Publication

Workshop Report: The Governance of Decision-Making Algorithms

Kujtesë Bejtullahu Michalopoulos
2018
Reports

Abstract

This report presents key interdisciplinary insights from IRGC’s expert workshop on the governance of decision-making algorithms, with particular focus on automated decisions based on learning algorithms (DMLAs). It highlights, among others, the need to improve the accuracy of DMLAs; embed good standards, principles or governance rules into their very design and functioning; address algorithmic bias with unfair social consequences; better differentiate if and when humans are in control, or unable to take control back; govern distinct risks arising by application or domain (e.g. DMLAs in healthcare/automated driving/predictive policy-making/insurance, etc.); better state key benchmarks against which DMLAs’ performance must be calibrated; better define accountability, responsibility and liabilities; and emphasize the centrality of digital trust.

Official source

https://infoscience.epfl.ch/entities/publication/17c0debc-5086-4ab1-a517-0f99ba5192aa

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Kujtesë Bejtullahu Michalopoulos
2018
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/17c0debc-5086-4ab1-a517-0f99ba5192aa

About this result

Related concepts (15)

Automated decision-making

Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics.

Algorithmic bias

Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results and social media platforms.

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

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