The application of Artificial Intelligence (AI) in Talent Management is obscured by marketing hype and misinformation. Much of what is called AI in the marketplace is something much less than what researchers and practitioners in the field consider "intelligent." Still, they are misleading to HR technology buyers who believe that AI is a panacea for many of their most challenging talent management problems. However, there are many promising applications of AI in Talent Management that are worth investigating further. For example, finding the best match from thousands of applicants for a handful of job openings or providing insights about an applicant's personality by analyzing their facial expressions during video interviews shows great promise.

Typically, fair machine learning research focuses on a single decisionmaker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decisionmakers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does the strategic behavior of decision subjects in partial compliance settings affect the allocation outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance (k% of employers) can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; and (4) partial compliance to local parity measures can induce extreme segregation.

You can watch the webstreaming of the debates here. AIDA MEPs will exchange views with Thomas Jarzombek, Commissioner for Digital Industry and Start-ups, German Federal Ministry for Economic Affairs and Energy and Daniela Kolbe, Chair of the Bundestag Study Commission on "Artificial Intelligence – Social Responsibility and Economic, Social and Ecological Potential". The topics under discussion will include the position of the German Presidency on the future EU regulatory framework on AI, as well as a review of the recent 800-page report of the Study Commission on AI, spanning an examination of a wide variety of societal AI applications and their policy implications.

It takes a typical U.S. employer six weeks to fill a role, which costs roughly $4,000. So the desire to reduce hiring costs and speed up the recruitment process has understandably piqued people's curiosity about AI. In turn, AI vendors promise to help find the right person for the job and screen out unfit candidates more quickly and affordably. For instance, AI-driven candidate assessments analyze people's facial movements, word choice, and tone of voice in an attempt to determine their employability. But what type of ethical, legal, and privacy implications does all this introduce into the hiring process?

Medical AI products require certification before deployment in most jurisdictions. To date, no clear pathways for regulating medical AI exist. I present a methodological guide to the development of a regulatory package which will form part of a certification process. This approach is predicated on the translation between a statistical risk perspective, typical of medical device regulators, and a deep understanding of machine learning methodologies. This work of translation envisages the statistician as the key negotiator between medical device regulators and machine learning experts, allowing them to communicate more clearly, and thus lead to the development of standardised pathways for medical AI regulation.

Nowhere could the application of machine learning prove more important -- nor more risky -- than in law enforcement and national security. In this article, I'll review this area and then cover six perplexing and pressing ethical quandaries that arise. Predictive policing introduces a scientific element to law enforcement decisions, such as whether to investigate or detain, how long to sentence, and whether to parole. In making such decisions, judges and officers take into consideration the probability a suspect or defendant will be convicted for a crime in the future -- which is commonly the dependent variable for a predictive policing model. These independent variables may include prior convictions, income level, employment status, family background, neighborhood, education level, and the behavior of family and friends.

Brianna Hill is a recent law school graduate. She knew it would be hard to contest a false report of cheating by proctoring software, the technology increasingly being used to monitor individuals taking tests remotely during the Covid-19 era. So she continued to complete her bar exam despite going into labor during the test this October. She sat still during contractions knowing she might be disqualified if the artificial intelligence couldn't watch her every move. Test-takers can't leave the view of the camera for any reason.

MEPs are currently working on legislation to be adopted around artificial intelligence (AI). Innovation, access to data, protection of citizens, ethics, research, legal, social and economic issues, the impacts of the future regulation are numerous and central for citizens, administrations and businesses alike. So what are the outlines of the EU legislation envisaged by MEPs on artificial intelligence? This is the question Parliament has answered. Intelligence plays a major role in the digital transformation of our societies.

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