We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. While AI-driven solutions are quickly becoming a mainstream technology across industries, it has also become clear that deployment requires careful management to prevent unintentional damage. As is the case with most tools, AI has the potential to expose individuals and enterprises to an array of risks, risks that could have otherwise been mitigated through diligent assessment of potential consequences early on in the process. This is where "responsible AI" comes in -- that is, a governance framework that documents how a specific organization should address the ethical and legal challenges surrounding AI. A key motivation for responsible AI endeavors is resolving uncertainty about who is accountable if something goes wrong.

Anja Kaspersen and Wendell Wallach are senior fellows at Carnegie Council for Ethics in International Affairs. In November 2021, they published an article that changed the AI ethics conversation: Why Are We Failing at the Ethics of AI? Six months later, the questions the article raised are no closer to resolution. This article was a don't-hold-your-punches review on the state of AI ethics, with which I am in almost complete agreement. If we want to advance the AI conversation, this is still a good place to start. I've quoted a portion of their article, with my comments interspersed: While it is clear that AI systems offer opportunities across various areas of life, what amounts to a responsible perspective on their ethics and governance is yet to be realized.

These new technologies, while promising in many respects, have garnered the attention of the Equal Employment Opportunity Commission, which last year launched the Artificial Intelligence and Algorithmic Fairness Initiative. The EEOC announced the initiative's intended mission "to ensure that the use of software, including artificial intelligence (AI), machine learning and other emerging technologies used in hiring and other employment decisions comply with the federal civil rights laws that the EEOC enforces."

Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair8 and interpretable16 machine learning. While important, much of this work has been limited in scope to the "last mile" of data analysis and has disregarded both the system's design, development, and use life cycle (What are we automating and why? Is the system working as intended? Are there any unforeseen consequences post-deployment?) and the data life cycle (Where did the data come from? How long is it valid and appropriate?). In this article, we argue two points. First, the decisions we make during data collection and preparation profoundly impact the robustness, fairness, and interpretability of the systems we build. Second, our responsibility for the operation of these systems does not stop when they are deployed. To make our discussion concrete, consider the use of predictive analytics in hiring. Automated hiring systems are seeing ever broader use and are as varied as the hiring practices themselves, ranging from resume screeners that claim to identify promising applicantsa to video and voice analysis tools that facilitate the interview processb and game-based assessments that promise to surface personality traits indicative of future success.c Bogen and Rieke5 describe the hiring process from the employer's point of view as a series of decisions that forms a funnel, with stages corresponding to sourcing, screening, interviewing, and selection. The hiring funnel is an example of an automated decision system--a data-driven, algorithm-assisted process that culminates in job offers to some candidates and rejections to others. The popularity of automated hiring systems is due in no small part to our collective quest for efficiency.

On May 12, 2022, the U.S. Equal Employment Opportunity Commission (EEOC) issued a "Technical Assistance" (TA) document addressing compliance with …

Security cameras are everywhere, but artificial intelligence is changing the way they are being used. KRON4 tested one such system at a grocery store in San Jose to see how A.I. is preventing shoplifting. Picture this scenario, someone walks into Lunardi's Market on Meridian Street. They decide to take home a nice bottle of Merlot -- only they also decide not to pay. But before they can walk out the door, the store manager steps in and stops them.

Ryan N. Phelan is a registered patent attorney who counsels and works with clients in all areas of intellectual property (IP), with a focus on patents. Clients enjoy Ryan's business-focused approach to IP. With a MBA from Northwestern's Kellogg School of Management, Ryan works with clients to achieve their business objectives, including developing and protecting their innovations and businesses with IP.

When Peter George saw news of the racially motivated mass-shooting at the Tops supermarket in Buffalo last weekend, he had a thought he's often had after such tragedies. "Could our system have stopped it?" he said. But I think we could democratize security so that someone planning on hurting people can't easily go into an unsuspecting place." George is chief executive of Evolv Technology, an AI-based system meant to flag weapons, "democratizing security" so that weapons can be kept out of public places without elaborate checkpoints. As U.S. gun violence like the kind seen in Buffalo increases -- firearms sales reached record heights in 2020 and 2021 while the Gun Violence Archive reports 198 mass shootings since January -- Evolv has become increasingly popular, used at schools, stadiums, stores and other gathering spots. To its supporters, the system is a more effective and less obtrusive alternative to the age-old metal detector, making events both safer and more pleasant to attend. To its critics, however, Evolv's effectiveness has hardly been proved. And it opens up a Pandora's box of ethical issues in which convenience is paid for with RoboCop surveillance. "The idea of a kinder, gentler metal detector is a nice solution in theory to these terrible shootings," said Jay Stanley, senior policy analyst for the American Civil Liberties Union's project on speech, privacy, and technology. "But do we really want to create more ways for security to invade our privacy?

Depending on which Terminator movies you watch, the evil artificial intelligence Skynet has either already taken over humanity or is about to do so. But it's not just science fiction writers who are worried about the dangers of uncontrolled AI. In a 2019 survey by Emerj, an AI research and advisory company, 14% of AI researchers said that AI was an "existential threat" to humanity. Even if the AI apocalypse doesn't come to pass, shortchanging AI ethics poses big risks to society -- and to the enterprises that deploy those AI systems. Central to these risks are factors inherent to the technology -- for example, how a particular AI system arrives at a given conclusion, known as its "explainability" -- and those endemic to an enterprise's use of AI, including reliance on biased data sets or deploying AI without adequate governance in place.

In the second of a series of blogs from our global offices, we provide a overview of key trends in artificial intelligence in France. What is France's strategy for Artificial Intelligence? The French president, Emmanuel Macron, announced in March 2018 his ambition for France to become a global leader of the artificial intelligence (AI) ecosystem. The first phase of the National Programme included an initial investment of €1.5 billion into the creation of a network of interdisciplinary institutes dedicated to artificial intelligence (the "3IA" institutes) and the financing of multiple AI projects overseen by Bpifrance. The second phase will provide for €2 billion of private and public funding to attract and train new talent.