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.
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 examines how governments can effectively build, attract, and retain AI talent to responsibly integrate artificial intelligence into public service delivery.
Best practices for procuring and developing accessible tools that utilize artificial intelligence (AI) and how to ensure that they are in compliance with the Web Content Accessibility Guidelines (WCAG) 2.1 Level A and Level AA.
The Commonwealth of Massachusetts Executive Office of Technology Services and Security (EOTSS)
Sarah Bargal provides an overview of AI, machine learning, and deep learning, illustrating their potential for both positive and negative applications, including authentication, adversarial attacks, deepfakes, generative models, personalization, and ethical concerns.
This report analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court.
DSN Spotlights are short-form project profiles that feature exciting work happening across our network of digital government practitioners. Spotlights celebrate our members’ stories, lift up actionable takeaways for other practitioners, and put the resources + examples we host in the Digital Government Hub in context.Â
This guide, directed at poverty lawyers, explains automated decision-making systems so lawyers and advocates can better identify the source of their clients' problems and advocate on their behalf. Relevant for practitioners, this report covers key questions around automated decision-making systems.
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.
The state of Indiana developed a policy framework for the ethical and efficient use of artificial intelligence (AI) within state agencies. The policy adopts the National Institute of Standards and Technology’s AI Risk Management Framework to manage potential risks effectively. It also details the applicability of the actions undertaken by the Office of the Chief Data Officer (OCDO) to enable the deployment of trustworthy AI systems.
