32 citations · 36 across the 5 of their papers we have counts for
6 papers
MonitrLLM: A Community-Centered Evaluation Infrastructure for Large Language Models
Victor Ojewale, Ro Encarnación, Suresh Venkatasubramanian +1
Benchmark suites assess model capability on controlled tasks; large-scale conversation corpora capture naturalistic use without user feedback; and in-interface feedback mechanisms…
Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
Victor Ojewale, Suresh Venkatasubramanian
As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate…
Audit Trails for Accountability in Large Language Models
Victor Ojewale, Harini Suresh, Suresh Venkatasubramanian
Large language models (LLMs) are increasingly embedded in consequential decisions across healthcare, finance, employment, and public services. Yet accountability remains fragile be…
Multi-lingual Functional Evaluation for Large Language Models
Victor Ojewale, Inioluwa Deborah Raji, Suresh Venkatasubramanian
Multi-lingual competence in large language models is often evaluated via static data benchmarks such as Belebele, M-MMLU and M-GSM. However, these evaluations often fail to provide…
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Victor Ojewale, Ryan Steed, Briana Vecchione +2
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains inc…
AI auditing: The Broken Bus on the Road to AI Accountability
Abeba Birhane, Ryan Steed, Victor Ojewale +2
One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical n…