· 2020
Closing the AI accountability gap
- 1,064citations
- 2020year
Short summary
New methods are needed to identify AI harms before deployment, as current external audits struggle to trace emergent issues back to their source.
AI-generated from the title and abstract; the full text is not read.
Key points
- External audits of deployed AI systems are becoming more common.
- Practitioners face challenges in identifying AI system harms before deployment.
- Emergent issues in deployed AI systems are difficult to trace to their origin.
- There is a need for methods to proactively identify AI harms.
AI-generated from the title and abstract; the full text is not read.
Abstract
Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, emergent issues can become difficult or impossible to trace back to their source.
The authors' abstract, as published at the source., 2020 · DOI ↗
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Field: Safety Research
Safety ResearchSocial Sciences