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Wednesday, November 6, 2019

How to operationalize AI ethics

Khari Johnson
Originally published October 7, 2019

Here is an excerpt:

Tools, frameworks, and novel approaches

One of Thomas’ favorite AI ethics resources comes from the Markkula Center for Applied Ethics at Santa Clara University: a toolkit that recommends a number of processes to implement.

“The key one is ethical risk sweeps, periodically scheduling times to really go through what could go wrong and what are the ethical risks. Because I think a big part of ethics is thinking through what can go wrong before it does and having processes in place around what happens when there are mistakes or errors,” she said.

To root out bias, Sobhani recommends the What-If visualization tool from Google’s People + AI Research (PAIR) initiative as well as FairTest, a tool for “discovering unwarranted association within data-driven applications” from academic institutions like EPFL and Columbia University. She also endorses privacy-preserving AI techniques like federated learning to ensure better user privacy.

In addition to resources recommended by panelists, Algorithm Watch maintains a running list of AI ethics guidelines. Last week, the group found that guidelines released in March 2018 by IEEE, the world’s largest association for professional engineers, have seen little adoption at Facebook and Google.

The info is here.