Data Trusts: Ethics, Architecture and Governance for Trustworthy Data Stewardship

Abstract

In their report on the development of the UK AI industry, Wendy Hall and Jerome Pesenti recommend the establishment of data trusts, “proven and trusted frameworks and agreements” that will “ensure exchanges [of data] are secure and mutually beneficial” by promoting trust in the use of data for AI. This paper defends the following thesis: A data trust works within the law to provide ethical, architectural and governance support for trustworthy data processing. Data trusts are therefore both constraining and liberating. They constrain: they respect current law, so they cannot render currently illegal actions legal. They are intended to increase trust, and so they will typically act as further constraints on data processors, adding the constraints of trustworthiness to those of law. Yet they also liberate: if data processors are perceived as trustworthy, they will get improved access to data. The paper addresses the areas of: trust and trustworthiness; ethics; architecture; legal status.

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