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The Ethical Web and Data Architecture in the Age of AI

1st June 2021 to 31st May 2024

Ethical Web and Data Infrastructure in the Age of AI (EWADA) is an ambitious 3-year programme that is funded by the Oxford Martin School and aims to reform the concentration of power on the World Wide Web by developing and deploying new forms of technical and legal infrastructure.


Thirty years ago, the World Wide Web launched as an open, common, universal infrastructure that anyone with a computer and a modem could use to communicate, publish and access information. In recent years, however, it has radically diverged from the values upon which it was founded, and it is now dominated by a number of platform companies, whose business models and services generate huge profits. To reform the centralised Web and create “architectures for autonomy”, EWADA aims to investigate novel re-decentralisation architectures and privacy-preserving AI methods and define new institutional and legal constructs that are required in this transformation.


Our work is organised around four themes:

  • Data Autonomy We wish to ensure individuals can control, manage, maintain and use personal data by 1) empowering people with their data; and 2) enabling people to understand and control how, when, and for what purposes data are shared.
  • Data Privacy We will develop a range of privacy-preserving machine learning (PPML) methods that will mean AI training can be decentralised, enabling data to be processed locally, rather than in large, centralised data centres. Processing data in a way that maintains user privacy, prevents subversion of the results whilst still extracting wider collective value.
  • Accountability We will develop methods to assess whether AI or algorithmic decision making is fair, equitable and complies with regulatory requirements, and seek ways to promote a right to explanation of the internal decision-making process of algorithms.
  • Data Sharing We will explore new institutional and legal constructs within which to hold data or algorithmic outputs. We will develop new forms of holding and sharing data through concepts such as data trusts, mutuals or cooperatives. We will also develop architectures that could allow data to be shared in more flexible and innovative ways, respecting individual autonomy while generating wider societal benefits.

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Selected Publications

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Principal Investigator


Reuben Binns
Associate Professor, Director for Academic Environment
Max Van Kleek
Associate Professor

Associate members

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