Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology

References

1. 1.↵
   2. Geis JR,
   3. Brady AP,
   4. Wu CC, et al

   . Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement. Radiology 2019;293:436–40 doi:10.1148/radiol.2019191586 pmid:31573399

   CrossRefPubMed
2. 2.↵
   “Ethics”. Merriam-Webster.com Dictionary. Merriam-Webster, Inc. http://www.merriam-webster.com/dictionary/ethics. Accessed January 3, 2023
3. 3.↵
   2. Panch T,
   3. Mattie H,
   4. Atun R

   . Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health 2019;9:010318 doi:10.7189/jogh.09.020318 pmid:31788229

   CrossRefPubMed
4. 4.↵
   “Fairness”. Merriam-Webster.com Dictionary. Merriam-Webster, Inc. http://www.merriam-webster.com/dictionary/fairness. Accessed January 3, 2023
5. 5.↵
   2. Dwork C,
   3. Hardt M,
   4. Pitassi T, et al

   . Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, Cambridge, Massachusetts. January 8–10, 2012 doi:10.1145/2090236.2090255
6. 6.↵
   2. Crawford K

   . The trouble with bias. NIPS 2017;10 https://www.youtube.com/watch?v=fMym_BKWQzk. Accessed January 3, 2023
7. 7.↵
   2. Abbasi M,
   3. Friedler SA,
   4. Scheidegger C, et al

   . Fairness in representation: quantifying stereotyping as a representational harm. In: Proceedings of the 2019 SIAM International Conference on Data Mining: SIAM, Calgary, Alberta, Canada, May 2–4, 2019
8. 8.↵
   2. Amini A,
   3. Soleimany AP,
   4. Schwarting W, et al

   . Uncovering and mitigating algorithmic bias through learned latent structure. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society 2019;289–95 doi:10.1145/3306618.3314243

   CrossRef
9. 9.↵
   Association for Computing Machinery. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), Soeul, South Korea. June 21–24, 2022
10. 10.↵
    2. Jillson E

    . Aiming for truth, fairness, and equity in your company's use of AI. Business Blog Federal Trade Commision. April 19, 2021. https://www.ftc.gov/business-guidance/blog/2021/04/aiming-truth-fairness-equity-your-companys-use-ai. Accessed January 5, 2023
11. 11.↵
    Algorithmic Accountability Act of 2022. HR 6580. 117th Congress, 2021–2022
12. 12.↵
    2. Chouldechova A

    . Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 2017;5:153–63 doi:10.1089/big.2016.0047 pmid:28632438

    CrossRefPubMed
13. 13.↵
    2. Kleinberg J,
    3. Mullainathan S,
    4. Raghavan M

    . Inherent trade-offs in the fair determination of risk scores. In: Proceedings of the 8th Innovations in Theoretical Computer Science Conference (ITCS 2017), Berkeley, California. January 9–11, 2017. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik
14. 14.↵
    2. Corbett-Davies S,
    3. Goel S

    . The measure and mismeasure of fairness: a critical review of fair machine learning. arXiv 2018 arXiv:180800023
15. 15.↵
    2. Verma S,
    3. Rubin J

    . Fairness definitions explained. In: Proceedings of the 2018 IEEE/ACM International Workshop on Software Fairness (Fairware). IEEE Xplore 2018;1–7 doi:10.1145/3194770.3194776

    CrossRef
16. 16.↵
    2. Rajkomar A,
    3. Hardt M,
    4. Howell MD, et al

    . Ensuring fairness in machine learning to advance health equity. Ann Intern Med 2018;169:866–72 doi:10.7326/M18-1990 pmid:30508424

    CrossRefPubMed
17. 17.↵
    2. Lundberg SM,
    3. Lee SI

    . A unified approach to interpreting model predictions. arXiv 1705.07874 May 22, 2017
18. 18.↵
    2. Lundberg SM

    . Explaining quantitative measures of fairness. In: Proceedings of the Fair & Responsible AI Workshop. CHI ‘20: CHI Conference on Human Factors in Computing Systems, Honolulu, Hawaii. April 25–30, 2020
19. 19.↵
    2. Zhao J,
    3. Wang T,
    4. Yatskar M, et al

