Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Nov 8:7:1426761.
doi: 10.3389/frai.2024.1426761. eCollection 2024.

Ethics dumping in artificial intelligence

Affiliations

Ethics dumping in artificial intelligence

Jean-Christophe Bélisle-Pipon et al. Front Artif Intell. .

Abstract

Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI's influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation-underscoring the need to examine AI's ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of "ethics dumping" in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem.

Keywords: AI ethics; AI governance; accountability; artificial intelligence; ethical guidelines; ethics dumping.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

    1. Akhai S. (2023). From black boxes to transparent machines: The quest for explainable AI. SSRN. doi: 10.2139/ssrn.4390887 - DOI
    1. Alami H., Lehoux P., Auclair Y., de Guise M., Gagnon M. P., Shaw J., et al. . (2020). Artificial intelligence and health technology assessment: anticipating a new level of complexity. J. Med. Internet Res. 22:e17707. doi: 10.2196/17707, PMID: - DOI - PMC - PubMed
    1. Algorithm Watch (2020) Automating Society Report 2020. Available at: https://automatingsociety.algorithmwatch.org
    1. Ananny M., Crawford K. (2018). Seeing without knowing: limitations of the transparency ideal and its application to algorithmic accountability. New Media Soc. 20, 973–989. doi: 10.1177/1461444816676645 - DOI
    1. Andanda P., Wathuta J., Leisinger K., Schroeder D. (2014) National and International Compliance Tools, a report for TRUST, Available at: https://trust-project.eu/wp-content/uploads/2017/02/TRUST-664771-Nationa...

LinkOut - more resources