Machine learning systems are deployed in consequential domains such as education, employment, and credit, where decisions have profound effects on socioeconomic opportunity and life outcomes. High stakes decision settings present new statistical, ...
Machine learning algorithms are widely used for decision making in societally high-stakes settings from child welfare and criminal justice to healthcare and consumer lending. Recent history has illuminated numerous examples where these algorithms proved unreliable ...
Machine learning (ML) is being deployed to a vast array of real-world applications with profound impacts on society. ML can have positive impacts, such as aiding in the discovery of new cures for diseases and improving government transparency and efficiency. But it can also be harmful: reinforcing authoritarian regimes, scaling ...
Online platforms like Facebook, Wikipedia, Amazon, and LinkedIn are embedded in the very fabric of our society. They “curate content:” moderate, recommend, and monetize it, and, in doing so, can impact people’s lives positively or negatively. This ...
Platforms, devices, and algorithms are increasingly weaponized to control and harass the most vulnerable among us. Some of these harms occur at the individual and interpersonal level: for example, abusers in intimate partner violence (IPV) use smartphones and social media to surveil ...
The learned behaviors of AI and robot agents should align with the intentions of their human designers. Alignment is necessary for AI systems to be used in many sectors of the economy, and so the process of aligning AI systems becomes critical to study for defining effective AI policy. Toward ...