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Ethics, Transparency, Fairness and the Responsibility of Artificial Intelligence

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New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence (DiTTEt 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1410))

Abstract

Artificial Intelligence (AI), in all its different sub-fields, has grown significantly over the past years. When compared with other scientific or technological fields, this can almost be seen as a revolution. Nonetheless, as in other revolutions, not all that revolves around AI evolved at the same pace. As a consequence, many serious legal and ethical issues on the use of Artificial Intelligence are presently being raised. This paper addresses the main root causes for these problems from a technical standpoint, and then analyzes the legal and ethical framework. Finally, the paper describes a range of techniques and methods that can be used to address the identified problems, namely by ensuring transparency, fairness, equality, explanability and avoiding bias or discrimination. The field is presently at a tipping point, which can either lead to an avoidance of Artificial Intelligence due to fear or lack of regulation, or to a wide adoption supported by increased transparency and more human-centered approaches. Given the recent developments addressed in this paper, the paper argues in favor of a tendency towards the latter.

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References

  1. Alvarez-Melis, D., Jaakkola, T.S.: On the robustness of interpretability methods. arXiv preprint arXiv:1806.08049 (2018)

  2. Ananny, M., Crawford, K.: Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media Soc. 20(3), 973–989 (2018)

    Google Scholar 

  3. Antwarg, L., Miller, R.M., Shapira, B., Rokach, L.: Explaining anomalies detected by autoencoders using shap. arXiv preprint arXiv:1903.02407 (2019)

  4. Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)

    Article  Google Scholar 

  5. Atkinson, K., Bench-Capon, T., Bollegala, D.: Explanation in AI and law: past, present and future. Artificial Intelligence, p. 103387 (2020)

    Google Scholar 

  6. Bashayreh, M., Sibai, F.N., Tabbara, A.: Artificial intelligence and legal liability: towards an international approach of proportional liability based on risk sharing. Inf. Commun. Technol. Law 30(2), 169–192 (2021)

    Article  Google Scholar 

  7. Bertino, E., Kundu, A., Sura, Z.: Data transparency with blockchain and AI ethics. J. Data Inf. Quality (JDIQ) 11(4), 1–8 (2019)

    Article  Google Scholar 

  8. Blacklaws, C.: Algorithms: transparency and accountability. Philosophical Trans. Roy. Soc. A Math. Phys. Eng. Sci. 376(2128), 20170351 (2018)

    Article  Google Scholar 

  9. Brkan, M.: Ai-supported decision-making under the general data protection regulation. In: Proceedings of the 16th Edition of the International Conference on Artficial Intelligence and Law, pp. 3–8 (2017)

    Google Scholar 

  10. Buiten, M.C.: Towards intelligent regulation of artificial intelligence. Eur. J. Risk Regulation 10(1), 41–59 (2019)

    Article  Google Scholar 

  11. Carneiro, D., Silva, F., Guimarães, M., Sousa, D., Novais, P.: Explainable intelligent environments. In: International Symposium on Ambient Intelligence, pp. 34–43. Springer (2020)

    Google Scholar 

  12. Carrillo, M.R.: Artificial intelligence: from ethics to law. Telecommun. Policy 44(6), 101937 (2020)

    Google Scholar 

  13. Chang, H.: “he’s a machine-made so”.: Rethinking humanlike robots in issac asimov’s i, robot. ANQ: A Quarterly Journal of Short Articles, Notes and Reviews, pp. 1–5 (2020)

    Google Scholar 

  14. Comission, E.: Ethics Guidelines for Thrustworthy AI. Technical report, European Comission, April 2019

    Google Scholar 

  15. Comission, E.: White Paper on Artificial Intelligence: a European approach to excellence and trust. Technical report, European Comission, February 2020

    Google Scholar 

  16. Cox, V.: Exploratory data analysis. In: Translating Statistics to Make Decisions, pp. 47–74. Springer (2017)

    Google Scholar 

  17. Dressel, J., Farid, H.: The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4(1), eaao5580 (2018)

    Google Scholar 

  18. Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a “right to explanation". AI Mag. 38(3), 50–57 (2017)

