There are many applications that can help public agencies improve outcomes and streamline processes to make the best use of limited resources. New technologies, including AI and ML do come with challenges, and understanding these risks, including bias, is crucial for organizations to increase positive outcomes.
Advanced analytics can transform public-sector services, but US agencies are wary of the risk of biased outcomes. Sound risk management is the key to a wider adoption that will benefit all citizens. The solution may partially be found in better model risk management. By applying practices to minimize the risk of bias and other forms of unfair treatment, leaders can help empower their institutions to adopt mission-enhancing AI and ML approaches while increasing public confidence in the government’s use of analytics to improve outcomes for all.
Here, we outline a best-practice approach to developing and monitoring algorithms that can help public-sector agencies harness the power of advanced analytics to deliver better public services, while mitigating bias and other forms of unfair treatment, and offer six key actions that will help mitigate the risk of bias, along with other risks associated with advanced analytics models.
This article was originally published on McKinsey.com on July 16, 2021 and is reprinted here by permission.