Articles Posted in Algorithms

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CFPB-logo-e1655398162416-300x274As previously discussed, financial services regulators are increasingly focused on how businesses use artificial intelligence (AI) and machine learning (ML) in underwriting and pricing consumer finance products. Although algorithms provide opportunities for financial services companies to offer innovative products that expand access to credit, some regulators have expressed concern that the complexity of AI/ML technology, particularly so-called “black box” algorithms, may perpetuate disparate outcomes. Companies that use AI/ML in underwriting and pricing loans must therefore have a robust fair lending compliance program and be prepared to explain how their models work.

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In this week’s News of Note, ransomware continues to ravage institutions—including a 157-year-old college and the government of Costa Rica—AI learns to accurately predict a patient’s race based on their medical images, cryptocurrency crashes, and more.

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Algorithm-ai-box-460688633-300x220Regulators at the state and federal level are increasing their scrutiny of businesses’ use of artificial intelligence (AI). For example, recently, representatives from the Office of the Comptroller of the Currency and the New York State Department of Financial Services discussed the need for developing additional AI guidance.

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2000px-US-FederalTradeCommission-Seal_svg-300x300It might be a little meta to have a blog post about a blog post, but there’s no way around it when the FTC publishes a post to its blog warning companies that use AI to “[h]old yourself accountable—or be ready for the FTC to do it for you.” When last we wrote about facial recognition AI, we discussed how the courts are being used to push for AI accountability and how Twitter has taken the initiative to understand the impacts of its machine learning algorithms through its Responsible ML program. Now we have the FTC weighing in with recommendations on how companies can use AI in a truthful, fair and equitable manner—along with a not-so-subtle reminder that the FTC has tools at its disposal to combat unfair or biased AI and is willing to step in and do so should companies fail to take responsibility.

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Smiling black man in white t-shirt looking to the side (right). Surrounded by images evoking personal info like mapsAs part of our on-going coverage on the use and potential abuse of facial recognition AI, we bring you news out of Michigan, where the University of Michigan’s Law School, the American Civil Liberties Union (ACLU) and the ACLU of Michigan have filed a lawsuit against the Detroit Police Department (DPD), the DPD Police Chief, and a DPD investigator on behalf of Robert Williams—a Michigan resident who was wrongfully arrested based on “shoddy” police work that relied upon facial recognition technology to identify a shoplifter.

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Sand in hand on a black backgroundInteractive online platforms have become an integral part of our daily lives. While user-generated content, free from traditional editorial constraints, has spurred vibrant online communications, improved business processes and expanded access to information, it has also raised complex questions regarding how to moderate harmful online content. As the volume of user-generated content continues to grow, it has become increasingly difficult for internet and social media companies to keep pace with the moderation needs of the information posted on their platforms. Content moderation measures supported by artificial intelligence (AI) have emerged as important tools to address this challenge.

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While we’ve devoted ample time to discussing areas of potential concern regarding the application of algorithms—and algorithm bias in particular—it’s also a good time to remember algorithmic technology is poised to make our lives better, often in ways we’ll never know about.

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Machine-learning-1079012838-300x209One of the biggest obstacles self-driving cars have to get around is the one between our ears. Even as these vehicles are hitting the streets in pilot projects, three out of four Americans aren’t comfortable with the idea of their widespread use.

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Facial-recognition-bias-1190624951-300x213The sweeping use of facial recognition software across public and private sectors has raised alarm bells in communities of color, for good reason. The data that feed the software, the photographic technology in the software, the application of the software—all these factors work together against darker-skinned people.

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GettyImages-1269726290-scaled-e1606147822524-300x219As research continues to prove that AI is not an impartial arbiter of who’s who (or who’s what), various mechanisms are being devised to mitigate the collateral damage from facial recognition software.

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