Assessing AI Governance Maturity Model – NIST AI RMF

Organizations often face challenges in adopting appropriate AI governance and practices. The AI risk maturity model is crucial for organizations seeking to understand, assess, and improve their programs and processes. It provides a North Star, guiding organizations in their AI maturity journey and paving the way for continuous improvement Fortunately, NIST’s AI RMF provides a […]

Evolving AI Risk Management: A Maturity Model based on the NIST AI Risk Management Framework

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 […]

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 […]