Article Text
Abstract
In this article, our aim is to show why increasing the effectiveness of detecting doping fraud in sport by the use of artificial intelligence (AI) may be morally wrong. The first argument in favour of this conclusion is that using AI to make a non-ideal antidoping policy even more effective can be morally wrong. Whether the increased effectiveness is morally wrong depends on whether you believe that the current antidoping system administrated by the World Anti-Doping Agency is already morally wrong. The second argument is based on the possibility of scenarios in which a more effective AI system may be morally worse than a less effective but non-AI system. We cannot, of course, conclude that the increased effectiveness of doping detection is always morally wrong. But our point is that whether the introduction of AI to increase detection of doping fraud is a moral improvement depends on the moral plausibility of the current system and the distribution of harm that will follow from false positive and false negative errors.
- Ethics
- Decision Making
- Enhancement
- Policy
- Fraud
Data availability statement
Data sharing not applicable as no datasets generated and/or analysed for this study.
Statistics from Altmetric.com
Data availability statement
Data sharing not applicable as no datasets generated and/or analysed for this study.
Footnotes
Twitter @EthicsThSoebirk, @SebastianJonHo1
Contributors All authors have followed the Vancouver reommendations. TSP is the guarantor of the study.
Funding This research is supported by the Independent Reserach Fund Denmark - grant number 7023-00018B
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Read the full text or download the PDF:
Other content recommended for you
- Implications of conscious AI in primary healthcare
- Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy
- AI-enabled suicide prediction tools: ethical considerations for medical leaders
- Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review
- Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis
- Ethics of the algorithmic prediction of goal of care preferences: from theory to practice
- Limits of trust in medical AI
- Using AI ethically to tackle covid-19
- Does “AI” stand for augmenting inequality in the era of covid-19 healthcare?
- Glaucoma management in the era of artificial intelligence