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AI Explainability, Interpretability, Fairness, and Privacy: An Integrative Review of Reviews

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Artificial Intelligence in HCI (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14050))

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Abstract

This integrative review incorporates findings from reviews on AI explainability, interpretability, fairness, and privacy to synthesize and understand the concepts. There is synergy between these concepts. Increasing explainability, interpretability, and privacy might help increase fairness. Explainability is necessary to ensure AI is interpretable, fair, and private AI. AI cannot be fair if it is incomprehensible or does not involve consent. Explainable and interpretable AI affords transparency to help counteract biases. This review integrates knowledge and applicability of results of significant studies to inform practice. Human and non-human coding was used for interpretation and analysis. A total of 42 reviews were included in the integrative review. The four domains converged often regarding the need for human-centeredness, trustworthiness, and transparency. Although real-world implementation was a persistent theme for all domains, global models, transfer learning, neural and genetic networks, and machine and federated learning were the strategies most mentioned. These strategies rely on metaphorical and deductive logic that risks underfitting, overfitting, and untethering from real-world grounding and complexity. The review significantly contributes to new thinking by integrating all conceptual findings. Overall, consensus definitions are needed on all terms, as are multi-disciplinary and multi-perspectival analyses.

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References

  1. Gandhi, M.: What exactly is meant by explainability and interpretability of AI? Medium. https://medium.com/analytics-vidhya/what-exactly-is-meant-by-explainability-and-interpretability-of-ai-bcea30ca1e56. Accessed 15 Feb 2023

  2. The Royal Society, Explainable AI: The Basics, https://royalsociety.org/-/media/policy/projects/explainable-ai/AI-and-interpretability-policy-briefing.pdf. Accessed 15 Feb 2023

  3. IBM, Explainable AI (XAI). https://www.ibm.com/watson/explainable-ai (2023). Accessed 15 Feb 2023

  4. Ceurstemont, S.: Finding the Fairness in AI. Communications of the ACM. https://cacm.acm.org/news/261047-finding-the-fairness-in-ai/fulltext. Accessed 15 Feb 2023

  5. Smith, G., Kohli, N., Rustagi, I.: What does “fairness” mean for machine learning systems? Center for Equity, Gender & Leadership (EGAL). Berkeley Haas. https://haas.berkeley.edu/wp-content/uploads/What-is-fairness_-EGAL2.pdf. Accessed 15 Feb 2023

  6. Koerner, K.: Privacy and Responsible AI. The Privacy Advisor. https://iapp.org/news/a/privacy-and-responsible-ai/. Accessed 15 Feb 2023

  7. Roundtree, A.: ANT ethics in professional communication: an integrative review. Am. Commun. J. 22(1), 1–13 (2020)

    Google Scholar 

  8. Roundtree, A.: Ethics and facial recognition technology: an integrative review. In: 3rd World Symposium on Artificial Intelligence, pp. 10–19. IEEE, New York (2021)

    Google Scholar 

  9. Araujo, T.: Automated decision-making fairness in an AI-driven world: public perceptions, hopes and concerns. Digital Communication Methods Lab (2018)

    Google Scholar 

  10. Hong, S.J., Cho, H.: Privacy management and health information sharing via contact tracing during the COVID-19 pandemic: a hypothetical study on AI-based technologies. Health Commun. 38, 913–924 (2021)

    Article  Google Scholar 

  11. Roundtree, A.K.: Computer Simulation, Rhetoric, and the Scientific Imagination: How Virtual Evidence Shapes Science in the Making and in the News. Lexington Books, Lanham (2013)

    Google Scholar 

  12. O’Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., Ananiadou, S.: Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst. Rev. 4(1), 1–22 (2015)

    Google Scholar 

  13. Albahri, A.S.: A systematic review of trustworthy and explainable artificial intelligence in healthcare: assessment of quality, bias risk, and data fusion. Inf. Fusion (2023). https://doi.org/10.1016/j.inffus.2023.03.008

    Article  Google Scholar 

  14. Alsaigh, R., Mehmood, R., Katib, I.: AI explainability and governance in smart energy systems: a review. arXiv preprint arXiv:2211.00069 (2022)

  15. Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. fusion 58, 82–115 (2020)

    Article  Google Scholar 

  16. Cabiddu, F., Moi, L., Patriotta, G., Allen, D.G.: Why do users trust algorithms? A review and conceptualization of initial trust and trust over time. Eur. Manag. J. 40, 685–706 (2022)

    Article  Google Scholar 

  17. Chazette, L., Brunotte, W., Speith, T.: Explainable software systems: from requirements analysis to system evaluation. Requirements Eng. 27, 457–487 (2022). https://doi.org/10.1007/s00766-022-00393-5

