Fair Transparent and Accountable Algorithmic Decision making Processes

The Premise, the Proposed Solutions, and the Open Challenges The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development.

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Additional Info
Field Value
Creator Lepri, Bruno
Creator Oliver, Nuria
Creator Letouzé, Emmanuel
Creator Pentland, Alex
Creator Vink, Patrick
DOI 10.1007/s13347-017-0279-x
Group Social Impact of AI and explainable ML
Publisher Springer
Source Philosophy and Technology
Thematic Cluster Privacy Enhancing Technology [PET]
system:type JournalArticle
Management Info
Field Value
Version 1
Last Updated 8 September 2023, 18:12 (CEST)
Created 5 April 2021, 18:43 (CEST)