On the black box: “The 10 Top Recommendations for the AI Field in 2017”

AI Now, comprised of researchers from Google Open Research, Microsoft Research, and New York University, issued a list of top 10 Recommendations for AI.  Worth the read.  Here are two recommendations of note:

2 — Before releasing an AI system, companies should run rigorous pre-release trials to ensure that they will not amplify biases and errors due to any issues with the training data, algorithms, or other elements of system design. As this is a rapidly changing field, the methods and assumptions by which such testing is conducted, along with the results, should be openly documented and publicly available, with clear versioning to accommodate updates and new findings.

And:

4 — More research and policy making is needed on the use of AI systems in workplace management and monitoring, including hiring and HR. This research will complement the existing focus on worker replacement via automation. Specific attention should be given to the potential impact on labor rights and practices, and should focus especially on the potential for behavioral manipulation and the unintended reinforcement of bias in hiring and promotion.

A worthwhile read.

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