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Robert Mahari

7 accepted papers

2025

Bridging the Data Provenance Gap Across Text, Speech, and Video

ICLR 2025poster

Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities --- pop…

Cited by 1SourcePDFScholar
2025

Position: AI Agents Need Authenticated Delegation

ICML 2025oral

The rapid deployment of autonomous AI agents creates urgent challenges in the areas of authorization, accountability, and access control in task delegation. This position paper argues that authenticated and auditable delegation of authority to AI agents is a critical component of mitigating practica…

Cited by 0SourcePDFScholar
2024

Consent in Crisis: The Rapid Decline of the AI Data Commons

NeurIPS 2024poster

General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training…

Cited by 36SourceScholar
2024

LePaRD: A Large-Scale Dataset of Judicial Citations to Precedent

ACL 2024long

We present the Legal Passage Retrieval Dataset, LePaRD. LePaRD contains millions of examples of U.S. federal judges citing precedent in context. The dataset aims to facilitate work on legal passage retrieval, a challenging practice-oriented legal retrieval and reasoning task. Legal passage retrieval…

2024

Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling

ACL 2024long

Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large…

2024

Position: Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?

ICML 2024spotlight

New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in tracing authenticity, verifying consent, preserving privacy, addressing representation and bias, re…

Cited by 3SourcePDFScholar
2023

The Law and NLP: Bridging Disciplinary Disconnects

EMNLP 2023short findings

Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP). At the same time, the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. I…

Cited by 0SourceScholar