A workshop led by Elham Ali on integrating the principles of human-centered design and equity to Artificial Intelligence (AI) design, use, and evaluation.
A training course on using artificial intelligence (AI) tools to de-jargonize government language, with a tutorial on turning a complex piece of government writing into simpler and easier-to-understand language for government employees and residents alike.
This report offers a critical framework for designing algorithmic impact assessments (AIAs) by drawing lessons from existing impact assessments in areas like environment, privacy, and human rights to ensure accountability and reduce algorithmic harms.
This award documentation from the National Association of State Chief Information Officers (NASCIO) explains how agencies in Ohio used automation to support administration of public benefits programs.
National Association of State Chief Information Officers (NASCIO)
Through a field scan, this paper identifies emerging best practices as well as methods and tools that are becoming commonplace, and enumerates common barriers to leveraging algorithmic audits as effective accountability mechanisms.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
This report documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This action plan outlines Oregon’s strategic approach to adopting AI in state government, emphasizing ethical use, privacy, transparency, and workforce readiness.
This news release highlights Pennsylvania’s first-in-the-nation Generative AI pilot under Governor Shapiro, showcasing its positive impact on state employees and commitment to responsible, ethical AI use.
This executive order establishes governance, values, and oversight structures for the ethical and responsible use of generative AI technologies within the Commonwealth of Pennsylvania.
The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
This report reviews global AI governance tools, highlighting their importance in ensuring trustworthy AI, while identifying gaps and risks in their effectiveness, and offering recommendations to improve their development, oversight, and integration into policy frameworks.
