-one marketing, omnichannel experiences data, marketing attribution, and consumer privacy frictions. The research team demonstrates that advances in machine learning (ML) and blockchain can address these frictions.