Bibliografía específica sobre ética, inteligencia artificial y COVID-19
Ausín, T. Andreu Martínez, B. (2020). Ética y protección de datos de salud en contexto de pandemia: una referencia especial al caso de las aplicaciones de rastreo de contactos. Enrahonar : quaderns de filosofia, Vol. 65, p. 47-56
Automating Society Report 2020. ADM Systems in the COVID-19 Pandemic: A European Perspective. AlgorithmWatch and Bertelsmann Stiftung
Bansal, A., Padappayil, R.P., Garg, C. et al. (2020) Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review. J Med Syst 44, 156.
Bengio Y, Janda R, Yu YW, Ippolito D, Jarvie M, Pilat D, Struck B, Krastev S, Sharma A. (2020) The need for privacy with public digital contact tracing during the COVID-19 pandemic. Lancet Digit Health;2(7):e342-e344. doi: 10.1016/S2589-7500(20)30133-3.
Birhane, A. (2021). Algorithmic injustice: a relational ethics approach, Patterns, 2 (2)100205. https://doi.org/10.1016/j.patter.2021.100205
E. Casiraghi et al. (2020), “Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments,” in IEEE Access, 8, pp. 196299-196325, doi: 10.1109/ACCESS.2020.3034032.
Casacuberta, D., Guersenzvaig, A. & Moyano-Fernández, C. Justificatory explanations in machine learning: for increased transparency through documenting how key concepts drive and underpin design and engineering decisions. AI & Soc (2022).
Char, D. S., Abràmoff, M. D. & Feudtner, C. (2020) Identifying Ethical Considerations for Machine Learning Healthcare Applications. The American Journal of Bioethics, 20:11, 7-17, DOI: 10.1080/15265161.2020.1819469
Colizza V, Grill E, Mikolajczyk R, Cattuto C, Kucharski A, Riley S, Kendall M, Lythgoe K, Bonsall D, Wymant C, Abeler-Dörner L, Ferretti L, Fraser C. (2021) Time to evaluate COVID-19 contact-tracing apps. Nat Med; 27(3):361-362. doi: 10.1038/s41591-021-01236-6.
Debnath, S., Barnaby, D.P., Coppa, K. et al. (2020). Machine learning to assist clinical decision-making during the COVID-19 pandemic. Bioelectron Med 6, 14.
Eubanks, V. (2018). Automating Inequality. How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.
Gao, Y., Cai, GY., Fang, W. et al. (2020) Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun 11, 5033.
Grantz, K.H., Meredith, H.R., Cummings, D.A.T. et al. (2020) The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun 11, 4961
Gulliver, R., Fahmi, M, Abramson, D. (2020) Technical considerations when implementing digital infrastructure for social policy AJSI
Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc. 2020 Dec 9;27(12):2011-2015. doi: 10.1093/jamia/ocaa088.
Jamshidi MB, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P, Jamshidi M, Spada L, Mirmozafari M, Dehghani M, Sabet A, Roshani S, Roshani S, Bayat-Makou N, Mohamadzade B, Malek Z, Jamshidi A, Kiani S, Hashemi-Dezaki H, Mohyuddin W. (2020) Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment. IEEE Access. 12;8:109581-109595. doi: 10.1109/ACCESS.2020.3001973.
Kaplan, B. (2020). Seeing through health information technology: the need for transparency in software, algorithms, data privacy, and regulation, Journal of Law and the Biosciences, lsaa062,
Kearns, M. & Roth, A. (2020). The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Oxford: OUP.
Kiener M. (2020). Artificial intelligence in medicine and the disclosure of risks. AI & society, 1–9. Advance online publication.
Klingwort, J., & Schnell, R. (2020). Critical limitations of digital epidemiology: Why COVID-19 apps are useless. Survey Research Methods, a joural of the European Survey Research Association, 14 (2).
Leslie D., et al. (2021) Does “AI” stand for augmenting inequality in the era of covid-19 healthcare? BMJ 2021; 372.
Liang, W., Yao, J., Chen, A. et al. (2020). Early triage of critically ill COVID-19 patients using deep learning. Nat Commun 11, 3543.
Lumb, R., Lall, V., & Moreno, A. (2020). The role of AI in testing, tracking and treatment of covid-19. American Journal of Management, 20(3), 55-64. Retrieved from
Marjanovic, O, Cecez-Kecmanovic, D., Vidgen, R. (2022) Theorising Algorithmic Justice, European Journal of Information Systems, 31:3, 269-287, DOI: 10.1080/0960085X.2021.1934130.
