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Knowledge Hub

Accountability

Algorithmic systems impact work. These impacts are the result of human decisions taken in design and deployment of the systems. Resources in this section consider how human decisions determine such impacts; mechanisms for making these impacts visible; and tools to mitigate them such as bias assessment, impact assessment, and auditing. This shelf is curated by IFOW with the help of our Research Fellows. We are keen to critically engage with a wide range of perspectives on the future of work. These resources do not necessarily reflect the views of IFOW.

Accountability

Mapping global AI governance: a nascent regime in a fragmented landscape

This paper reviews emerging AI governance regimes across the world.
Accountability

Technology managing people The worker experience

This overview of the experience of AI at work also provides a helpful review of intersecting legal regimes for accountability.
Accountability

Data Protection Impact Assessments: A Guide

Guidance developed by Prospect union, in partnership with IFOW, for union representatives on the DPIA process.
Accountability

AI Law Hub

This hub features varied resouces on the future of law surrounding AI, curated by Robin Allen QC.
Accountability

Demystifying the Draft EU Artificial Intelligence Act

This paper reviews the contents and probable efficacy of proposed EU AI regulation.
Accountability

Algorithms and Economic Justice: A Taxonomy of Harms and a Path Forward

To regulate in ways which protect workers, we must understand harms to them. This paper reviews economic and wider harms caused by algorithms at work.
Accountability

Meeting new challenges to equality and non-discrimination

This report by Robin Allen QC and Dee Masters to the European Commission proposes new measures to ensure all can benefit from AI without discrimination.
Accountability

Big Brother and Big Boss: Are New Digital Rights Needed?

We consider whether new rights may be needed to address new challenges at work.
Accountability

Making algorithms safe for workers: occupational risks associated with work managed by artificial intelligence

This paper considers the impacts of algorithms at work on worker wellbeing.
Accountability

Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability

Can “black boxes’ ever be opened, and if so, would that ever be sufficient?
Accountability

Decision Provenance: Harnessing Data Flow for Accountable Systems

This paper argues we need to explose the 'decision pipelines' that create algorithmic systems in order for them to be accountable.
Accountability

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved

This paper considers the ways in which auditing tools can detect bias in systems.

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