Marco, David. “Understanding Black Box AI: Challenges and Solutions.” EWSolutions, DataManagementU, 28 July 2025, www.ewsolutions.com/understanding-black-box-ai/. Accessed 29 Sept. 2025.
This article offers further discussion on the black-box problem, particularly through a number of challenges and issues with AI automation in practice such as bias, issues of legal liability, the loss of trust, and opaque decision-making methods. A handful of offered cases through this piece may be familiar, echoes of issues that we’re undoubtedly at least aware of. Be it the disaccreditation of women in hiring practices, predictive policing tools and the designation of certain places as “high-risk” for unknown reasons and more, there are clear questions to be had at how AI tools arrive at the conclusions that they do. Ensuring that these tools and their creators are held accountable and operate within a just framework is paramount to ensure that other areas of our lives, even if only grazed by AI technologies, remain largely unaffected and ideally improved.
Besides some of the potential cases to examine on the issues of black-box AI decision making, a number of other factors to consider are offered by this piece and may give further insight on where to further refine focus to articulate an argument for generative AI operational transparency.
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