Two Reasons for Subjecting Medical AI Systems to Lower Standards than Humans

TY - GEN

T1 - Two Reasons for Subjecting Medical AI Systems to Lower Standards than Humans

AU - Mainz, Jakob Thrane

AU - Munch, Lauritz Aastrup

AU - Bjerring, Jens Christian

PY - 2023/6/12

Y1 - 2023/6/12

N2 - This paper concerns the double standard debate in the ethics of AI literature. This debate revolves around the question of whether we should subject AI systems to different normative standards than humans. So far, the debate has centered around transparency. That is, the debate has focused on whether AI systems must be more transparent than humans in their decision-making processes in order for it to be morally permissible to use such systems. Some have argued that the same standards of transparency should be applied to AI systems and humans. Others have argued that we should hold AI systems to higher standards than humans in terms of transparency. In this paper, we first highlight that debates concerning double standards, which have a similar structure to those related to transparency, exist in relation to other values such as predictive accuracy. Second, we argue that when we focus on predictive accuracy, there are at least two reasons for holding AI systems to a lower standard than humans.

AB - This paper concerns the double standard debate in the ethics of AI literature. This debate revolves around the question of whether we should subject AI systems to different normative standards than humans. So far, the debate has centered around transparency. That is, the debate has focused on whether AI systems must be more transparent than humans in their decision-making processes in order for it to be morally permissible to use such systems. Some have argued that the same standards of transparency should be applied to AI systems and humans. Others have argued that we should hold AI systems to higher standards than humans in terms of transparency. In this paper, we first highlight that debates concerning double standards, which have a similar structure to those related to transparency, exist in relation to other values such as predictive accuracy. Second, we argue that when we focus on predictive accuracy, there are at least two reasons for holding AI systems to a lower standard than humans.

KW - Cost-effectiveness

KW - Double Standard

KW - Opacity

KW - Predictive Accuracy

KW - Speed

UR - http://www.scopus.com/inward/record.url?scp=85163624275&partnerID=8YFLogxK

U2 - 10.1145/3593013.3593975

DO - 10.1145/3593013.3593975

M3 - Article in proceedings

SN - 9798400701924

SP - 44

EP - 49

BT - Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

PB - Association for Computing Machinery

CY - New York

ER -