Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability

By Mike Ananny, Kate Crawford. 2018

1,462 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Safety Research, AI Ethics, Fairness & Society

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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): “Ananny and Crawford (2018) comment that resorting to full algorithmic transparency may not be an adequate means to address their ethical dimensions; opening up the black-box would not suffice to disclose their modus operandi. Moreover, developers of algorithm may not be capable of explaining in plai”
Seven HCI Grand Challenges (2019, background): “On the other hand, the ideal of complex systems ’ transparency faces technical and temporal limitations, as well as major short-comings (Ananny & Crawford, 2018).”
Algorithms as culture: Some tactics for the ethnography of algorithmic systems (2017, background): “Transparency is not a revealing of what was always there, but a methodical reconﬁgur-ation of the social scene that changes it toward particular ends (see e.g. Ananny and Crawford, 2016; Strathern, 2000; Ziewitz, 2015).”
Algorithmic content moderation: Technical and political challenges in the automation of platform governance (2020, background): “This is not to say that transparency is any sort of panacea, either in general or when it comes to algorithmic systems (Ananny and Crawford, 2018; Gorwa and Garton Ash, 2019), but minimum standards of decisional transparency are essential to allow both ordinary users and critical experts to…”
The Role of Cooperation in Responsible AI Development (2019, background): “Full transparency is problematic as an ideal to strive for, in that it is neither necessary nor sufficient for achieving accountability in all cases (Desai and Kroll, 2017; Ananny and Crawford, 2018).”
Truth from the machine: artificial intelligence and the materialization of identity (2021, background): “In practice, the conclusions of these systems resist human interpretation ‘ even for those with specialized training, even for computer scientists ’ (Burrell 2016; Ananny and Crawford 2018).”
Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes (2022, neutral): “affected by the limitations of transparency [11].”