Skip to main content Skip to main navigation menu Skip to site footer
Articles
Published: 2024-12-19

A review of caveats and future considerations of research on responsible AI across disciplines

Simon Fraser University
Simon Fraser University
artificial intelligence AI responsible AI education review

Abstract

Research on responsible AI has boomed over the past few years. Yet, an understanding of the responsible AI research across primary disciplines is missing in the literature. Through a review of 1224 studies across 7 databases, a total of 177 studies were included for a review of responsible AI across the emerging areas of Business, Engineering, Governance, Health, Science, and Social Science. Using grounded theory and axial and open coding, we extracted and summarized areas, principles, research designs, challenges, findings, and trends in responsible AI research across the emerging areas. The contribution of this work lies in presenting the caveats and future considerations of research on responsible AI holistically and across primary disciplines in higher education.

Metrics

Metrics Loading ...

References

  1. Abeywickrama, D. B., Cirstea, C., Ramchurn, S. D., & IEEE. (2019). Model Checking Human-Agent Collectives for Responsible AI. In 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (Issues 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)). https://doi.org/10.1109/ro-man46459.2019.8956429 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  2. Agarwal, S., & Mishra, S. (2021). Responsible AI: Implementing Ethical and Unbiased Algorithms. In Responsible AI: Implementing Ethical and Unbiased Algorithms. https://doi.org/10.1007/978-3-030-76860-7
  3. Akbarighatar, P., Pappas, I., & Vassilakopoulou, P. (2023). A sociotechnical perspective for responsible AI maturity models: Findings from a mixed-method literature review. International Journal of Information Management Data Insights, 3(2), 100193. https://doi.org/https://doi.org/10.1016/j.jjimei.2023.100193
  4. Al-Dhaen, F., Hou, J., Rana, N. P., & Weerakkody, V. (2021). Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare. Information Systems Frontiers, 1–20.
  5. Al-Hwsali, A., Alsaadi, B., Abdi, N., Khatab, S., Alzubaidi, M., Solaiman, B., & Househ, M. (2023). Scoping Review: Legal and Ethical Principles of Artificial Intelligence in Public Health...21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), July 1-3, 2023, Athens, Greece. Studies in Health Technology & Informatics, 305, 640–643. https://doi.org/10.3233/SHTI230579
  6. Al Hashlamoun, N., Al Barghuthi, N., & Tamimi, H. (2023). Exploring the Intersection of AI and Sustainable Computing: Opportunities, Challenges, and a Framework for Responsible Applications. 2023 9th International Conference on Information Technology Trends, ITT 2023, 220–225. https://doi.org/10.1109/ITT59889.2023.10184228
  7. Alam, A. (2023). Developing a Curriculum for Ethical and Responsible AI: A University Course on Safety, Fairness, Privacy, and Ethics to Prepare Next Generation of AI Professionals. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 171, pp. 879–894). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-1767-9_64
  8. Aler Tubella, A., Mora-Cantallops, M., & Nieves, J. C. (2024). How to teach responsible AI in Higher Education: challenges and opportunities. Ethics & Information Technology, 26(1), 1–14. https://doi.org/10.1007/s10676-023-09733-7
  9. Alfrink, K., Keller, I., Doorn, N., Kortuem, G., & ACM. (2023). Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute. Proceedings Of The 2023 Chi Conference on Human Factors in Computing Systems, CHI 2023, CHI conference on Human Factors in Computing Systems (CHI). https://doi.org/10.1145/3544548.3580984 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  10. Alibašić, H. (2023). Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis. FinTech, 2(3), 430–443. https://doi.org/10.3390/fintech2030024
