AI for Social Good: Why "Good" Isn't Enough with Ben Green - #368

This article discusses the limitations of current AI research focused on social good. It highlights the work of Ben Green, a PhD candidate at Harvard and research fellow at the AI Now Institute at NYU. Green's research centers on the social and policy implications of data science, particularly algorithmic fairness and the criminal justice system. The core argument, based on his paper 'Good' Isn't Good Enough,' is that AI research often lacks a clear definition of "good" and a "theory of change," hindering its effectiveness in achieving positive social impact. The article suggests a need for more rigorous definitions and a strategic approach to implementing AI solutions.

• •AI research for social good needs a clear definition of what constitutes "good."
• •A "theory of change" is crucial for understanding how AI interventions will achieve desired outcomes.
• •Algorithmic fairness and the criminal justice system are key areas of focus for Green's research.