Skip to main content

2024 | OriginalPaper | Buchkapitel

12. Responsible Data Science

verfasst von : Laura Igual, Santi Seguí

Erschienen in: Introduction to Data Science

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Data science has an increasing responsibility in society, which means it needs to consider more than just technical skills. Data scientists must recognize and embrace this responsibility, acknowledging its ethical, moral, and societal implications. Addressing these responsibilities ensures that data science is used for the benefit of society while preserving individual rights. Data science’s impact on privacy, autonomy, and well-being requires prioritizing personal data protection and respecting privacy rights. Ethical data handling, informed consent, and robust security measures are imperative to prevent unauthorized access and misuse of personal information. Upholding these principles fosters trust between individuals and the data-driven systems influencing their lives, ultimately guiding data science toward a socially responsible and ethically sound future.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
2.
Zurück zum Zitat L. Floridi, M. Taddeo, What is data ethics? Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 374(2083), 20160360 (2016)CrossRef L. Floridi, M. Taddeo, What is data ethics? Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 374(2083), 20160360 (2016)CrossRef
3.
Zurück zum Zitat A. Jobin, M. Ienca, E. Vayena, The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1(9), 389–399 (2019)CrossRef A. Jobin, M. Ienca, E. Vayena, The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1(9), 389–399 (2019)CrossRef
4.
Zurück zum Zitat L. Taylor, N. Purtova, What is responsible and sustainable data science? Big Data & Soc. 6(2), 2053951719858114 (2019)CrossRef L. Taylor, N. Purtova, What is responsible and sustainable data science? Big Data & Soc. 6(2), 2053951719858114 (2019)CrossRef
5.
Zurück zum Zitat W.J. Von Eschenbach, Transparency and the black box problem: why we do not trust AI. Philos. & Technol. 34(4), 1607–1622 (2021)CrossRef W.J. Von Eschenbach, Transparency and the black box problem: why we do not trust AI. Philos. & Technol. 34(4), 1607–1622 (2021)CrossRef
6.
Zurück zum Zitat N. Burkart, M.F. Huber, A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 70, 245–317 (2021)MathSciNetCrossRef N. Burkart, M.F. Huber, A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 70, 245–317 (2021)MathSciNetCrossRef
7.
Zurück zum Zitat S. Garfinkel, J. Matthews, S.S. Shapiro, J.M. Smith, Toward algorithmic transparency and accountability. Commun. ACM 60(9), 5–5 (2017)CrossRef S. Garfinkel, J. Matthews, S.S. Shapiro, J.M. Smith, Toward algorithmic transparency and accountability. Commun. ACM 60(9), 5–5 (2017)CrossRef
8.
Zurück zum Zitat M. Pushkarna, A. Zaldivar, O. Kjartansson, Data cards: purposeful and transparent dataset documentation for responsible AI, in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 1776–1826 M. Pushkarna, A. Zaldivar, O. Kjartansson, Data cards: purposeful and transparent dataset documentation for responsible AI, in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 1776–1826
9.
Zurück zum Zitat C. Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Mach. Intell. 1(5), 206–215 (2019)CrossRef C. Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Mach. Intell. 1(5), 206–215 (2019)CrossRef
10.
Zurück zum Zitat C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova, C. Zhong, Interpretable machine learning: fundamental principles and 10 grand challenges. Stat. Surv. 16, 1–85 (2022)MathSciNetCrossRef C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova, C. Zhong, Interpretable machine learning: fundamental principles and 10 grand challenges. Stat. Surv. 16, 1–85 (2022)MathSciNetCrossRef
11.
Zurück zum Zitat S.M. Lundberg, S.I. Lee, A unified approach to interpreting model predictions, in Advances in Neural Information Processing Systems 30 (2017) S.M. Lundberg, S.I. Lee, A unified approach to interpreting model predictions, in Advances in Neural Information Processing Systems 30 (2017)
12.
