The privacy legislative proposals that involve these issues do not address artificial intelligence in name. To evaluate the effect of AI on privacy, it is necessary to distinguish between data issues that are endemic to all AI, like the incidence of false positives and negatives or overfitting to patterns, and those that are specific to use of personal information. 8 These both raise significant issues, but privacy legislation is complicated enough even without packing in all the social and political issues that can arise from uses of information. The discussion of AI in the context of the privacy debate often brings up the limitations and failures of AI systems, such as predictive policing that could disproportionately affect minorities 7 or Amazon’s failed experiment with a hiring algorithm that replicated the company’s existing disproportionately male workforce. The challenge for Congress is to pass privacy legislation that protects individuals against any adverse effects from the use of personal information in AI, but without unduly restricting AI development or ensnaring privacy legislation in complex social and political thickets. In this brief, I discuss some potential concerns regarding artificial intelligence and privacy, including discrimination, ethical use, and human control, as well as the policy options under discussion.
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As Congress considers comprehensive privacy legislation to fill growing gaps in the current checkerboard of federal and state privacy, it will need to consider if or how to address use personal information in artificial intelligence systems. This policy brief explores the intersection between AI and the current privacy debate. 5 California, New Hampshire, and Oregon all have enacted legislation banning use of facial recognition with police body cameras. Owing to concerns over facial recognition, the cities of Oakland, Berkeley, and San Francisco in California, as well as Brookline, Cambridge, Northampton, and Somerville in Massachusetts, have adopted bans on the technology. However, China’s use of facial recognition as a tool of authoritarian control in Xinjiang 4 and elsewhere has awakened opposition to this expansion and calls for a ban on the use of facial recognition. Facial recognition systems are being deployed in cities and airports around America.
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With the benefit of rich databases of digital photographs available via social media, websites, driver’s license registries, surveillance cameras, and many other sources, machine recognition of faces has progressed rapidly from fuzzy images of cats 3 to rapid (though still imperfect) recognition of individual humans. “As artificial intelligence evolves, it magnifies the ability to use personal information in ways that can intrude on privacy interests by raising analysis of personal information to new levels of power and speed.”įacial recognition systems offer a preview of the privacy issues that emerge. As artificial intelligence evolves, it magnifies the ability to use personal information in ways that can intrude on privacy interests by raising analysis of personal information to new levels of power and speed. Much of the most privacy-sensitive data analysis today–such as search algorithms, recommendation engines, and adtech networks–are driven by machine learning and decisions by algorithms. Streams of data from mobile phones and other online devices expand the volume, variety, and velocity of information about every facet of our lives and puts privacy into the spotlight as a global public policy issue.Īrtificial intelligence likely will accelerate this trend. And velocity facilitates analysis as well as sharing in real time. Variety adds to this power and enables new and unanticipated inferences and predictions. 2 More data makes analysis more powerful and more granular. Twitter impact of big data is commonly described in terms of three “Vs”: volume, variety, and velocity.