Decision systems that respect privacy, fairness
Increasingly, decisions and actions affecting people's lives are determined by automated systems processing personal data. Excitement about these systems has been accompanied by serious concerns about their opacity and threats they pose to privacy, fairness, and other values. Examples abound in real-world systems: Target's use of predicted pregnancy status for marketing; Google's use of health-related search queries for targeted advertising; race being associated with automated predictions of recidivism; gender affecting displayed job-related ads; race affecting displayed search ads; Boston's Street Bump app focusing pothole repair on affluent neighborhoods; Amazon's same day delivery being unavailable in black neighborhoods; and Facebook showing either "white" or "black" movie trailers based upon "ethnic affiliation."
Source: Vidya Palepu
Recognizing these concerns, CyLab's Anupam Datta, associate professor of electrical and computer engineering at Carnegie Mellon's Silicon Valley campus, will lead a $3 million National Science Foundation project on accountable decision systems that respect privacy and fairness expectations. The project seeks to make real-world automated decision-making systems accountable for privacy and fairness by enabling them to detect and explain violations of these values. The project will explore applications in online advertising, healthcare, and criminal justice, in collaboration with domain experts.
The project team includes Matthew Fredrikson, assistant professor of computer science, and Ole Mengshoel, principal systems scientist in electrical and computer engineering. The project also marks a collaboration between CMU, Cornell Tech, and the International Computer Science Institute; additional contributors are Helen Nissenbaum, professor of information science at Cornell, Thomas Ristenpart, associate professor of computer science at Cornell, and Michael C. Tschantz, senior researcher at the International Computer Science Institute in Berkeley.
"A key innovation of the project is to automatically account for why an automated system with artificial intelligence components exhibits behavior that is problematic for privacy or fairness," says Datta. "These explanations then inform fixes to the system to avoid future violations."
"The hard part is creating such explanations for systems that employ statistical machine learning," adds Mengshoel. "But doing so is critical, since these methods are increasingly used to power automated decision systems."
But in order to address privacy and fairness in decision systems, the team must first provide formal definitional frameworks of what privacy and fairness truly entail. These definitions must be enforceable and context-dependent, dealing with both protected information itself—like race, gender, or health information—as well as proxies for that information, so that the full scope of risks is covered.