  18. Chen, H., Gomez, C., Huang, C.M., Unberath, M.: Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review. Digit. Med. 5(1), 156–163 (2022)

    Article  Google Scholar 

  19. Chou, Y.L., Moreira, C., Bruza, P., Ouyang, C., Jorge, J.: Counterfactuals and causability in explainable artificial intelligence: theory, algorithms, and applications. Inform. Fusion 81, 59–83 (2022)

    Article  Google Scholar 

  20. Dey, S., et al.: Human-centered explainability for life sciences, healthcare, and medical informatics. Patterns 3(5), 100493 (2022)

    Article  Google Scholar 

  21. Gevaert, C.M.: Explainable AI for earth observation: a review including societal and regulatory perspectives. Int. J. Appl. Earth Obs. Geoinformation 112, 102869 (2022). https://doi.org/10.1016/j.jag.2022.102869

    Article  Google Scholar 

  22. Groen, A.M., Kraan, R., Amirkhan, S.F., Daams, J.G., Maas, M.: A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: limited use of explainable AI? Eur. J. Radiol. 157, 110592 (2022). https://doi.org/10.1016/j.ejrad.2022.110592

    Article  Google Scholar 

  23. Hall, O., Ohlsson, M., Rögnvaldsson, T.: A review of explainable AI in the satellite data, deep machine learning, and human poverty domain. Patterns 3(10), 100600 (2022)

    Article  Google Scholar 

  24. Bahalul Haque, A.K.M., Najmul Islam, A.K.M., Mikalef, P.: Explainable Artificial Intelligence (XAI) from a user perspective: a synthesis of prior literature and problematizing avenues for future research. Technol. Forecast. Soc. Chang. 186, 122120 (2023). https://doi.org/10.1016/j.techfore.2022.122120

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Loh, H.W., Ooi, C.P., Seoni, S., Barua, P.D., Filippo Molinari, U., Acharya, R.: Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011–2022). Comput. Methods Programs Biomed. 226, 107161 (2022). https://doi.org/10.1016/j.cmpb.2022.107161

    Article  Google Scholar 

  27. Markus, A.F., Kors, J.A., Rijnbeek, P.R.: The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. J. Biomed. Inform. 113, 103655 (2021)

    Article  Google Scholar 

  28. Mohseni, S., Zarei, N., Ragan, E.D.: A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Trans. Interact. Intell. Syst. (TiiS) 11(3–4), 1–45 (2021)

    Google Scholar 

  29. Nauta, M., et al.: From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI. arXiv preprint arXiv:2201.08164 (2022)

  30. Nazir, S., Dickson, D.M., Akram, M.U.: Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks. Comput. Biol. Med. 156, 106668 (2023). https://doi.org/10.1016/j.compbiomed.2023.106668

    Article  Google Scholar 

  31. Okolo, C.T., Dell N., Vashistha, A.: Making AI explainable in the global south: a systematic review. In: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) Jun 29, pp. 439–452 (2022)

    Google Scholar 

  32. Saleem, R., Yuan, B., Kurugollu, F., Anjum, A., Liu, L.: Explaining deep neural networks: a survey on the global interpretation methods. Neurocomputing 513, 165–180 (2022)

    Article  Google Scholar 

  33. Tiddi, I., Schlobach, S.: Knowledge graphs as tools for explainable machine learning: A survey. Artif. Intell. 302, 103627 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  34. Vo, T.H., Nguyen, N.T.K., Kha, Q.H., Le, N.Q.K.: On the road to explainable AI in drug-drug interactions prediction: a systematic review. Comput. Struct. Biotechnol. J. 20, 2112–2123 (2022)

    Article  Google Scholar 

  35. Yang, G., Ye, Q., Xia, J.: Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond. Inf. Fusion 77, 29–52 (2022)

    Article  Google Scholar 

  36. McCrindle, B., Zukotynski, K., Doyle, T.E., Noseworthy, M.D.: A radiology-focused review of predictive uncertainty for AI interpretability in computer-assisted segmentation. Radiol. Artif. Intell. 3(6), e210031 (2021)

    Article  Google Scholar 

  37. Salahuddin, Z., Woodruff, H.C., Chatterjee, A., Lambin, P.: Transparency of deep neural networks for medical image analysis: a review of interpretability methods. Comput. Biol. Med. 140, 105111 (2022)

    Article  Google Scholar 

  38. Tomsett, R., et al.: Rapid trust calibration through interpretable and uncertainty-aware AI. Patterns 1(4), 100049 (2020)

    Article  Google Scholar 

  39. Baleis, J., Keller, B., Starke, C., Marcinkowski, F.: Cognitive and emotional response to fairness in AI–A systematic review (2019)

