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Coders at the bar

Markets and the law as computer scientists see them, in their own words. 13 publications, from 2022 to 2025.

All 13 publications

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2025

  1. Understanding Digital Sovereignty in the Age of Artificial Intelligence

    William Lehr, Volker Stocker and Jason Whalley

    EU Digital Sovereignty has emerged as a key policy topic amid growing geopolitical tensions fuelled by an AI arms race in which corporate investment and government support appear mutually reinforcing.

  2. Principal-Agent Dynamics and Digital (Platform) Economics in the Age of Agentic AI

    Volker Stocker and William Lehr

    This article applies the principal–agent framework to the use of autonomous AI systems in digital markets. It examines the challenge of aligning AI agents with the interests of end-users, given that many systems may also reflect…

  3. Implementing the European AI Act: Balancing Horizontal Consistency with Sector-Specific Requirements

    Daniel Schnurr

    The European Union’s AI Act applies across a broad range of domains and use cases, aiming to promote horizontal consistency and prevent the sectoral fragmentation of AI governance.

  4. The Growing Complexity of Digital Economies over the GenAI Waterfall: Challenges and Policy Implications

    Volker Stocker and William Lehr

    The GenAI genie is out of the bottle. AI, and its vanguard GenAI, is a change agent that profoundly impacts the global transition to a digital economy.

2024

  1. Bridging Disciplinary Disconnects: The Role of Legal Experts in Legal NLP

    Robert Mahari

    The potential of Natural Language Processing (NLP) to transform the legal industry is increasingly undeniable. NLP can be used to reimagine legal search, improve legal reasoning and writing, answer legal questions, or extract…

  2. AI Regulation and 6G: The Measurement Ecosystem Challenge

    William Lehr and Volker Stocker

    Artificial intelligence (AI) and its economic impacts have been emerging over decades. Even though the public launch of OpenAI’s ChatGPT in November 2022 has caused a frenzy about generative AI that arguably prompted a surge in…

  3. Discit ergo est: Training Data Provenance And Fair Use

    Robert Mahari and Shayne Longpre

    AI is the product of its training data. Public discussion of this subject frequently centers on massive corpora of text and images that have been scraped from the web, often without consent from the creators of these works.

2023

  1. Competition Between AI Foundation Models

    Thibault Schrepel and Alex ‘Sandy’ Pentland

    This short article serves as an introduction to Thibault Schrepel and Sandy Pentland’s latest working paper, “Competition between AI Foundation Models: Dynamics and Policy Recommendations” (open-access)…

  2. Transparency by Design for Large Language Models

    Alex ‘Sandy’ Pentland, Robert Mahari and Tobin South

    Large Language Models – like ChatGPT, Bard, and Claude – are akin to modern day oracles: they provide impressively useful outputs without revealing their reasoning.

  3. Justice in a Vacuum?

    Alex ‘Sandy’ Pentland, Robert Mahari and Tobin South

    From the Palaeolithic era until the mid-1800s, tally sticks represented an international system for recording debts. Debtors and creditors would break a piece of wood or bone in half, and the unique fracture memorialized the…

2022

  1. Near-Decomposability as A Regenerative Process in Complex Social Phenomena

    Anamaria Berea

    In complex systems, particularly when we study scalability, we address phenomena from “cells to societies”. The cell is an “ecology” of information (DNA) and chemical substances (also DNA) that self-organize in a biological…

  2. Legal Dynamism

    Alex ‘Sandy’ Pentland and Robert Mahari

    Shortly after the start of the French Revolution, Thomas Jefferson wrote a now famous letter to James Madison. He argued that no society could make a perpetual constitution, or indeed a perpetual law, that binds future…

  3. Economics and the Search for Life

    Anamaria Berea

    Economics was informally named the “dismal science” in the 19 century, in order to denote the “dismal” predictions some economists of the era had about the future of humanity (e.g., population overgrowth).

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