One 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.
The 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.
As 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.
We’ve previously touched on some of the issues caused by AI bias. We’ve described how facial recognition technology may result in discriminatory outcomes, and more recently, we’ve addressed a parade of “algorithmic horror shows” such as flash stock market crashes, failed photographic technology, and egregious law enforcement errors. As uses of AI technology burgeons, so, too, do the risks. In this post, we explore ways to allocate the risks caused by AI bias in contracts between developers/licensors of the products and the customers purchasing the AI systems. Drafting a contract that incentivizes the AI provider to implement non-biased techniques may be a means to limit legal liability for AI bias.
Say what you want about the digital ad you received today for the shoes you bought yesterday, but research shows that algorithms are a powerful tool in online retail and marketing. By some estimates, 80 percent of Netflix viewing hours and 33 percent of Amazon purchases are prompted by automated recommendations based on the consumer’s viewing or buying history.
But algorithms may be even more powerful where they’re less visible—which is to say, everywhere else. Between 2015 and 2019, the use of artificial intelligence technology by businesses grew by more than 270 percent, and that growth certainly isn’t limited to the private sector.
As we’ve discussed in this space previously, the effect of AI bias, especially in connection with facial recognition, is a growing problem. The most recent example—users discovered that the Twitter photo algorithm that automatically crops photos seemed to consistently crop out black faces and center white ones. It began when a user noticed that, when using a virtual background, Zoom kept cropping out his black coworker’s head. When he tweeted about this phenomenon, he then noticed that Twitter automatically cropped his side-by-side photo of him and his co-worker such that the co-worker was out of the frame and his (white) face was centered. After he posted, other users began performing their own tests, generally finding the same results.
As the world grapples with the impacts of the COVID-19 pandemic, we have become increasingly reliant on artificial intelligence (AI) technology. Experts have used AI to test potential treatments, diagnose individuals, and analyze other public health impacts. Even before the pandemic, businesses were increasingly turning to AI to improve efficiency and overall profit. Between 2015 and 2019, the adoption of AI technology by businesses grew more than 270 percent.
As the world continues to deal with the unprecedented challenges caused by the COVID-19 pandemic, Artificial Intelligence (AI) systems have emerged as a potentially formidable tool in detecting and predicting outbreaks. In fact, by some measures the technology has proven to be a step ahead of humans in tracking the spread of COVID-19 infections. In December 2019, it was a website-leveraging AI technology that provided one of the key early warnings of an unknown form of pneumonia spreading in Wuhan, China. Soon after, information sharing among medical professionals followed as experts tried to understand the extent of the unfolding public health crisis. While humans eventually acted on these warnings, the early detection enabled through use of AI-supported data aggregation demonstrates both the promise and potential concerns associated with these systems.
We’ve discussed before the potential of AI to detect financial crimes like money laundering. On March 23, colleague Deborah Thoren-Peden will explore the growing nexus between artificial intelligence and the detection and prevention of financial misdeeds.
In “Leveraging AI to Combat Financial Crimes,” Thoren-Peden will be joined by Sam Small (ZeroFox) and Tim Mueller (GuideHouse) to discuss how AI is being integrated into RegTech solutions for enhanced AML compliance and screening, and how AI is being used to monitor insider trading, market manipulation and other suspicious market activities. In addition, they will identify best practices from law enforcement and financial institutions where AI is being successfully deployed to curb financial criminal activity.
Apple gets around to AR, the NHL enters esports, the Internet of Things may bring new meaning to “workers unite,” so many medical records, and more …
Internet & Social Media Law Blog