Embryo Selection by “Black-Box” Artificial Intelligence: The Ethical and Epistemic Considerations
Background: The combination of time-lapse imaging and artificial intelligence (AI) offers novel potential for embryo assessment by allowing a vast quantity of image data to be analysed via machine learning. Most algorithms developed to date have used neural networks which are uninterpretable (“black...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
World Scientific Publishing
2022-09-01
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| Series: | Fertility & Reproduction |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S2661318222740590 |
| Summary: | Background: The combination of time-lapse imaging and artificial intelligence (AI) offers novel potential for embryo assessment by allowing a vast quantity of image data to be analysed via machine learning. Most algorithms developed to date have used neural networks which are uninterpretable (“black-box”) and cannot be understood by doctors, embryologists and patients, which raises ethical and epistemic concerns for embryo selection in a clinical setting. Aim: This study aims to discuss ethical and epistemic considerations surrounding clinical implementation of “black-box” based embryo selection algorithms. Method: A scoping review was performed by evaluating publications reporting “black-box” embryo selection algorithms. Potential ethical and epistemic issues were identified and discussed. Results: No randomised controlled trial was identified in the literature evaluating clinical effectiveness of “black-box” embryo selection algorithms. Several ethical and epistemic concerns were identified. Potential ethical issues included (1) lack of randomised controlled trials, (2) impact on the shared decision-making process in embryo selection between clinicians and patients, (3) misrepresentation of patient values due to hidden reasoning process in “black-box” algorithms, (4) social impacts if algorithm subsequently proven to be biased, and (5) unclear responsibility when algorithm makes obviously poor choices of embryos. Potential epistemic issues included (1) information asymmetries between algorithm developers and doctors, embryologists and patients; (2) risk of biased prediction due to data selection during training process; (3) inability to troubleshoot for data training purposes due to limited interpretability; and (4) the economics of buying into commercial proprietary add-ons. Conclusion: There are significant epistemic and ethical concerns with “black-box” embryo selection. No published randomised controlled trial is available to support its clinical implementation. AI embryo selection in general, however, is potentially useful but must be done carefully and transparently. Interpretable AI would be preferred alternative in causing fewer issues. |
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| ISSN: | 2661-3182 2661-3174 |