Forbes Insight Team
Originally posted March 27, 2019
Here is an excerpt:
The traditional ethics oversight and compliance model has two major problems, whether it is used in biomedical research or in AI. First, a list of guiding principles—whether four or 40—just summarizes important ethical concerns without resolving the conflicts between them.
Say, for example, that the development of a life-saving AI diagnostic tool requires access to large sets of personal data. The principle of respecting autonomy—that is, respecting every individual’s rational, informed, and voluntary decision making about herself and her life—would demand consent for using that data. But the principle of beneficence—that is, doing good—would require that this tool be developed as quickly as possible to help those who are suffering, even if this means neglecting consent. Any board relying solely on these principles for guidance will inevitably face an ethical conflict, because no hierarchy ranks these principles.
Second, decisions handed down by these boards are problematic in themselves. Ethics boards are far removed from researchers, acting as all-powerful decision-makers. Once ethics boards make a decision, typically no appeals process exists and no other authority can validate their decision. Without effective guiding principles and appropriate due process, this model uses ethics boards to police researchers. It implies that researchers cannot be trusted and it focuses solely on blocking what the boards consider to be unethical.
We can develop a better model for AI ethics, one in which ethics complements and enhances research and development and where researchers are trusted collaborators with ethicists. This requires shifting our focus from principles and boards to ethical reasoning and teamwork, from ethics policing to ethics integration.
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