Algorithmic Impact Methods Lab

AIMLab’s approach was developed through iterative field testing with local government, non-profits, industry, and community-based organizations. These pilots helped us refine our approach.

Executive Summary

– The first three AIMLab pilots in San José, South Africa, and San Francisco tested participatory methods of algorithmic impact assessment in very different domains: municipal governance, gender-based violence support, and commercial wellness. Taken together, they show the promises and limits of participatory AIA.

– Pilots showed that AIAs surface concrete, actionable insights about risks and harms when impacted communities are directly engaged in the process.

– Our findings confirmed that technical expertise in AI is not required for participants to challenge a system’s premise and anticipate its real-world consequences.

– Across all of our cases, participants surfaced risks that technical audits alone would miss: criminalization of homelessness, retraumatization of survivors, dependency on wellness chatbots. Participants consistently flagged sensitive data practices, surveillance risks, and questioned whether automation was appropriate for the problems at hand.

– AIA findings carry weight when backed by external accountability pressures, such as news media or policy mandates. In the absence of regulatory or institutional commitments to act on identified risks, AIA risks sliding into symbolism rather than responding to anticipated harms.

Case reports from our pilots

– Pilot 1: City of San Jose

– Pilot 2: Kwanele and Mozilla

– Pilot 3: Wellness company