Evolving Risk Management
Ravit Dotan, Borhane Blili-Hamelin, Ravi Madhavan, Jeanna Matthews, Joshua Scarpino Researchers, government bodies, and organizations have been repeatedly calling for a shift in the responsible AI community from general principles to tangible and operationalizable practices in mitigating the potential sociotechnical harms of AI. Frameworks like the NIST AI RMF embody an emerging consensus on recommended practices in operationalizing sociotechnical […]
Evaluating AI Governance
AI governance programmes vary enormously in their substance. This paper provides a practical methodology for assessing what is real and what is documentation.
Ethical Artificial Intelligence and Machine Learning By Design
The underlying consequences of implementing technologies without fully understanding privacy, bias and possible discrimination remain a constant threat for organizations as they integrate new technologies. When these issues remain present, it impacts every individual’s ability to participate in society fairly. They can include personal reputational damage, financial impacts, litigation, regulatory backlash, privacy concerns and ultimately […]
Evaluating Ethical Challenges in AI and ML
In general, privacy, bias and discrimination are currently receiving a lot of attention. However, it is common for them to be underprioritized in technology implementations and treated as isolated issues, only receiving attention when necessary. Many organizations instead prioritize goals such as efficiency gains or increased profits, which often require richer data sets, but they […]