“Algorithmic Accountability for the Public Sector”
Ada Lovelace Institute, AI Now Institute, & Open Government Partnership.
This report provides an overview and analysis of algorithmic accountability policies in the public sector, identifying challenges and best practices. It highlights the importance of “meaningful public engagement and civic participation in the governance of algorithmic systems”. https://www.adalovelaceinstitute.org/project/algorithmic-accountability-public-sector/
“Artificial intelligence in the city: Building civic engagement and public trust”
Ana Brandusescu and Jess Reia, Centre for Interdisciplinary Research on Montréal, McGill University.
This collection of essays brings together experience and expertise from around the world on fostering meaningful civic engagement and public trust as data and AI increasingly shape life in urban areas.
https://www.mcgill.ca/centre-montreal/projects/completed-projects/ai-city
“The legitimacy gap of algorithmic decision-making in the public sector: Why it arises and how to address it”
Pascal D. König and Georg Wenzelburger , Technology in Society
This paper argues that stakeholder involvement can address the legitimacy gaps which exist in algorithmic decision-making systems used by the state. They then propose a model for stakeholder participation.
https://www.sciencedirect.com/science/article/abs/pii/S0160791X21001639
“AI and Cities: Risks, Applications and Governance”
UN Habitat
This report serves as a guide for local authorities on the responsible use of AI by identifying risks, applications, and governance tools. It recommends public participation and deliberative processes methods to “enrich the contextual knowledge and co-design of AI systems in the city”
https://unhabitat.org/ai-cities-risks-applications-and-governance
“ Democratizing Algorithmic Fairness”
Pak Hang Wong, Philosophy and Technology
In this article, Wong argues that algorithmic fairness is a political measure because it involves choices between competing values. As such, it should be resolved politically through a deliberative approach.
https://scholars.hkbu.edu.hk/en/publications/democratizing-algorithmic-fairness
“Power and Public Participation in AI”
Eric Corbett, Emily Denton, and Sheena Erete, Equity and Access in Algorithms, Mechanisms, and Optimization
This article uses the ladder of citizen participation to evaluate participatory approaches to AI development in recent scholarship, and offers suggestions for new participatory approaches.
https://dl.acm.org/doi/fullHtml/10.1145/3617694.3623228
“ What do the public think about AI? Understanding public attitudes and how to involve the public in decision-making about AI”
Ada Lovelelace Institute
This evidence review summarizes existing research on people’s views on AI technologies, and provides “evidence-based solutions for how to meaningfully include the views of the public in decision-making processes”.https://www.adalovelaceinstitute.org/evidence-review/what-do-the-public-think-about-ai/
“Democratizing AI: Principles for Meaningful Public Participation”
Michele Gilman, Data & society
This report identifies critical uses of AI where public participation is particularly important, and provides practical guidelines for meaningful public participation