All Article Summaries

Artificial Intelligence (AI) surveillance systems often use binary male/female gender classifications, failing to recognize the complexity of LGBTQ+ identity formation.

Automated hiring, including automated video interviews, raises concerns about employment discrimination and privacy. Workplace wellness programs and electronic workplace surveillance raise similar concerns.

The Study of Underrepresented Classes Chasing Engineering and Science Success (SUCCESS) Act of 2018 requires the United States Patent Office (USPTO) to better support women, minorities, and veterans in innovation.

Automated hiring systems are not well understood. Hiring platforms can use proxies for gender and race to facilitate and conceal discrimination. Antidiscrimination law is not designed to address these new issues.

The use of algorithms to make decisions can lead to bias; one algorithm displayed a “gender neutral” ad to more men than women. The algorithm was designed to be cost-effective; because advertisers pay more to display ads to young women, the ad was shown to fewer women.

Computers now make many decisions formerly made by humans. Procedures used to oversee human decision-makers cannot be applied to computers. This article describes technological tools to help developers design algorithms consistent with social goals.

The author argues that the privacy protections are actually harmful to antidiscrimination laws in some contexts.