Demystifying Legalese: An Automated Approach for Summarizing and Analyzing Overlaps in Privacy Policies and Terms of Service

no code implementations • 17 Apr 2024 • Shikha Soneji, Mitchell Hoesing, Sujay Koujalgi, Jonathan Dodge

The complexities of legalese in terms and policy documents can bind individuals to contracts they do not fully comprehend, potentially leading to uninformed data sharing.

Experiments with Encoding Structured Data for Neural Networks

no code implementations • 15 Feb 2024 • Sujay Nagesh Koujalgi, Jonathan Dodge

The project's aim is to create an AI agent capable of selecting good actions in a game-playing domain called Battlespace.

Navigate

Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations

no code implementations • 28 Sep 2021 • Kin-Ho Lam, Zhengxian Lin, Jed Irvine, Jonathan Dodge, Zeyad T Shureih, Roli Khanna, Minsuk Kahng, Alan Fern

We describe a user interface and case study, where a small group of AI experts and developers attempt to identify reasoning flaws due to inaccurate agent learning.

Decision Making Reinforcement Learning (RL)

Explaining Reinforcement Learning to Mere Mortals: An Empirical Study

no code implementations • 22 Mar 2019 • Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett

We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents.

reinforcement-learning Reinforcement Learning (RL)

Visualizing and Understanding Atari Agents

3 code implementations • ICML 2018 • Sam Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern

While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so.

Reinforcement Learning (RL)