    . Men also like shopping: reducing gender bias amplification using corpus-level constraints. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark. September 7–11, 2017
20. 20.↵
    2. Zou J,
    3. Schiebinger L

    . AI can be sexist and racist: it's time to make it fair. Nature 2018;559:324–26 doi:10.1038/d41586-018-05707-8 pmid:30018439

    CrossRefPubMed
21. 21.↵
    2. Sweeney L

    . Discrimination in online ad delivery. Communications of the ACM 2013;56:44–54 doi:10.1145/2447976.2447990

    CrossRef
22. 22.↵
    2. Buolamwini J,
    3. Gebru T

    . Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of the Conference on Fairness, Accountability and Transparency, New York, New York. February 23–24, 2018;77–91
23. 23.↵
    2. Angwin J,
    3. Larson J,
    4. Matta S, et al

    . Machine bias: there's software used across the country to predict future criminals. and it's biased against Blacks. ProPublica May 23, 2016 https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed January 6, 2023
24. 24.↵
    2. Benjamin R

    . The New Jim code: reimagining the default settings of technology & society. Race and technology: A Research Lecture Series. May 1, 2021 to June 30, 2022. Virtual https://www.youtube.com/watch?v=aMuD_lAy2zQ. Accessed July 24, 2022
25. 25.↵
    2. Gunzer F,
    3. Jantscher M,
    4. Hassler EM, et al

    . Reproducibility of artificial intelligence models in computed tomography of the head: a quantitative analysis. Insights Imaging 2022;13:173 doi:10.1186/s13244-022-01311-7 pmid:36303079

    CrossRefPubMed
26. 26.↵
    2. Din M,
    3. Agarwal S,
    4. Grzeda M, et al

    . Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. J Neurointerv Surg 2023;15:262–71 doi:10.1136/jnis-2022-019456 pmid:36375834

    Abstract/FREE Full Text
27. 27.↵
    2. McLouth J,
    3. Elstrott S,
    4. Chaibi Y, et al

    . Validation of a deep learning tool in the detection of intracranial hemorrhage and large vessel occlusion. Front Neurol 2021;12:656112 doi:10.3389/fneur.2021.656112 pmid:33995252

    CrossRefPubMed
28. 28.↵
    2. Guo J,
    3. Gong E,
    4. Fan AP, et al

    . Predicting (15)O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias. J Cereb Blood Flow Metab 2020;40:2240–53 doi:10.1177/0271678X19888123 pmid:31722599

    CrossRefPubMed
29. 29.↵
    2. Gao MM,
    3. Wang J,
    4. Saposnik G

    . The art and science of stroke outcome prognostication. Stroke 2020;51:1358–60 pmid:32208841

    CrossRefPubMed
30. 30.↵
    2. Edlow BL,
    3. Giacino JT,
    4. Hirschberg RE, et al

    . Unexpected recovery of function after severe traumatic brain injury: the limits of early neuroimaging-based outcome prediction. Neurocrit Care 2013;19:364–75 doi:10.1007/s12028-013-9870-x pmid:23860665

    CrossRefPubMed
31. 31.↵
    2. Cramer H,
    3. Holstein K,
    4. Vaughan J, et al

    . Challenges of incorporating algorithmic fairness into industry practice. ACM FAT* 2019 Translation Tutorial. February 22, 2019 https://www.youtube.com/watch?v=UicKZv93SOY, Accessed July 24, 2022
32. 32.↵
    2. Barocas S,
    3. Hardt M

    . Fairness in machine learning. Tutorial. NIPS 2017. Long Beach, CA. December 4–9, 2017
33. 33.↵
    2. Schwarz CG,
    3. Kremers WK,
    4. Therneau TM, et al

    . Identification of anonymous MRI research participants with face-recognition software. N Engl J Med 2019;381:1684–86 doi:10.1056/NEJMc1908881 pmid:31644852

    CrossRefPubMed
34. 34.↵
    2. Athinoula A

    . Martinos Center for Biomedical Imaging: Martinos Center Anthropomorphic Phantoms. https://phantoms.martinos.org/Main_Page. Accessed May 21, 2023
35. 35.↵
    2. Schwarz CG,
    3. Kremers WK,
    4. Lowe VJ, et al

