AIAAIC is an independent, non-partisan, public interest initiative that examines and makes the case for real AI, algorithmic, and automation transparency and openness.

AIAAIC's independent, free, open library identifies and assesses 850+ incidents and controversies driven by and relating to AI, algorithms and automation.

  • Forrester Research study uses AIAAIC Repository to set out how to build confidence and trust in AI decision-making

  • University of Cambridge/Data & Society Research Institute study says the AIAAIC Repository 'painstakingly .. establishes the significant scale of algorithmic harms' across the world

  • Science|Business quotes AIAAIC founder Charlie Pownall on the politics and identity of the UK's post-Brexit AI strategy

  • The European Commission's AI Watch cites the AIAAIC Repository in AI in Public Services report

  • AIAAIC founder Charlie Pownall discusses the trustworthiness of black box systems with IEEE Spectrum

Crowdsourced collection of current and proposed laws mandating the transparency of AI and algorithmic systems

Review of current government and company AI and algorithmic transparency models, types, and forms

AIAAIC believes that AI, algorithms, and automation, and the organisations and individuals involved in their design, development, and deployment, must be transparent and honest about their aims and how they go about pursuing them. More

Transparency is cited as a core principle of ethical, responsible and trustworthy AI. But it is often approached in a partial, piecemeal, and reactive manner.

Here's why real transparency and openness of AI, algorithmic and automation systems is needed, and what it should look like.

AIAAIC content is available to use, copy, adapt, and redistribute under a CC BY-SA 4.0 license. More

AIAAIC CC BY-SA 4.0 license