Robert Mahari

Robert Mahari

Boston, Massachusetts

About me

I'm pursuing a joint JD-PhD degree at Harvard Law School and the MIT Media Lab. My work focuses on Computational Law: leveraging computation to analyze, improve, and extend the study and practice of law.

My research aims to bridge the gap between technology and law to surface new quantitative insights into jurisprudential systems and to identify computationally-enabled approaches to the practice of law. By formalizing research around computational law, I aim to deepen our understanding of legal processes, while building tools that improve legal practice, expand access to justice, and increase judicial efficacy. I also study the technological transformation of the legal profession and its effect on how organizations manage legal services and risks. To this end, I collaborate with private and public entities around the world to prototype practice-oriented computational legal solutions, deploying and studying computational law in the real world.


Art and Science of Generative AI

Exploring the role of human creativity in generative AI.

Data Provenance Project

A large-scale audit of AI training data including data licenses, sources, and provenance.

Uncovering the universal dynamics of citation systems

Universal citation dynamics across knowledge systems and a community-based citation model that better predicts future citation trajectories.

Economics of Litigation Finance

Grounded in empirical data, we explore how litigation finance affects the way litgants and law firms behave.

The Law and NLP: Bridging Disciplinary Disconnects

The slow uptake of NLP in legal practice may be exacerbated by a disconnect between the needs of the legal community and the focus of NLP researchers.

LePaRD: A Large-Scale Dataset of Judges Citing Precedents

A massive collection of U.S. federal judicial citations to precedent in context that facilitates work on legal passage prediction, a challenging practice oriented legal retrieval and reasoning task.

Legal Dynamism

Advances in computational law allow us to re-examine and update regulation in real time, recasting laws as dynamic systems rather than as static rules.

Transparency by Design for Large Language Models

A proposal for an LLM-powered data trust that allows modification of underlying data to increase privacy and transparency.

Justice in a Vacuum?

Is a truly decentralized form of dispute resolution that relies entirely on network effects to enforce judgments feasible?

AML by Design

Designing central bank digital currencies to stifle money laundering.

Time for a New Antitrust Era

Responding to unique challenges of a data-driven marketplace by expanding monopoly power to include data ownership and enhancing premerger review with network science.

Co-creation and ownership for AI radio

Blending AI-generated music with listener ratings and personalized recommendations enables large-scale human curation and raises questions about the ownership of AI-created content.

AutoLaw: Augmented Legal Reasoning

Introducing Legal Precedent Prediction (LPP), the task of predicting relevant passages from precedential court decisions given the context of a legal argument.

zkTax: zero-knowledge tax disclosures

Zero-knowledge tax disclosure system that allows individuals and organizations to make provable claims about tax data without revealing additional informatio.


The Future of Law: AI Powered Justice? | TEDxBoston

Inaugural Speech | University of Warsaw

Digital Enterprise Panel | MIT Sloan CIO Symposium

Overview of GenAI+Law Research at MIT |

AI, Creative Ownership and Attribution | DigitalFUTURES

The Law and NLP | NLLP Workshop @ EMNLP

Future for digital universal wallets | Linux Foundation

Data-driven Justice | UN World Data Forum

Computational Insights from Legal Data |

A New Antitrust Era | Stanford Computational Antitrust |

CBDC & Privacy Technology | FNA