Objective Robustness in Deep Reinforcement Learning. - Jack Koch; Lauro Langosco; Jacob Pfau; James Le; Lee Sharkey (CoRR, 2021)

Goal Misgeneralization in Deep Reinforcement Learning. - Lauro Langosco di Langosco; Jack Koch; Lee D. Sharkey; Jacob Pfau; David Krueger 001 (ICML, 2022)

Interpreting Neural Networks through the Polytope Lens. - Sid Black; Lee Sharkey; Léo Grinsztajn; Eric Winsor; Dan Braun; Jacob Merizian; Kip Parker; Carlos Ramón Guevara; Beren Millidge; Gabriel Alfour; Connor Leahy (CoRR, 2022)

Circumventing interpretability: How to defeat mind-readers. - Lee Sharkey (CoRR, 2022)

A technical note on bilinear layers for interpretability. - Lee Sharkey (CoRR, 2023)

Black-Box Access is Insufficient for Rigorous AI Audits. - Stephen Casper; Carson Ezell; Charlotte Siegmann; Noam Kolt; Taylor Lynn Curtis; Benjamin Bucknall; Andreas A. Haupt; Kevin Wei; Jérémy Scheurer; Marius Hobbhahn; Lee Sharkey; Satyapriya Krishna; et al. (FAccT, 2024)

Sparse Autoencoders Find Highly Interpretable Features in Language Models. - Robert Huben; Hoagy Cunningham; Logan Riggs Smith; Aidan Ewart; Lee Sharkey (ICLR, 2024)

Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning. - Dan Braun; Jordan Taylor; Nicholas Goldowsky-Dill; Lee Sharkey (NeurIPS, 2024)

Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs. - Kola Ayonrinde; Michael T. Pearce; Lee Sharkey (CoRR, 2024)

Sparse Autoencoders Do Not Find Canonical Units of Analysis. - Patrick Leask; Bart Bussmann; Michael T. Pearce; Joseph Isaac Bloom; Curt Tigges; Noura Al Moubayed; Lee Sharkey; Neel Nanda (ICLR, 2025)

Bilinear MLPs enable weight-based mechanistic interpretability. - Michael T. Pearce; Thomas Dooms; Alice Rigg; José Oramas; Lee Sharkey (ICLR, 2025)

Interpretability in Parameter Space: Minimizing Mechanistic Description Length with Attribution-based Parameter Decomposition. - Dan Braun; Lucius Bushnaq; Stefan Heimersheim; Jake Mendel; Lee Sharkey (CoRR, 2025)

Open Problems in Mechanistic Interpretability. - Lee Sharkey; Bilal Chughtai; Joshua Batson; Jack Lindsey; Jeff Wu 003; Lucius Bushnaq; Nicholas Goldowsky-Dill; Stefan Heimersheim; Alejandro Ortega; Joseph Isaac Bloom; Stella Biderman; Adrià Garriga-Alonso; et al. (CoRR, 2025)

Identifying Sparsely Active Circuits Through Local Loss Landscape Decomposition. - Brianna Chrisman; Lucius Bushnaq; Lee Sharkey (CoRR, 2025)

AI Behind Closed Doors: a Primer on The Governance of Internal Deployment. - Charlotte Stix; Matteo Pistillo; Girish Sastry; Marius Hobbhahn; Alejandro Ortega; Mikita Balesni; Annika Hallensleben; Nix Goldowsky-Dill; Lee Sharkey (CoRR, 2025)

Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video. - Sonia Joseph; Praneet Suresh; Lorenz Hufe; Edward Stevinson; Robert Graham; Yash Vadi; Danilo Bzdok; Sebastian Lapuschkin; Lee Sharkey; Blake Aaron Richards (CoRR, 2025)

Stochastic Parameter Decomposition. - Lucius Bushnaq; Dan Braun; Lee Sharkey (CoRR, 2025)