ArticleDigital society : ethics, socio-legal and governance of digital technology2022
AI Ethics, Ethics Washing, and the Need to Politicize Data Ethics.
Article in Digital society : ethics, socio-legal and governance of digital technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- From principles to binding rules: A one health governance readiness analysis of Saudi Arabia's AI health policy against global regulatory benchmarks.One health (Amsterdam, Netherlands) · 2026Article
- AI ethics through a decolonial lens: what does AI ethics look like if we take seriously the push to decolonise it?AI & society · 2026Article
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics.Human genetics · 2025Review
- Ethical sourcing in the context of health data supply chain management: a value sensitive design approach.JAMIA open · 2025Article
- Towards a digital planetary health perspective: generative AI and the digital determinants of health.Health promotion international · 2025Article
- The Political Economy of Digital Health Equity: Structural Analysis.Journal of medical Internet research · 2024Article
- Reframing data ethics in research methods education: a pathway to critical data literacy.International journal of educational technology in higher education · 2023Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many commercial actors in the tech sector publish ethics guidelines as a means to 'wash away' concerns raised about their policies. For some academics, this phenomenon is reason to replace ethics with other tools and methods in an attempt to make sure that the tech sector does not cross any moral Rubicons. Others warn against the tendency to reduce a criticism of 'ethics washing' into one of ethics simpliciter. In this essay, I argue firstly that the dominant focus on principles, dilemmas, and theory in conventional ethical theories and practices could be an explanation of it lacking resistance to abuse by dominant actors, and hence its rather disappointing capacity to stop, redirect, or at least slow down big tech's course. Secondly, drawing from research on casuistry and political philosopher Raymond Geuss, this essay will make a case for a question, rather than theory or principle-based ethical data practice. The emphasis of this approach is placed on the acquisition of a thorough understanding of a social-political phenomenon like tech development. This approach should be replenished with one extra component to the picture of the repoliticized data ethics drawn so far: the importance of 'exemplars,' or stories. Precisely the fact that one should acquire an in-depth understanding of the problem in practice will also allow one to look in the past, present, or future for similar and comparable stories from which one can learn.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.