Márquez Díaz, J. (2020). Inteligencia artificial y Big Data como soluciones frente a la COVID-19. Revista de Bioética y Derecho, 50, 315-331.
Mbunge E. (2020) Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls. Diabetes Metab Syndr;14(6):1631-1636. doi: 10.1016/j.dsx.2020.08.029.
Mbunge, E; Akinnuwesi, B; Fashoto, S. G.; Metfula, A. S.; Mashwama, P. (2021) A critical review of emerging technologies for tackling COVID-19 pandemic.Human Behavior and Emerging Technologies, 3:25- 39.
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., … & Gebru, T. (2019, January). Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency (pp. 220-229).
Mökander, J., Floridi, L. (2021) Ethics-Based Auditing to Develop Trustworthy AI. Minds & Machines 31, 323–327.
Moseley, D. (2019) Bias. In Hugh LaFollette (ed.), The International Encyclopedia of Ethics. Malden, MA, USA: Wiley-Blackwell
Moss, E. & Metcalf, J. (2020). High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response. Patterns, 1(7), 100102.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447 LP – 453.
Park S, Choi GJ, Ko H. (2020). Information Technology–Based Tracing Strategy in Response to COVID-19 in South Korea—Privacy Controversies. JAMA. ;323(21):2129–2130. doi:10.1001/jama.2020.6602.
Quiñones AR, Botoseneanu A, Markwardt S, Nagel CL, Newsom JT, et al. (2019) Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults. PLOS ONE 14(6): e0218462.
Quiroz-Juárez MA, Torres-Gómez A, Hoyo-Ulloa I, León-Montiel RJ, U’Ren AB. Identification of high-risk COVID-19 patients using machine learning. PLoS One. 2021 Sep 20;16(9):e0257234. doi: 10.1371/journal.pone.0257234.
Ravizza A, Sternini F, Molinari F, Santoro E, Cabitza F. (2021) A Proposal For COVID-19 Applications Enabling Extensive Epidemiological Studies. Procedia Comput Sci;181:589-596. doi: 10.1016/j.procs.2021.01.206.
Roche, S. (2020) Smile, you’re being traced! Some thoughts about the ethical issues of digital contact tracing applications, Journal of Location Based Services, 14:2, 71-91, DOI: 10.1080/17489725.2020.1811409.
Romeo Casabona, C. et al. (2020). Inteligencia artificial en salud: retos éticos y legales. Informes anticipando. Informes anticipando. Fundación Instituto Roche.
Röösli, E., Rice, B., Hernandez-Boussard, T. (2021) Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19, Journal of the American Medical Informatics Association, 28 (1), Pages 190–192,
Sáez C., Romero N., Conejero J.A., García-Gómez J.M. (2021) Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset, Journal of the American Medical Informatics Association, 28 (2) Pages 360–364,
Scott IA, Coiera EW. (2020) Can AI help in the fight against COVID-19? Med J Aust.;213(10):439-441.e2. doi: 10.5694/mja2.50821.
Shachar C, Gerke S, Adashi EY. (2020) AI Surveillance during Pandemics: Ethical Implementation Imperatives. Hastings Cent Rep.;50(3):18-21. doi: 10.1002/hast.1125.
Shaw, J.A., Sethi, N. & Block, B.L. (2020). Five things every clinician should know about AI ethics in intensive care. Intensive Care Med.
Shneiderman B. (2020) Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems. ACM Trans. Interact. Intell. Syst. 10 (4)
Starke G, De Clercq E, Elger BS. (2021). Towards a pragmatist dealing with algorithmic bias in medical machine learning. Med Health Care Philos, 24(3):341-349. doi: 10.1007/s11019-021-10008-5.
Sun, R.,Wang, W., Xue, M., Tyson, G., Camtepe, S. and Ranasinghe, D.C. (2021) “An Empirical Assessment of Global COVID-19 Contact Tracing Applications,” 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), 1085-1097, doi: 10.1109/ICSE43902.2021.00101.
Tsamados, A.Aggarwal, N., Cowls, J. et al. (2022). The Ethics of Algorithms: Key Problems and Solutions.AI & Soc 37, 215–230
Whittlestone, J. Nyrup, R. Alexandrova, A. Dihal, K. Cave, S. (2019) Ethical and societal implications of algorithms, data, and artificial intelligence: a roadmap for research. London: Nuffield Foundation.
