Summary

According to this document, "Ethics in AI becomes an issue when the social context is involved," and "almost everything an actuary does involves a social context because it involves people" (Raden, 2019: 8). The "twist" is that "after we launch technologies related to AI and machine learning, they not only shape us, but they also begin to shape themselves" (Rushkoff, 2019). Thus, "AI provides many new opportunities for ethical issues in practice beyond current practices," for example, 'black box' decision models, masked bias, and unregulated data. (Raden, 2019: 9) These make risk and responsibility more difficult to establish.
Author Neil Radan offers "guidance to the actuarial profession for understanding ethics in relation to using AI," focuses to a large degree on the determination of responsibility in relation to SAI ethics. Responsibility is the first of five related principles, the others being transparency, predictability, auditability and incorruptibility. and is intended to "highlight the ethical risks arising from the application of AI in actuarial practice and to have tools to use to identify and manage it" (Ibid: 10-11). According to the author, "A scan of the literature reveals that these five principles are more or less uniformly agreed upon." (Ibid: 11) Not surprisingly in an actuarial context, there is an emphasis on the potential for malicious use of AI, risks and remedies, and cybersecurity.

Content

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