Join us for the launch of the AI Accountability Lab (AIAL)

Date: Thursday, 28th November 2024
Time: 5pm to 7pm
Venue: O'Reilly Institute, Trinity College Dublin

The AI Accountability Lab (AIAL) at Trinity College Dublin, led by Dr. Abeba Birhane, is dedicated to advancing ground-breaking research to ensure AI technologies are transparent, just, and accountable, with a focus on protecting historically marginalised communities. As AI becomes an integral part of society, the Lab’s mission is to conduct rigorous audits, challenge power asymmetries, and shape AI governance for a more equitable future.

Who Should Attend?

This event is perfect for:

AI researchers and practitioners
Policymakers and regulators
Civil society organizations
Technology professionals
Academics in the fields of AI, ethics, and social justice
Journalists covering technology, policy, and ethics

Attendee Details

Agenda

5.00pm Event starts
5.10pm Dr Abeba Birhane welcoming remarks
5.20pm Prof Gregory O’Hare, Head of School of Computer Science and Statistics, Trinity College Dublin
5.25pm Keynote address, Joseph Hackett, Secretary General of the Department of Foreign Affairs
5.35pm Fireside chat: What Accountability Means in the Current AI Climate, with Roel Dobbe, Delft University, Zeerak Talat, Centre for Technomoral Futures at Edinburgh University, and Ellen Rushe, TCD.
6.05pm Luminate / AI Collaborative
6.20pm Fireside chat: The Impact of Accountability Research on Policy with David Leslie, Alan Turing Institute, Delaram Golpayegani, ADAPT research centre, Alga Cronin, Irish Council for Civil Liberties, and Dr Patricia Scanlon, Ireland’s AI Ambassador.
6.45pm Networking and reception.
7.30pm Close

About Dr Abeba Birhane

Dr Abeba Birhane researches human behaviour, social systems, and responsible and ethical artificial intelligence and was recently appointed to the UN’s Advisory Body on AI.

Abeba's work is at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Her current research examines the challenges and pitfalls of computational models and datasets from a conceptual, empirical, and critical perspective.