AI Safety, Strategy, and Frameworks
The Harm Blindness Framework: A Practical Application Methodology for Stakeholder Harm Prevention in Technology Development
- Travis Gilly, Real Safety AI Foundation
Publisher: Real Safety AI Foundation
Research paper.
- Pages
- 27
Abstract
Technology development consistently produces preventable harm to stakeholders who were identifiable at the time of key decisions. This paper introduces the Harm Blindness Framework, a checkpoint-based methodology designed to surface stakeholder impacts during development rather than after deployment. The framework operationalizes consultation of historical precedent through structured analysis at four decision points: ideation, design, testing, and launch. Validation against 161 historical cases spanning approximately 5,000 years reveals that 95.7% of analyzed harms had documented precedent available at the time decisions were made. The remaining 4.3% represent genuinely first-of-kind situations. A parallel analysis of 151 corporate disasters from 1970-2025 demonstrates that prevention costs typically range from 1-10% of eventual disaster costs, yielding return-on-investment ratios of 1,000-10,000x for harm prevention.
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Gilly, Travis. "The Harm Blindness Framework: A Practical Application Methodology for Stakeholder Harm Prevention in Technology Development." Real Safety AI Foundation Research Paper, n.d.. https://realsafetyai.org/research/pe7nk5/
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