Optimising Human-AI Collaboration by Learning Convincing Explanations

13 November 2023

Papers citing "Optimising Human-AI Collaboration by Learning Convincing Explanations"

21 / 21 papers shown

Title
Inverse Decision Modeling: Learning Interpretable Representations of Behavior Daniel Jarrett Alihan Huyuk M. Schaar AI4CE 55 28 0 28 Oct 2023
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes Tennison Liu Alex J. Chan B. V. Breugel M. Schaar FaML 41 2 0 11 Nov 2022
Estimating and Penalizing Induced Preference Shifts in Recommender Systems Micah Carroll Anca Dragan Stuart J. Russell Dylan Hadfield-Menell OffRL 84 43 0 25 Apr 2022
POETREE: Interpretable Policy Learning with Adaptive Decision Trees Alizée Pace Alex J. Chan M. Schaar OffRL 37 17 0 15 Mar 2022
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies Alex J. Chan Alicia Curth M. Schaar CML OffRL 36 8 0 14 Mar 2022
Uncalibrated Models Can Improve Human-AI Collaboration Kailas Vodrahalli Tobias Gerstenberg James Zou HAI 75 30 0 12 Feb 2022
Explaining Latent Representations with a Corpus of Examples Jonathan Crabbé Zhaozhi Qian F. Imrie M. Schaar FAtt 40 38 0 28 Oct 2021
Inverse Contextual Bandits: Learning How Behavior Evolves over Time Alihan Huyuk Daniel Jarrett M. Schaar CML OffRL 49 11 0 13 Jul 2021
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation Alex J. Chan Ioana Bica Alihan Huyuk Daniel Jarrett M. Schaar 63 14 0 08 Jun 2021
Scalable Bayesian Inverse Reinforcement Learning Alex J. Chan M. Schaar OffRL BDL 70 67 0 12 Feb 2021
Sequential Explanations with Mental Model-Based Policies A. Yeung Shalmali Joshi Joseph Jay Williams Frank Rudzicz FAtt LRM 56 15 0 17 Jul 2020
Consistent Estimators for Learning to Defer to an Expert Hussein Mozannar David Sontag 49 201 0 02 Jun 2020
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems O. Lahav Nicholas Mastronarde M. Schaar 27 30 0 27 Nov 2018
Interpreting Neural Networks With Nearest Neighbors Eric Wallace Shi Feng Jordan L. Boyd-Graber AAML FAtt MILM 100 54 0 08 Sep 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 83 1,855 0 31 May 2018
AI safety via debate G. Irving Paul Christiano Dario Amodei 234 217 0 02 May 2018
MNL-Bandit: A Dynamic Learning Approach to Assortment Selection Shipra Agrawal Vashist Avadhanula Vineet Goyal A. Zeevi 125 158 0 13 Jun 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 184 3,865 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 175 5,968 0 04 Mar 2017
Cascading Bandits: Learning to Rank in the Cascade Model Branislav Kveton Csaba Szepesvári Zheng Wen Azin Ashkan 168 284 0 10 Feb 2015
An MDP-based Recommender System Guy Shani Ronen I. Brafman David Heckerman LRM 101 970 0 12 Dec 2012