Williams, J. C., Anderson, N., Mathis, M., Sanford, E., Eugene, J., & Isom, J. (2020). Colorblind Algorithms: Racism in the Era of COVID-19. Journal of the National Medical Association.
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., Ye, L., Gao, M., Zhou, Z., Li, L., Wang, J., Yang, Z., Cai, H., Xu, J., Yang, L., Cai, W., … Wang, G. (2020). Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell, 181(6), 1423–1433.e11.
Zhu, JS, Ge, P, Jiang, C, et al. Deep‐learning artificial intelligence analysis of clinical variables predicts mortality in COVID‐19 patients. JACEP Open. 2020; 1‐ 10.
Otra bibliografía no específica de Covid-19
Adamson AS, Smith A. Machine Learning and Health Care Disparities in Dermatology. JAMA Dermatol.2018;154(11):1247–1248. doi:10.1001/jamadermatol.2018.2348.
Amann, J., Blasimme, A., Vayena, E. et al. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 20, 310.
Bolukbasi, T., Chang, K., Zou, J.Y., Saligrama, V., & Kalai, A.T. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. NIPS.
Chakraborti, T., Kulkarni, A., Sreedharan, S., Smith, D. E., & Kambhampati, S. (2021). Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior. Proceedings of the International Conference on Automated Planning and Scheduling, 29(1), 86-96.
Choraś, M., Pawlicki, M., Puchalski, D., Kozik, R. (2020). Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?. In: , et al. Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science(), vol 12140. Springer, Cham.
Chouldechova, A., & Roth, A. (2018). The Frontiers of Fairness in Machine Learning. ArXiv, abs/1810.08810.
Crawford, K. (2021) Atlas of AI. Yale University Press.
D’Amour A, et al. (2020). Underspecification Presents Challenges for Credibility in Modern Machine Learning. ArXiv.org. November 6.
D’Ignazio C and Klein L F. (2020) Data feminism. MIT, Cambridge, Massachusetts.
Gigerenzer, Gerd, Simply Rational: Decision Making in the Real World, Evolution and Cognition Series(2015; online edn, Oxford Academic, 23 Apr. 2015),
IEEE (2020) IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being, in IEEE Std 7010-2020 , vol., no., pp.1-96 doi: 10.1109/IEEESTD.2020.9084219.
Jobin, A., Ienca, M. & Vayena, E. (2019) The global landscape of AI ethics guidelines. Nat Mach Intell 1, 389–399.
Kostick-Quenet KM, Cohen IG, Gerke S, Lo B, Antaki J, Movahedi F, Njah H, Schoen L, Estep JE, Blumenthal-Barby JS. Mitigating Racial Bias in Machine Learning. J Law Med Ethics. 2022;50(1):92-100. doi: 10.1017/jme.2022.13.
Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute.
London AJ. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. Hastings Cent Rep. 2019 Jan;49(1):15-21. doi:10.1002/hast.973.
Madaio, M.A., Stark, L., Vaughan, J. W., Wallach, H. (2020). Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing SystemsApril 2020 Pages 1–14.
Marewski JN, Gigerenzer G. Heuristic decision making in medicine. Dialogues Clin Neurosci. 2012 Mar;14(1):77-89. doi:10.31887/DCNS.2012.14.1/jmarewski.
Noor P. (2020) Can we trust AI not to further embed racial bias and prejudice? BMJ. Feb 12;368:m363. doi: 10.1136/bmj.m363.
Noseworthy PA, Attia ZI, Brewer LC, Hayes SN, Yao X, Kapa S, Friedman PA, Lopez-Jimenez F. Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis. Circ Arrhythm Electrophysiol. 2020 Mar;13(3):e007988. doi: 10.1161/CIRCEP.119.007988.
Pot M, Kieusseyan N, Prainsack B. Not all biases are bad: equitable and inequitable biases in machine learning and radiology. Insights Imaging. 2021 Feb 10;12(1):13. doi: 10.1186/s13244-020-00955-7. .
Robles Carrillo, M. (2020). Artificial intelligence: From ethics to law, Telecommunications Policy, 44 (6)
Ursin, F, Timmermann, C, Steger, F. (2021) Explicability of artificial intelligence in radiology: Is a fifth bioethical principle conceptually necessary? Bioethics 36 (2):143-153
Zhou, Y. and Danks, D. (2020). Different “Intelligibility” for Different Folks. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery, New York, NY, USA, 194–199.