    Google Scholar 

  40. Birzhandi, P., Cho, Y.S.: Application of fairness to healthcare, organizational justice, and finance: a survey. Expert Syst. Appl. 216, 119465 (2022)

    Article  Google Scholar 

  41. Richardson, B., Gilbert, J.E.: A framework for fairness: A systematic review of existing fair AI solutions. arXiv preprint arXiv:2112.05700 (2021)

  42. Rieskamp, J., Hofeditz, L., Mirbabaie, M., Stieglitz, S.: Approaches to improve fairness when deploying AI-based algorithms in hiring–using a systematic literature review to guide future research. In: Hawaii International Conference on System Sciences (2023)

    Google Scholar 

  43. Xivuri, K., Twinomurinzi, H.: A systematic review of fairness in artificial intelligence algorithms. In: Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y.K., Pappas, I., Mäntymäki, M. (eds.) Responsible AI and Analytics for an Ethical and Inclusive Digitized Society. I3E 2021. Lecture Notes in Computer Science, vol. 12896, pp. 271–284 . Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85447-8_24

  44. Aslan, A., Greve, M., Diesterhöft, T.O., Kolbe L.M.: Can Our Health Data Stay Private? A Review and Future Directions for IS Research on Privacy-Preserving AI in Healthcare (2022)

    Google Scholar 

  45. Augustin, Y., Carolus, A., Wienrich, C.: Privacy of AI-based voice assistants: understanding the users’ perspective.  In: Salvendy, G., Wei, J. (eds.) Design, Operation and Evaluation of Mobile Communications. HCII 2022. Lecture Notes in Computer Science, vol. 13337, pp. 309–321. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05014-5_26

  46. Duda, S., Geyer, D., Guggenberger, T., Principato, M., Protschky, D.: A systematic literature review on how to improve the privacy of artificial intelligence using blockchain. In: Pacific Asia Conference on Information Systems, pp. 1–17 (2022)

    Google Scholar 

  47. Giordano, G., Palomba, F., Ferrucci, F.: On the use of artificial intelligence to deal with privacy in IoT systems: a systematic literature review. J. Syst. Softw. 193, 111475 (2022)

    Article  Google Scholar 

  48. Hameed, S.S., Hassan, W.H., Latiff, L.A., Ghabban, F.: A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches. PeerJ Computer Science 7, e414 (2021)

    Article  Google Scholar 

  49. Himeur, Y., Sohail, S.S., Bensaali, F., Amira, A., Alazab, M.: Latest trends of security and privacy in recommender systems: a comprehensive review and future perspectives. Comput. Secur. 118, 102746 (2022)

    Article  Google Scholar 

  50. Mothukuri, V., Parizi, R.M., Pouriyeh, S., Huang, Y., Dehghantanha, A., Srivastava, G.: A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 115, 619–640 (2021)

    Article  Google Scholar 

  51. Smidt, H.J., Jokonya, O.: The challenge of privacy and security when using technology to track people in times of COVID-19 pandemic. Procedia Comp. Sci. 181, 1018–1026 (2021)

    Article  Google Scholar 

  52. Taitingfong, R., et al.: A systematic literature review of Native American and Pacific Islanders’ perspectives on health data privacy in the United States. J. Am. Med. Inform. Assoc. 27(12), 1987–1998 (2020)

    Article  Google Scholar 

  53. Xu, J., et al.: Data-Driven Learning for Data Rights, Data Pricing, and Privacy Computing. Engineering (2023)

    Google Scholar 

  54. Zhang, Yi., Mengjia, Wu., Tian, G.Y., Zhang, G., Jie, Lu.: Ethics and privacy of artificial intelligence: Understandings from bibliometrics. Knowl.-Based Syst. 222, 106994 (2021)

    Article  Google Scholar 

  55. Howard, G.T.: A Glossary of Rhetorical Terms. Accessed Corporation, Bloomington (2018)

    Google Scholar 

  56. Simpson, R.L.: Essentials of Symbolic Logic. Broadview Press, New York (2008)

    MATH  Google Scholar 

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Acknowledgement

This study was conducted with support from a research gift from the NEC Foundation.

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Authors and Affiliations

  1. Texas State University, San Marcos, TX, 78666, USA

    Aimee Kendall Roundtree

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  1. Aimee Kendall Roundtree
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Correspondence to Aimee Kendall Roundtree .

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  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Roundtree, A.K. (2023). AI Explainability, Interpretability, Fairness, and Privacy: An Integrative Review of Reviews. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14050. Springer, Cham. https://doi.org/10.1007/978-3-031-35891-3_19

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  • DOI: https://doi.org/10.1007/978-3-031-35891-3_19

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