    ; Alzheimer's Disease Neuroimaging Initiative. Face recognition from research brain PET: an unexpected PET problem. Neuroimage 2022;258:119357 doi:10.1016/j.neuroimage.2022.119357 pmid:35660089

    CrossRefPubMed
36. 36.↵
    2. Jeong YU,
    3. Yoo S,
    4. Kim YH, et al

    . De-identification of facial features in magnetic resonance images: software development using deep learning technology. J Med Internet Res 2020;22:e22739 doi:10.2196/22739 pmid:33208302

    CrossRefPubMed
37. 37.↵
    2. Arora A,
    3. Arora A

    . Synthetic patient data in health care: a widening legal loophole. Lancet 2022;399:1601–02 doi:10.1016/S0140-6736(22)00232-X pmid:35358423

    CrossRefPubMed
38. 38.↵
    2. Goodfellow I,
    3. Pouget-Abadie J,
    4. Mirza M, et al

    . Generative adversarial nets; NIPS'14: In: Proceedings of the 27th International Conference on Neural Information Processing, Montreal, Quebec, Canada. December 8–13, 2014
39. 39.↵
    2. Ross C,
    3. Swetlitz I

    . IBM's Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show. STAT+. July 25, 2018 https://www.statnews.com/wp-content/uploads/2018/09/IBMs-Watson-recommended-unsafe-and-incorrect-cancer-treatments-STAT.pdf. Accessed July 19, 2022
40. 40.↵
    2. Onder O,
    3. Yarasir Y,
    4. Azizova A, et al

    . Errors, discrepancies and underlying bias in radiology with case examples: a pictorial review. Insights Imaging 2021;12:51 doi:10.1186/s13244-021-00986-8 pmid:33877458

    CrossRefPubMed
41. 41.↵
    2. Choi K,
    3. Grover A,
    4. Singh T, et al

    . In: Proceedings of the International Conference on Machine Learning, Virtual. July 12–18, 2020;1887–98
42. 42.↵
    2. Obermeyer Z,
    3. Powers B,
    4. Vogeli C, et al

    . Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019;366:447–53 doi:10.1126/science.aax2342 pmid:31649194

    Abstract/FREE Full Text
43. 43.↵
    2. Agarwal A,
    3. Beygelzimer A,
    4. Dudík M, et al

    . A reductions approach to fair classification. In: Proccedings of the International Conference on Machine Learning, Stockholm, Sweden. July 10–15, 2018;60–69
44. 44.↵
    Fairlearn. Improve fairness of AI systems. https://fairlearn.org/. Accessed July 24, 2022
45. 45.↵
    IBM Research. AI Fairness 360. https://ibm.com/opensource/open/projects/ai-fairness-360. Accessed July 24, 2022
46. 46.↵
    Google. The ML Fairness Gym. https://github.com/google/ml-fairness-gym. Accessed July 24, 2022
47. 47.↵
    What-If Tool. https://pair-code.github.io/what-if-tool/. Accessed July 24, 2022
48. 48.↵
    1. Marina M,
    2. Tong Z

    2. Koh PW,
    3. Sagawa S,
    4. Marklund H, et al

    . WILDS: a benchmark of in-the-wild distribution shifts. In: Marina M, Tong Z, eds. In: Proceedings of the 38th International Conference on Machine Learning Virtual. July 18-21, 2021:5637–64
49. 49.↵
    2. Yan W,
    3. Wang Y,
    4. Xia M, et al

    . Edge-guided output adaptor: highly efficient adaptation module for cross-vendor medical image segmentation. IEEE Signal Process Lett 2019;26:1593–97 doi:10.1109/LSP.2019.2940926

    CrossRef
50. 50.↵
    2. Schumann C,
    3. Wang X,
    4. Beutel A, et al

    . Transfer of machine learning fairness across domains. arXiv 2019 arXiv:190609688
51. 51.↵
    2. Joshi N,
    3. Burlina P

    . AI fairness via domain adaptation. arXiv 2021 arXiv:2104.01109
52. 52.↵
    2. Floridi L,
    3. Cowls J

