Foundational Challenges in Assuring Alignment and Safety of Large Language Models

no code implementations • 15 Apr 2024 • Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob Foerster, Florian Tramer, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs).

What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems

no code implementations • 28 Sep 2019 • Javier Sanchez-Monedero, Lina Dencik, Lilian Edwards

The ability to get and keep a job is a key aspect of participating in society and sustaining livelihoods.

Algorithms that Remember: Model Inversion Attacks and Data Protection Law

no code implementations • 12 Jul 2018 • Michael Veale, Reuben Binns, Lilian Edwards

Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms.

Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"?

no code implementations • 20 Mar 2018 • Lilian Edwards, Michael Veale

As concerns about unfairness and discrimination in "black box" machine learning systems rise, a legal "right to an explanation" has emerged as a compellingly attractive approach for challenge and redress.