Even as artificial intelligence (AI) has become more commonplace and relied upon by businesses in different industries, it still faces criticism on whether it can be implemented in a safe and ethical manner and, related, how the bias often inherent in the underlying algorithms can be detected and reduced.
Artificial intelligence has long since evolved from a technology with exciting potential to a near ubiquitous and integral component in the day-to-day conduct of many businesses. Take the automotive and aerospace industries—each is undergoing massive changes and movements toward more competitive, efficient and innovative uses of technology and AI in order to meet consumer demands, create more efficient factories, optimize supply chains, and achieve better performance in operations and production. Using modern software and AI has become essential across many companies.
As 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.
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.
Regulators 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.
It 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.
As 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.
Interactive 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.
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.