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A governance framework for algorithmic accountability and transparency

Koene, Ansgar; Clifton, Chris; Hatada, Yohko; Webb, Helena; Richardson, Rashida

Authors

Chris Clifton

Yohko Hatada

Helena Webb

Rashida Richardson



Abstract

Algorithmic systems are increasingly being used as part of decision-making processes in both the public and private sectors, with potentially significant consequences for individuals, organisations and societies as a whole. Algorithmic systems in this context refer to the combination of algorithms, data and the interface process that together determine the outcomes that affect end users. Many types of decisions can be made faster and more efficiently using algorithms. A significant factor in the adoption of algorithmic systems for decision-making is their capacity to process large amounts of varied data sets (i.e. big data), which can be paired with machine learning methods in order to infer statistical models directly from the data. The same properties of scale, complexity and autonomous model inference however are linked to increasing concerns that many of these systems are opaque to the people affected by their use and lack clear explanations for the decisions they make. This lack of transparency risks undermining meaningful scrutiny and accountability, which is a significant concern when these systems are applied as part of decision-making processes that can have a considerable impact on people's human rights (e.g. critical safety decisions in autonomous vehicles; allocation of health and social service resources, etc.). This study develops policy options for the governance of algorithmic transparency and accountability, based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems. Based on a review and analysis of existing proposals for governance of algorithmic systems, a set of four policy options are proposed, each of which addresses a different aspect of algorithmic transparency and accountability: 1. awareness raising: education, watchdogs and whistleblowers; 2. accountability in public-sector use of algorithmic decision-making; 3. regulatory oversight and legal liability; and 4. global coordination for algorithmic governance.

Citation

Koene, A., Clifton, C., Hatada, Y., Webb, H., & Richardson, R. (2019). A governance framework for algorithmic accountability and transparency. Brussels: European Parliamentary Research Service

Report Type Policy Document
Acceptance Date Apr 4, 2019
Online Publication Date Apr 4, 2019
Publication Date Jun 12, 2019
Deposit Date Feb 18, 2020
Publicly Available Date Feb 28, 2020
Pages 124
DOI https://doi.org/10.2861/59990
Public URL https://nottingham-repository.worktribe.com/output/3979928
Publisher URL https://www.europarl.europa.eu/stoa/en/home/highlights
Related Public URLs https://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_STU(2019)624262
Additional Information Brussels © European Union, 2019

Written at the request of the Panel for the Future of Science and
Technology (STOA) and managed by the Scientific Foresight Unit, within the Directorate-General for Parliamentary Research Services (EPRS) of the Secretariat of the European Parliament.