Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?

By P. D. de Laat. 2017

222 citations recorded by the source corpus.

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves (“gaming the system” in particular), the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante.

Subfields: Artificial Intelligence, Safety Research

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Citations in the corpus

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “In this setting, seeking algorithm transparency would not be harmful for their developers as scrutiny would be delegated to entrusted intermediate parties, to take place behind closed doors (de Laat, 2018).”
Algorithms, Platforms, and Ethnic Bias: An Integrative Essay Forthcoming in Phylon: The Clark Atlanta University Review of Race and Culture (2018, unknown): “In an increasing number of cases, even the algorithm’s creators may not be able to explain the computational outcomes, and thus mandating “transparency” is unlikely to prevent untoward decisions (De Laat, 2017).”
Towards Transparency by Design for Artificial Intelligence (2020, background): “Despite these positive aspects and functions of transparency, an increasing body of literature has pointed to issues associated with the concept (Ananny and Crawford 2018; De Laat 2018; Tielenburg 2018).”
Trustworthy Artificial Intelligence: A Review (2022, background): “Because of these side effects, they proposed that total transparency for accountability should not be provided to the general public but only to the oversight agencies the public should trust [52].”
The Bouncer Problem: Challenges to Remote Explainability (2019, background): “Modern decision-making driven by black-box systems now impacts a signiﬁcant share of our lives [9, 29].”
Transparency in artificial intelligence (2020, background): “It is a concept often framed with positive values, portrayed by ‘open data’, ‘open source’, ‘open code’ and ‘open access’ (Larsson, 2017, p. 215-220), as well as ‘open science’ (see Fecher & Friesike, 2014). Transparency in the sense of ‘open government’ comes with a political framing of empowerment”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, unknown): “27 academic articles [8, 9, 17, 20, 22, 28, 35, 48, 52, 58, 61, 65, 86, 90, 93, 98, 106, 110, 111, 120, 124, 135, 139–141, 143, 144] which were found to be relevant to the topic prior to the start of the systematic review were assessed for their keywords.”