What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability

By Maranke Wieringa. 2020

328 citations recorded by the source corpus.

As research on algorithms and their impact proliferates, so do calls for scrutiny/accountability of algorithms. A systematic review of the work that has been done in the field of 'algorithmic accountability' has so far been lacking. This contribution puts forth such a systematic review, following the PRISMA statement. 242 English articles from the period 2008 up to and including 2018 were collected and extracted from Web of Science and SCOPUS, using a recursive query design coupled with computational methods. The 242 articles were prioritized and ordered using affinity mapping, resulting in 93 'core articles' which are presented in this contribution. The recursive search strategy made it possible to look beyond the term 'algorithmic accountability'. That is, the query also included terms closely connected to the theme (e.g. ethics and AI, regulation of algorithms). This approach allows for a perspective not just from critical algorithm studies, but an interdisciplinary overview drawing on material from data studies to law, and from computer science to governance studies. To structure the material, Bovens's widely accepted definition of accountability serves as a focal point. The material is analyzed on the five points Bovens identified as integral to accountability: its arguments on (1) the actor, (2) the forum, (3) the relationship between the two, (3) the content and criteria of the account, and finally (5) the consequences which may result from the account. The review makes three contributions. First, an integration of accountability theory in the algorithmic accountability discussion. Second, a cross-sectoral overview of the that same discussion viewed in light of accountability theory which pays extra attention to accountability risks in algorithmic systems. Lastly, it provides a definition of algorithmic accountability based on accountability theory and algorithmic accountability literature.

Subfields: Artificial Intelligence, Information Systems, Safety Research, Safety, Robustness & Interpretability

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Citations in the corpus

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “made answerable to a forum: information-sharing, deliberation and discussion, and the imposition of consequences [214].”
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020, methodology): “ are all directly applicable to datasets. Table 2 summarizes how her concerns relate to datasets, as well as the mitigating proposals which we will elaborate below.1 Our analysis and proposals follow [145] and [83] in using temporal situatedness as a key organizing factor. At the highest level, what”
Algorithmic bias: review, synthesis, and future research directions (2021, background): “First, their focuses are either too broad, such as data science ethics (Saltz & Dewar, 2019) and algorithmic accountability (Wieringa, 2020), or too narrow, including AI for employee management (Robert et al., 2020) and facial analysis systems (Khalil et al., 2020).”
Six Human-Centered Artificial Intelligence Grand Challenges (2023, neutral): “…foundation of AI-enabled systems, are in effect socio-technical systems embedded within and across cultures with different meanings and points of interaction (Seaver, 2017) and self-regulation as a way to satisfy a moral imperative over a formal legal one is insufficient (Wieringa, 2020).”
Trustworthy Artificial Intelligence: A Review (2022, background): “Wieringa [211] defines accountability as a networked account where responsibility is distributed among stakeholders and applied at various stages of the AI lifecycle.”
Harms from Increasingly Agentic Algorithmic Systems (2023, background): “” And in a systematic review on algorithmic accountability, Wieringa [196] defines algorithms as “basically instructions fed to a computer”.”
Towards Transparency by Design for Artificial Intelligence (2020, background): “However, accountability is also broader than transparency, as transparency merely refers to the “transparent workings of a system” and does not say “why this system was deemed ‘good enough’ at decision making” (Wieringa 2020, p. 4).”