    Google Scholar 

  19. Iphofen, R., Kritikos, M.: Regulating artificial intelligence and robotics: ethics by design in a digital society. Contemporary Soc. Sci. 16(2), 170–184 (2021)

    Article  Google Scholar 

  20. Lavalle, A., Maté, A., Trujillo Mondéjar, J.C., et al.: An approach to automatically detect and visualize bias in data analytics (2020)

    Google Scholar 

  21. Lloyd, E.P., Hugenberg, K.: Beyond bias: response bias and interpersonal (in) sensitivity as a contributors to race disparities. European Review of Social Psychology, pp. 1–34 (2021)

    Google Scholar 

  22. London, A.J.: Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent. Rep. 49(1), 15–21 (2019)

    Article  Google Scholar 

  23. Mantelero, A.: Ai and big data: a blueprint for a human rights, social and ethical impact assessment. Comput. Law Secur. Rev. 34(4), 754–772 (2018)

    Article  Google Scholar 

  24. Mazurek, G., Małagocka, K.: Perception of privacy and data protection in the context of the development of artificial intelligence. J. Manage. Anal. 6(4), 344–364 (2019)

    Google Scholar 

  25. McCauley, L.: Ai armageddon and the three laws of robotics. Ethics Inf. Technol. 9(2), 153–164 (2007)

    Article  Google Scholar 

  26. Mundhenk, T.N., Chen, B.Y., Friedland, G.: Efficient saliency maps for explainable ai. arXiv preprint arXiv:1911.11293 (2019)

  27. Orr, W., Davis, J.L.: Attributions of ethical responsibility by artificial intelligence practitioners. Inf. Commun. Soc. 23(5), 719–735 (2020)

    Article  Google Scholar 

  28. Reed, C.: How should we regulate artificial intelligence? Philosophical Trans. Roy. Soc. A Math. Phys. Eng. Sci. 376(2128), 20170360 (2018)

    Article  Google Scholar 

  29. Tam, S.M., Kim, J.K.: Big data ethics and selection-bias: an official statistician’s perspective. Stat. J. IAOS 34(4), 577–588 (2018)

    Article  Google Scholar 

  30. Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR 2011, pp. 1521–1528. IEEE (2011)

    Google Scholar 

  31. Wachter-Boettcher, S.: Technically wrong: Sexist apps, biased algorithms, and other threats of toxic tech. WW Norton & Company (2017)

    Google Scholar 

  32. Wu, W., Huang, T., Gong, K.: Ethical principles and governance technology development of AI in China. Engineering 6(3), 302–309 (2020)

    Article  Google Scholar 

  33. Zuiderveen Borgesius, F.J.: Strengthening legal protection against discrimination by algorithms and artificial intelligence. Int. J. Hum. Rights 24(10), 1572–1593 (2020)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Northern Regional Operational Program, Portugal 2020 and European Union, trough European Regional Development Fund (ERDF) in the scope of project number 39900 - 31/SI/2017, and by FCT - Fundação para a Ciência e a Tecnologia, through projects UIDB/04728/2020 and UID/CEC/00319/2019.

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Authors and Affiliations

  1. CIICESI, Escola Superior de Tecnologia e Gestão, Instituto Politécnico do Porto, Felgueiras, Portugal

    Davide Carneiro & Patrícia Veloso

  2. Centro ALGORITMI, Universidade do Minho, Braga, Portugal

    Davide Carneiro

Authors
  1. Davide Carneiro
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  2. Patrícia Veloso
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Corresponding author

Correspondence to Davide Carneiro .

Editor information

Editors and Affiliations

  1. Faculty of Science, University of Salamanca, Salamanca, Spain

    Juan F. de Paz Santana

  2. Facultad de Informática, Universidad Pontificia de Salamanca, Salamanca, Spain

    Daniel H. de la Iglesia

  3. Facultad de Informática, Pontifical University of Salamanca, Salamanca, Spain

    Alfonso José López Rivero

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Carneiro, D., Veloso, P. (2022). Ethics, Transparency, Fairness and the Responsibility of Artificial Intelligence. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-87687-6_12

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