This publication seeks to answer one of the most common questions that CIOs ask: “What are other states doing with generative AI and what is the role of the state CIO?”
National Association of State Chief Information Officers (NASCIO)
This policy supports the appropriate development, deployment, and use of generative artificial intelligence (GenAI) systems, products, services, tools, and content within consolidated state agencies in Colorado.
Colorado Governor's Office of Information Technology (OIT)
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.
A catalogue to help teams design trustworthy services that work for people. Categories including informing decisions, signing into services, giving and removing consent, and doing security checks.
In this interview, Code for America staff members share how client success, data science, and qualitative research teams work together to consider the responsible deployment of artificial intelligence (AI) in responding to clients who seek assistance with three products.
The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs and debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.
This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
