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Animal Ethics Biosafety Human Research Ethics Research Integrity

Principles alone cannot guarantee ethical AI (Papers: Brent Mittelstadt | November 2020)

Posted by Dr Gary Allen in Human Research Ethics on December 16, 2020
Keywords: Data management, Good practice, Human research ethics, Institutional responsibilities, Researcher responsibilities, Respect for persons

The Linked Original Item was Posted On November, 4 2019

A compiling digital face, representing AI

The use of principles in human research ethics is well established (though which principles and how they’re used in different disciplines and cultural settings has been debated), but do they work for AI? This open access paper reflects on this question. #SpoilerAlert it suggests not.

Abstract
Artificial intelligence (AI) ethics is now a global topic of discussion in academic and policy circles. At least 84 public–private initiatives have produced statements describing high-level principles, values and other tenets to guide the ethical development, deployment and governance of AI. According to recent meta-analyses, AI ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite the initial credibility granted to a principled approach to AI ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance. Significant differences exist between medicine and AI development that suggest a principled approach for the latter may not enjoy success comparable to the former. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional accountability mechanisms. These differences suggest we should not yet celebrate consensus around high-level principles that hide deep political and normative disagreement.

Mittelstadt, B. (2018) Principles alone cannot guarantee ethical AI. Nature Machine Intelligence 1, 501–507. https://doi.org/10.1038/s42256-019-0114-4
Publisher (Open Access): https://www.nature.com/articles/s42256-019-0114-4

 

Related Reading

Looking before we leap: expanding ethical review processes for AI and data science research. (Papers: Ada Lovelace Institute | December 2022)

Advancing ethics review practices in AI research (Papers: Madhulika Srikumar et. al. | December 2022)

(Australia) Factsheet: Research Governance (Resource: The Institute of Internal Auditors Australia | October 2020)

National Statement on Ethical Conduct in Human Research (2007) – Updated with new link to July 2018 update

‘Silicon Valley is ethically lost’: Google grapples with reaction to its new ‘horrifying’ and uncanny AI tech – Financial Post (Mark Bergen | May 2018)

Considering the ethics of big data research: A case of Twitter and ISIS/ISIL (Elizabeth Buchanan | December 2017)

Codes of Ethics for Economists: A Pluralist View (Papers: Sheila C Dow | 2013)

One size does not fit all: organisational diversity in New Zealand tertiary sector ethics committees (Papers: Martin Tolich et al 2015)

An Alternative Ethics? Justice and Care as Guiding Principles for Qualitative Research (Papers: Martyn Hammersley and Anna Traianou 2014)

Australian Council for International Development (ACFID) (2016) Principles and Guidelines for ethical research and evaluation in development

On Being Ethical in Geographical Research (Books: Iain Hay 2016)

Applying “Place” to Research Ethics and Cultural Competence/Humility Training (Papers: Dianne P Quigley 2016)

Office for the Australian Information Commissioner (OAIC)

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