10 Lessons Learned for Assessing and Mitigating Unexpected Patterns in AI Models (Part 1 of 3)

Getting to trustworthy AI is not simple. It takes a lot of moving parts and collective resources (well beyond a data science team) to get every element right, from people and culture to governance to data and processes. But that’s true of most worthy endeavors. In this 3 part series, author Sheri Feinzig and Phaedra Boinodiris walk you through the top 10 lessons learned for both assessing and mitigating for unexpected patterns in AI models.

Phaedra BoinodirisPhaedra BoinodirisFebruary 28, 2022lessons learned, bias, patterns, ai models, systemic empathy, AI ethics

Combating Toxicity in Online Games

AI holds great promise in the ability to stem online toxic behavior from within games, but there are complications that can be daunting. By tackling the three pillars of Culture, Forensic Technology, and Governance Standards, a game publisher can use AI to help their community managers create safe environments that also balances free speech factors.

Phaedra BoinodirisPhaedra BoinodirisNovember 15, 2020gaming, bias, cyber-bullyingComment

Mitigating the risks of Intelligent Automation: Bias, worker displacement and more

Intelligent automation offers much promise to companies to drive efficiencies but it is not the panacea. By adopting a responsible framework for the deployment of such systems, companies can ensure that they are not inadvertently causing individual or societal or indeed economic harm to their business.