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Keegan Hines

3 accepted papers

2026

Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness

ICLR 2026poster

We **color-coded** the added changes to the **paper** and **Appendix** for the comfort of our reviewers. Computer-Use Agents (CUAs) are an increasingly deployed class of agents that take actions on GUIs to accomplish user goals. In this paper, we show that CUAs consistently exhibit *Blind Goal-Dir…

Cited by 0SourceScholar
2025

MULTIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities

EMNLP 2025

The emerging capabilities of large language models (LLMs) have sparked concerns about their immediate potential for harmful misuse. The core approach to mitigate these concerns is the detection of harmful queries to the model. Current detection approaches are fallible, and are particularly susceptib

2022

Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models

AAAI 2022technical

Explainable machine learning (ML) has gained traction in recent years due to the increasing adoption of ML-based systems in many sectors. Algorithmic Recourses (ARs) provide "what if" feedback of the form "if an input datapoint were x' instead of x, then an ML-based system's output would be y' inste…