    . A united framework of five principles for AI in society. Harvard Data Science Review 2019;1 doi:10.1162/99608f92.8cd550d1

    CrossRef
53. 53.↵
    2. Sheller MJ,
    3. Edwards B,
    4. Reina GA, et al

    . Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep 2020;10:12598 doi:10.1038/s41598-020-69250-1 pmid:32724046

    CrossRefPubMed
54. 54.↵
    2. Li T,
    3. Sahu AK,
    4. Talwalkar A, et al

    . Federated learning: challenges, methods, and future directions. IEEE Signal Processing Magazine 2020;37:50–60 doi:10.1109/MSP.2020.2975749

    CrossRef
55. 55.↵
    2. Wang L,
    3. Xu S,
    4. Wang X, et al

    . Eavesdrop the composition proportion of training labels in federated learning. arXiv 2019 arXiv:1910.06044
56. 56.↵
    2. Pejó B,
    3. Biczók G

    . Quality inference in federated learning with secure aggregation. arXiv 2020 arXiv:2007.06236
57. 57.↵
    2. Abay A,
    3. Zhou Y,
    4. Baracaldo N, et al

    . Mitigating bias in federated learning. arXiv 2020 arXiv:2012.02447
58. 58.↵
    2. Gu X,
    3. Tianqing Z,
    4. Li J, et al

    . Privacy, accuracy, and model fairness trade-offs in federated learning. Computers & Security 2022;122:102907 doi:10.1016/j.cose.2022.102907

    CrossRef
59. 59.↵
    2. Mittelstadt BD,
    3. Floridi L

    . The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics 2016;22:303–41 doi:10.1007/s11948-015-9652-2 pmid:26002496

    CrossRefPubMed
60. 60.↵
    2. Larson DB,
    3. Magnus DC,
    4. Lungren MP, et al

    . Ethics of using and sharing clinical imaging data for artificial intelligence: a proposed framework. Radiology 2020;295:675–82 doi:10.1148/radiol.2020192536 pmid:32208097

    CrossRefPubMed
61. 61.↵
    2. Varkey B

    . Principles of clinical ethics and their application to practice. Med Princ Pract 2021;30:17–28 doi:10.1159/000509119 pmid:32498071

    CrossRefPubMed
62. 62.↵
    2. Mongan J,
    3. Kohli M

    . Artificial intelligence and human life: five lessons for radiology from the 737 MAX Disasters. Radiol Artif Intell 2020;2:e190111 doi:10.1148/ryai.2020190111 pmid:33937819

    CrossRefPubMed
63. 63.↵
    2. Floridi L,
    3. Taddeo M

    . What is data ethics? Philos Trans A Math Phys Eng Sci 2016;374:20160360 doi:10.1098/rsta.2016.0360

    CrossRefPubMed
64. 64.↵
    2. Floridi L,
    3. Cowls J,
    4. Beltrametti M, et al

    . AI4People: an ethical framework for a good AI society—opportunities, risks, principles, and recommendations. Minds Mach (Dordr) 2018;28:689–707 doi:10.1007/s11023-018-9482-5 pmid:30930541

    CrossRefPubMed
65. 65.↵
    2. Gilpin LH,
    3. Bau D,
    4. Yuan BZ, et al

    . Explaining explanations: an overview of interpretability of machine learning. In: Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAS), Turin, Italy. October 1–3, 2018;80–89 doi:10.1109/DSAA.2018.00018

    CrossRef
66. 66.↵
    2. Haibe-Kains B,
    3. Adam GA,
    4. Hosny A, et al

    ; Massive Analysis Quality Control (MAQC) Society Board of Directors. Transparency and reproducibility in artificial intelligence. Nature 2020;586:E14–16 doi:10.1038/s41586-020-2766-y pmid:33057217

    CrossRefPubMed
67. 67.↵
    1. Bramer M,
    2. Petridis M

    2. Kingston JKC

    . Artificial intelligence and legal liability. In: Bramer M, Petridis M, eds. Research and Development in Intelligent Systems XXXIII. Springer-Verlag International Publishing 2016:269–79