Originally published 22 Dec 19
Here is an excerpt:
Inevitably, “there will be lawsuits that require you to reveal the human decisions behind the design of your AI systems, what ethical and social concerns you took into account, the origins and methods by which you procured your training data, and how well you monitored the results of those systems for traces of bias or discrimination,” warns Mike Walsh, CEO of Tomorrow, and author of The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You, in a recent Harvard Business Review article. “At the very least trust, the algorithmic processes at the heart of your business. Simply arguing that your AI platform was a black box that no one understood is unlikely to be a successful legal defense in the 21st century. It will be about as convincing as ‘the algorithm made me do it.’”
It’s more than legal considerations that should drive new thinking about AI ethics. It’s about “maintaining trust between organizations and the people they serve, whether clients, partners, employees, or the general public,” a recent report out of Accenture maintains. The report’s authors, Ronald Sandler and John Basl, both with Northeastern University’s philosophy department, and Steven Tiell of Accenture, state that a well-organized data ethics capacity can help organizations manage risks and liabilities associated with such data misuse and negligence.
“It can also help organizations clarify and make actionable mission and organizational values, such as responsibilities to and respect for the people and communities they serve,” Sandler and his co-authors advocate. A data ethics capability also offers organizations “a path to address the transformational power of data-driven AI and machine learning decision-making in an anticipatory way, allowing for proactive responsible development and use that can help organizations shape good governance, rather than inviting strict oversight.”
The info is here.