Skip to main content
SpringerLink
Log in

Explainable, Interpretable, Trustworthy, Responsible, Ethical, Fair, Verifiable AI... What’s Next?

  • Conference paper
  • First Online:
Advances in Databases and Information Systems (ADBIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13389))

Included in the following conference series:

Abstract

Artificial Intelligence plays an increasingly important role in many knowledge fields: computer science, technology, and other sciences such as health care, one of its most compelling applications. Artificial Intelligence has impacted arts, linguistics, law, sociology, society, and everyday lives. We are demanding many properties from the products of Artificial Intelligence: users of their application fields need trust and ask for fairness, accountability, and privacy. We overview the desired properties and recall the technology that enables Artificial Intelligence to satisfy them.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  2. AI Fairness 360. IBM (2022). https://aif360.mybluemix.net

  3. Awad, E., et al.: The moral machine experiment. Nature 563(7729), 59–64 (2018). https://doi.org/10.1038/s41586-018-0637-6

    Article  Google Scholar 

  4. Bennetot, A., et al.: A practical tutorial on explainable AI techniques. arXiv preprint arXiv:2111.14260 (2021)

  5. Bojarski, M., et al.: Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911 (2017)

  6. Chouldechova, A., Roth, A.: The frontiers of fairness in machine learning. arXiv preprint arXiv:1810.08810 (2018)

  7. Dastin, J.: Amazon scraps secret AI recruiting tool that showed bias against women. In: Ethics of Data and Analytics, pp. 296–299. Auerbach Publications (2018)

    Google Scholar 

  8. Dreossi, T., et al.: VerifAI: a toolkit for the formal design and analysis of artificial intelligence-based systems. In: Dillig, I., Tasiran, S. (eds.) CAV 2019. LNCS, vol. 11561, pp. 432–442. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25540-4_25

    Chapter  Google Scholar 

  9. Dignum, V.: Responsible artificial intelligence: designing AI for human values (2017)

    Google Scholar 

  10. Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)

  11. Du, M., Liu, N., Hu, X.: Techniques for interpretable machine learning. Commun. ACM 63(1), 68–77 (2019). https://doi.org/10.1145/3359786

    Article  Google Scholar 

  12. Fremont, D.J., Dreossi, T., Ghosh, S., Yue, X., Sangiovanni-Vincentelli, A.L., Seshia, S.A.: Scenic: a language for scenario specification and scene generation. In: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2019, pp. 63–78. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3314221.3314633

  13. Gunning, D., Aha, D.: Darpa’s explainable artificial intelligence (XAI) program. AI Mag. 40(2), 44–58 (2019)

    Google Scholar 

  14. Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: International Conference on Machine Learning, pp. 1885–1894. PMLR (2017)

    Google Scholar 

  15. Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable AI: a review of machine learning interpretability methods. Entropy 23(1), 18 (2020)

    Article  Google Scholar 

  16. Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31–57 (2018)

    Article  Google Scholar 

  17. OECD Policy Observatory: From principles to practice: tools for implementing trustworthy AI. OECD (2022). https://oecd.ai/en/tools

  18. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson, Hoboken (2009)

    MATH  Google Scholar 

  19. Seshia, S.A., Sadigh, D., Sastry, S.S.: Toward verified artificial intelligence. Commun. ACM 65(7), 46–55 (2022). https://doi.org/10.1145/3503914

    Article  Google Scholar 

  20. Srinivasan, R., Chander, A.: Biases in AI systems. Commun. ACM 64(8), 44–49 (2021). https://doi.org/10.1145/3464903

    Article  Google Scholar 

  21. Xu, W.: Toward human-centered AI: a perspective from human-computer interaction. Interactions 26(4), 42–46 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Torino, Turin, Italy

    Rosa Meo, Roberto Nai & Emilio Sulis

Authors
  1. Rosa Meo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roberto Nai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emilio Sulis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rosa Meo .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Meo, R., Nai, R., Sulis, E. (2022). Explainable, Interpretable, Trustworthy, Responsible, Ethical, Fair, Verifiable AI... What’s Next?. In: Chiusano, S., Cerquitelli, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2022. Lecture Notes in Computer Science, vol 13389. Springer, Cham. https://doi.org/10.1007/978-3-031-15740-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15740-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15739-4

  • Online ISBN: 978-3-031-15740-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation