Building a quality artificial intelligence system takes a diverse team made up of people from various ethnic, geographic and professional backgrounds, including programmers, data scientists and ethicists. But when putting that team together, who is the best person to lead it?
Advancements in machine learning and AI are enabling more decisions to be made by algorithms with less human interaction. But allowing machines to make decisions opens a litany of ethical issues, from the inherent bias of the people developing the algorithm to systemic prejudice in the processes being automated.
In order to combat these problems—one of, if not the chief priority for federal and military AI programs—development teams have to consider these ethical issues from the start, and continue to wrestle with them throughout production and operation.
Chakib Chraibi, chief data scientist for the National Technical Information Service, or NTIS, in the Commerce Department, suggested a five-phase approach to handling ethical questions throughout the AI development lifecycle.
“The first phase is what we’re doing now: awareness,” he said Thursday on a panel discussion during ATARC’s Role of Emerging Technology in the Federal Emergency Response Virtual Summit. “We need to learn more about the AI and the ethics and the issues that are there. I’ve been trying to develop very quickly AI solutions which have given us a lot of opportunities for federal agencies to actually save money, be more efficient and more effective. But we need to be careful about how we go about that.”
The remaining phases include identifying the specific intent of the algorithm being developed; designing the solution, at which point you’ll want to gather a diverse, cross-functional team that is representative of the problem you’re trying to solve; analyze the dat ..
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