1 citations · 3 across the 11 of their papers we have counts for
13 papers
FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy
Sidharth Pulipaka, Ruta Binkyte, Ivaxi Sheth +1
Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing with false claims, offering u…
Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best
Kevin Baum, Rūta Binkytė, Felix Jahn
AI agents sometimes act aligned when they infer they are being tested, and differently when not. We argue this is not an anomaly but what current training regimes are structured to…
Last Translation Benchmark
Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthines…
Inspectable AI for Science: A Research Object Approach to Generative AI Governance
Ruta Binkyte, Sharif Abuaddba, Chamikara Mahawaga +3
This paper introduces AI as a Research Object (AI-RO), a paradigm for governing the use of generative AI in scientific research. Instead of debating whether AI is an author or mere…
Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews
Sai Suresh Macharla Vasu, Ivaxi Sheth, Hui-Po Wang +2
The adoption of large language models (LLMs) is transforming the peer review process, from assisting reviewers in writing detailed evaluations to generating entire reviews automati…
Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Framework
Ruta Binkyte
As large language models (LLMs) are increasingly used in multi-agent systems, questions of fairness should extend beyond resource distribution and procedural design to include the…
On the Origins of Sampling Bias: Implications on Fairness Measurement and Mitigation
Sami Zhioua, Ruta Binkyte, Ayoub Ouni +1
Accurately measuring discrimination is crucial to faithfully assessing fairness of trained machine learning (ML) models. Any bias in measuring discrimination leads to either amplif…
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal method…
Safety Must Precede the Deployment of Open-Ended AI
Ivaxi Sheth, Jan Wehner, Sahar Abdelnabi +2
AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. Within this land…
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Tejumade Afonja, Ivaxi Sheth, Ruta Binkyte +4
Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understandin…
BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables
Ruta Binkyte, Daniele Gorla, Catuscia Palamidessi
We consider the problem of unfair discrimination between two groups and propose a pre-processing method to achieve fairness. Corrective methods like statistical parity usually lead…
On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis
Ruta Binkyte, Ljupcho Grozdanovski, Sami Zhioua
As Artificial Intelligence (AI) increasingly influences decisions in critical societal sectors, understanding and establishing causality becomes essential for evaluating the fairne…