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IBM's Dan Chenok: Data Governance, Explainable Algorithms Could Help Agencies Address AI-Related Risks

IBM's Dan Chenok: Data Governance, Explainable Algorithms Could Help Agencies Address AI-Related Risks - top government contractors - best government contracting event
Dan Chenok
IBM's Dan Chenok: Data Governance, Explainable Algorithms Could Help Agencies Address AI-Related Risks - top government contractors - best government contracting event
Dan Chenok

Dan Chenok, executive director of the IBM Center for The Business of Government, wrote in a Nextgov article published Thursday that government agencies should address ethical issues and risks associated with the implementation of artificial intelligence and one of the measures to consider is developing explainable algorithms.

“Well-designed, explainable models can increase accuracy in government service delivery, such as a neural network that could correct an initial decision to deny someone benefits for which they are entitled,“ Chenek wrote.

He noted that agencies should apply AI ethics within a cost-benefit framework, develop a strategic vision for the use of AI and come up with a data governance framework that includes audits and testing.

“Another element of data governance could set-up protocols for inter-agency data sharing, which can increase efficiency but also introduce privacy risk“”privacy protection within AI has engendered divergent views, and using a risk management perspective allows for agencies to assess how much personal data they need to collect and store based on the benefits to the data subjects,“ he added.

Chenok also cited the role of quality data in AI adoption.

“Agencies can build a greater understanding of the benefits and risks of AI, and help employees and citizens within an ethical framework, by reaching decisions that emerge from the responsible use of the right data,“ he noted.

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