A New Model For AI Ethics In R&D
"The ethical framework that evolved for biomedical research—namely, the ethics oversight and compliance model—was developed in reaction to the horrors arising from biomedical research during World War II and which continued all the way into the ’70s.
In response, bioethics principles and ethics review boards guided by these principles were established to prevent unethical research. In the process, these boards were given a heavy hand to regulate research without checks and balances to control them. Despite deep theoretical weaknesses in its framework and massive practical problems in its implementation, this became the default ethics governance model, perhaps due to the lack of competition.
The framework now emerging for AI ethics resembles this model closely. In fact, the latest set of AI principles—drafted by AI4People and forming the basis for the Draft Ethics Guidelines of the European Commission’s High-Level Expert Group on AI—evaluates 47 proposed principles and condenses them into just five.
Four of these are exactly the same as traditional bioethics principles: respect for autonomy, beneficence, non-maleficence, and justice, as defined in the Belmont Report of 1979. There is just one new principle added—explicability. But even that is not really a principle itself, but rather a means of realizing the other principles. In other words, the emerging default model for AI ethics is a direct transplant of bioethics principles and ethics boards to AI ethics. Unfortunately, it leaves much to be desired for effective and meaningful integration of ethics into the field of AI."
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