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.

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AI Education NOW

Artificial intelligence will be key to helping humanity travel to new frontiers and solve problems that today seem insurmountable. It enhances human expertise, makes predictions more accurate, automates decisions and processes, frees humans to focus on higher value work, and improves our overall efficiency. But public trust in the technology is at a low point, and there is good reason for that.

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Digital Transformation 101 — New Course 2021

Digital Transformation Debt DX 101 course — covers all the key digital technologies and cultural transformation trends with demos of leading and emerging platforms, case studies, and robust pragmatic recommendations for your transformation journey. This course is online, November 15, and brought to you by DBizInstitute.org and BPMInstitute.org.

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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.

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