Zurück zum Zitat M.T. Ribeiro, S. Singh, C. Guestrin, "Why should i trust you?" Explaining the predictions of any classifier, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016), pp. 1135–1144 M.T. Ribeiro, S. Singh, C. Guestrin, "Why should i trust you?" Explaining the predictions of any classifier, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016), pp. 1135–1144
13.
Zurück zum Zitat T. Miller, Explanation in artificial intelligence: insights from the social sciences. Artif. intell. 267, 1–38 (2019)MathSciNetCrossRef T. Miller, Explanation in artificial intelligence: insights from the social sciences. Artif. intell. 267, 1–38 (2019)MathSciNetCrossRef
14.
Zurück zum Zitat M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, ... , T. Gebru, Model cards for model reporting, in Proceedings of the Conference on Fairness, Accountability, and Transparency (2019), pp. 220–229 M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, ... , T. Gebru, Model cards for model reporting, in Proceedings of the Conference on Fairness, Accountability, and Transparency (2019), pp. 220–229
15.
Zurück zum Zitat S. Mitchell, E. Potash, S. Barocas, A. D’Amour, K. Lum, Algorithmic fairness: choices, assumptions, and definitions. Ann. Rev. Stat. Appl. 8, 141–163 (2021)MathSciNetCrossRef S. Mitchell, E. Potash, S. Barocas, A. D’Amour, K. Lum, Algorithmic fairness: choices, assumptions, and definitions. Ann. Rev. Stat. Appl. 8, 141–163 (2021)MathSciNetCrossRef
16.
Zurück zum Zitat S.A. Friedler, C. Scheidegger, S. Venkatasubramanian, The (im) possibility of fairness: different value systems require different mechanisms for fair decision making. Commun. ACM 64(4), 136–143 (2021)CrossRef S.A. Friedler, C. Scheidegger, S. Venkatasubramanian, The (im) possibility of fairness: different value systems require different mechanisms for fair decision making. Commun. ACM 64(4), 136–143 (2021)CrossRef
17.
Zurück zum Zitat Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013, May). Learning fair representations. In International conference on machine learning (pp. 325-333). PMLR Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013, May). Learning fair representations. In International conference on machine learning (pp. 325-333). PMLR
18.
Zurück zum Zitat G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, K.Q. Weinberger, On fairness and calibration, in Advances in Neural Information Processing Systems 30 (2017) G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, K.Q. Weinberger, On fairness and calibration, in Advances in Neural Information Processing Systems 30 (2017)
19.
Zurück zum Zitat A.N. Carey, X. Wu, The causal fairness field guide: perspectives from social and formal sciences. Front. Big Data 5, 892837 (2022)CrossRef A.N. Carey, X. Wu, The causal fairness field guide: perspectives from social and formal sciences. Front. Big Data 5, 892837 (2022)CrossRef
21.
Zurück zum Zitat P. Laskov, R. Lippmann, Machine learning in adversarial environments. Mach. Learn. 81, 115–119 (2010)CrossRef P. Laskov, R. Lippmann, Machine learning in adversarial environments. Mach. Learn. 81, 115–119 (2010)CrossRef
22.
Zurück zum Zitat S. Fort, J. Ren, B. Lakshminarayanan, Exploring the limits of out-of-distribution detection, Advances in Neural Information Processing Systems 34 (2021), pp. 7068–7081 S. Fort, J. Ren, B. Lakshminarayanan, Exploring the limits of out-of-distribution detection, Advances in Neural Information Processing Systems 34 (2021), pp. 7068–7081
23.
Zurück zum Zitat J. Mena, O. Pujol, J. Vitria, A survey on uncertainty estimation in deep learning classification systems from a Bayesian perspective. ACM Comput. Surv. (CSUR) 54(9), 1–35 (2021)CrossRef J. Mena, O. Pujol, J. Vitria, A survey on uncertainty estimation in deep learning classification systems from a Bayesian perspective. ACM Comput. Surv. (CSUR) 54(9), 1–35 (2021)CrossRef
24.
Zurück zum Zitat A. Subbaswamy, B. Chen, S. Saria, A unifying causal framework for analyzing dataset shift-stable learning algorithms. J. Causal Inf. 10(1), 64–89 (2022)MathSciNetCrossRef A. Subbaswamy, B. Chen, S. Saria, A unifying causal framework for analyzing dataset shift-stable learning algorithms. J. Causal Inf. 10(1), 64–89 (2022)MathSciNetCrossRef
Metadaten
Titel
Responsible Data Science
verfasst von
Laura Igual
Santi Seguí
Copyright-Jahr
2024
DOI
https://doi.org/10.1007/978-3-031-48956-3_12

Premium Partner