To promote consistency, accountability, and transparency, organizations should document findings, such as the origins and gaps in the training data used in AI systems. Documentation should include populations who are under or overrepresented for whom the AI system may have different success rates. This will put on notice those testing for gaps and harms at later stages as well as downstream users of the AI systems. This practice is akin to adhering to nutritional labels and ingredient lists, such as AI model cards, that documents what and who is part of datasets. We may not like them, but we have routine dentist visits.

Over the last few years, responsible AI has gone from a niche concept to a constant headline. Responsible, trustworthy AI is the subject of several documentaries, books, and conferences. The more we make responsible AI an expectation and a known commodity, the more likely we are to make it our reality. This enables us to flourish with more accessible AI. This is our shared goal through the Responsible AI Badge certification programme for senior executives.

Artificial intelligence tools in hiring have so far remained unregulated by U.S. civil rights agencies, despite growing use and potential discrimination risks. One EEOC official wants that to change. "What is unfair is if there are enforcement actions or litigation, both from the government and from the private sector, against those who are using the technologies, and the federal agency responsible for administering the laws has said nothing," Keith Sonderling, a Republican commissioner on the U.S. Equal Employment Opportunity Commission, told Bloomberg Law in an exclusive interview. The use of artificial intelligence for recruitment, resume screening, automated video interviews, and other employment tasks has for years been on the radar of federal regulators and lawmakers, as workers began filing allegations of AI-related discrimination to the EEOC. Attorneys have warned that bias litigation could soon be on the horizon.

AI is designed to make us more efficient and productive, but study after study shows that it can also lead us to biased decision-making. This course includes invaluable lectures, articles, case studies, and ready-to-use documents to help you (and your company) get your arms around these pervasive and rapidly evolving issues. Throughout the course, we'll explain artificial intelligence in simple terms as we focus on how and where it is lurking in your HR systems. You'll acquire essential knowledge and tools to help you gain confidence in speaking about AI system-driven biases in a way that puts your HR service providers on notice and helps you hold them accountable. We'll end the course with a brief discussion on what's next in the field of AI. (Hint, hint: It includes an avalanche of legislation!)

This week, we discuss what to do about bias in algorithms; Russia's limits in the Middle East; learning from other countries' experiences with fentanyl; what protests could mean for democracy in the Middle East; how cities can help U.S. diplomacy; and helping U.S. Army special operations forces assess their missions. Earlier this month, a controversy about gender bias in the Apple Card algorithm lit up social media; an outraged tech executive posted about how his credit line was 20 times higher than his wife's, even though the two share all assets. According to RAND's Osonde Osoba, problems like this may become more common as artificial intelligence is used in more kinds of decisionmaking. It's not always possible to pinpoint how a complex algorithm led to a bad outcome, he says. But there are ways for companies to audit algorithms for sexist, racist, biased behaviors.

"Artificial intelligence is a super powerful tool, and like any really powerful tool, it can be used to do a lot of things – some of which are good and some …

Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. Computer software teaches customer service agents how to be more compassionate, schools use machine learning to scan for weapons and mass shooters on campus, and doctors use AI to map the root cause of diseases. Sectors such as cybersecurity, online entertainment and retail use the tech in combination with wide swaths of customer data in revolutionary ways to streamline services. Though these applications may seem harmless, perhaps even helpful, the AI is only as good as the information fed into it, which can have serious implications. You might not realize it, but AI helps determine whether you qualify for a loan in some cases.