A Third-Way Approach to Regulating Facial Recognition Systems

The use of facial recognition systems powered by algorithms and software continues to raise controversy given their potential use by law enforcement and other government agencies.  For over a decade, the Department of Commerce’s National Institute for Standards and Technology (NIST) has evaluated facial recognition to identify and report gaps in its capabilities.  Its most recent report in 2019 quantified the effect of age, race, and sex on facial recognition accuracy.

The greatest discrepancies that NIST measured were higher false-positive rates in women, African Americans, and particularly African American women.  It noted, “False positives might present a security concern to the system owner, as they may allow access to impostors.  False positives also might present privacy and civil rights and civil liberties concerns such as when matches result in additional questioning, surveillance, errors in benefit adjudication, or loss of liberty.”

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Deploying U.S. AI Leadership for COVID-19

Among the cutting-edge technologies being employed by public health experts to map various aspects of COVID-19 both at home and abroad, artificial intelligence (AI) faces a test under life-and-death circumstances.  The ability of AI systems to undertake pattern detection and predict the spread of the pandemic and its treatments is promising.  The benefit of machine learning includes its powerful ability to analyze historic data to find key variables.  This task is dependent upon humans, however, specifically in the ability of data scientists who can work on creating data sets that supercomputers then can model.  On a global basis, this will require pooling both technical and human resources.

Given the unprecedented nature of COVID-19, historic data inputted for AI analysis may be of limited value.  Real-time data comparing growth curves in countries around the world, along with population and demographic information by neighborhood, may prove to be a better vein for producing actionable data anywhere and everywhere.  Automated machine learning also may improve the efficiency of data scientists, enabling them to focus on new data generation while relying on computer-to-computer analysis of massive-scale number crunching.

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