# Towards Meaningful Transparency in Civic AI Systems

arXiv: https://arxiv.org/abs/2510.07889 · HTML: https://mlsanity.com/paper/2510.07889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 162 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 29 of 29)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Designing for Responsible Trust in AI Systems: A Communication Perspective](https://mlsanity.com/paper/2204.13828/) (2022)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems](https://mlsanity.com/paper/2102.04201/) (2021)
- [CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations](https://mlsanity.com/paper/2108.08197/) (2021)
- [Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation](https://mlsanity.com/paper/2302.11623/) (2023)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Epistemic Injustice in Generative AI](https://mlsanity.com/paper/2408.11441/) (2024)
- [Mechanism Design for Social Good](https://mlsanity.com/paper/1810.09832/) (2018)
- [Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute](https://mlsanity.com/paper/2302.04603/) (2023)
- [(Un)making AI Magic: a Design Taxonomy](https://mlsanity.com/paper/2403.15216/) (2024)
- [How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India](https://mlsanity.com/paper/2205.05661/) (2022)
- [Blockchain and Beyond: Understanding Blockchains through Prototypes and Public Engagement](https://mlsanity.com/paper/2112.11891/) (2021)
- [Towards an accountable Internet of Things: A call for reviewability](https://mlsanity.com/paper/2102.08132/) (2021)
- [A Room With an Overview: Towards Meaningful Transparency for the Consumer Internet of Things](https://mlsanity.com/paper/2401.10669/) (2024)
- [Algorithmic Transparency and Participation through the Handoff Lens: Lessons Learned from the U.S. Census Bureau's Adoption of Differential Privacy](https://mlsanity.com/paper/2405.19187/) (2024)
- ["Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications](https://mlsanity.com/paper/2503.03927/) (2025)

## Built on by (most cited first, 1 of 1)

- [Sovereign AI-based Public Services are Viable and Affordable](https://mlsanity.com/paper/2603.01869/) (2026)

## Abstract (arXiv)

Artificial intelligence has become a part of the provision of governmental services, from making decisions about benefits to issuing fines for parking violations. However, AI systems rarely live up to the promise of neutral optimisation, creating biased or incorrect outputs and reducing the agency of both citizens and civic workers to shape the way decisions are made. Transparency is a principle that can both help subjects understand decisions made about them and shape the processes behind those decisions. However, transparency as practiced around AI systems tends to focus on the production of technical objects that represent algorithmic aspects of decision making. These are often difficult for publics to understand, do not connect to potential for action, and do not give insight into the wider socio-material context of decision making. In this paper, we build on existing approaches that take a human-centric view on AI transparency, combined with a socio-technical systems view, to develop the concept of meaningful transparency for civic AI systems: transparencies that allow publics to engage with AI systems that affect their lives, connecting understanding with potential for action.