Measuring AI’s Societal Impact, Not Just Performance

Evaluation of AI’s Impact on Humans

The article examines the prevailing focus of AI research on technical performance metrics and the lack of attention toward its psychosocial impacts on humans. Imran Khan of the Center for Humane Technology argues that as AI reshapes cognition, relationships, and behavior, there needs to be a systematic effort to measure its influence on human well-being. Khan highlights that while AI companies drive progress based on short-term task performance benchmarks, they often neglect long-term and broader societal harms, similar to the oversight seen with social media. The article emphasizes the need for long-term studies and post-deployment monitoring of AI, akin to how the pharmaceutical industry manages drug safety over extended periods. Additionally, Khan discusses the industry’s reluctance to share data for evaluating these broader impacts but suggests that regulatory frameworks and liability could shift incentives. The ultimate goal is to foster collaboration among AI experts, regulators, and researchers to create better, safer, and more humane AI interactions.

Key Points:

Psychosocial Impact Ignored: The evaluation of AI performance often misses crucial psychosocial impacts on humans.
Need for Long-Term Studies: Evaluating AI’s effects requires long-term studies and post-deployment monitoring.
Industry Incentives: Liability concerns and industry pressure could drive companies to participate in data sharing.
Future Collaboration: Success requires multidisciplinary collaboration to define a humane relationship between humans and AI.
Urgency of Evaluation: The rapid technological advancements outpace the progress in understanding AI’s broader effects.