  11. Anagnostou, M., Karvounidou, O., Katritzidaki, C., Kechagia, C., Melidou, K., Mpeza, E., Konstantinidis, I., Kapantai, E., Berberidis, C., Magnisalis, I., & Peristeras, V. (2022). Characteristics and challenges in the industries towards responsible AI: a systematic literature review. Ethics & Information Technology, 24(3), 1–18. https://doi.org/10.1007/s10676-022-09634-1 WE - Social Science Citation Index (SSCI) WE - Arts & Humanities Citation Index (A&HCI)
  12. Ansari, A., Hoffmann, A. L., Gurses, S., Sloane, M., Vasquez, M. A., & Pearl, Z. (2021). Technology, equity and social justice roundtable. In C. B., S. K.A., P. Z., D. R., & L. H.A. (Eds.), 2021 IEEE International Symposium on Society and Technology, ISTAS 2021 (Vols. 2021-Octob). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISTAS52410.2021.9629208
  13. Arora, A., Vats, P., Tomer, N., Kaur, R., Saini, A. K., Shekhawat, S. S., & Roopak, M. (2024). Data-Driven Decision Support Systems in E-Governance: Leveraging AI for Policymaking. Lecture Notes in Networks and Systems, 844, 229–243. https://doi.org/10.1007/978-981-99-8479-4_17
  14. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., Herrera, F., Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., … Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/https://doi.org/10.1016/j.inffus.2019.12.012
  15. Arya, A., Roy, S., & Jonnala, S. (2023). An Ensemble-based approach for assigning text to correct Harmonized system code. 2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023, 35–41. https://doi.org/10.1109/AISC56616.2023.10085512
  16. Asif, R., Hassan, S. R., & Parr, G. (2023). Integrating a Blockchain-Based Governance Framework for Responsible AI. Future Internet, 15(3), 97. https://doi.org/10.3390/fi15030097
  17. Ayoubi, H., Tabaa, Y., & El Kharrim, M. (2023). Artificial Intelligence in Green Management and the Rise of Digital Lean for Sustainable Efficiency. E3S Web of Conferences, 412. https://doi.org/10.1051/e3sconf/202341201053
  18. Azafrani, R., & Gupta, A. (2023). Bridging the civilian-military divide in responsible AI principles and practices. Ethics & Information Technology, 25(2), 1–5. https://doi.org/10.1007/s10676-023-09693-y
  19. Baeza-Yates, R., & Villoslada, P. (2022). Human vs. Artificial Intelligence. In 2022 IEEE 4th International Conference on Cognitive Machine Intelligence (Issue IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI), pp. 40–48). https://doi.org/10.1109/CogMI56440.2022.00016 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  20. Bani Ahmad, A. Y. A. (2024). Ethical implications of artificial intelligence in accounting: A framework for responsible ai adoption in multinational corporations in Jordan. International Journal of Data and Network Science, 8(1), 401–414. https://doi.org/10.5267/j.ijdns.2023.9.014
  21. Bao, Z. C., Zhang, W. S., Zeng, X. J., Zhao, H. W., Dong, C. H., Nie, Y. M., Liu, Y. E. R., Liu, Y. E. R., & Wu, J. Z. (2023). Software Architecture for Responsible Artificial Intelligence Systems: Practice in the Digitization of Industrial Drawings. Computer, 56(4), 38–49. https://doi.org/10.1109/MC.2023.3240416 WE - Science Citation Index Expanded (SCI-EXPANDED)
  22. Barletta, V. S., Caivano, D., Gigante, D., Ragone, A., Santa Barletta, V., Caivano, D., Gigante, D., Ragone, A., & ACM. (2023). A Rapid Review of Responsible AI frameworks: How to guide the development of ethical AI. 27TH International Conference on Evaluation and Assessment in Software Engineering, EASE 2023, 27th International Conference on Evaluation and Assessment in Software Engineering (EASE), 358–367. https://doi.org/10.1145/3593434.3593478 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  23. Belle, V. (2019). The quest for interpretable and responsible artificial intelligence. Biochemist, 41(5), 16–19. https://doi.org/10.1042/bio04105016
  24. Benjamins, R., Barbado, A., & Sierra, D. (2019). Responsible AI by design in practice. ArXiv Preprint.
  25. Bhattacharyya, R., Raj, N., Mukherjee, S., Choudhury, P., & Gaur, R. (2023). Responsible AI for Sustainable Agriculture: Forecasting Rice Yield to Combat Global Food Insecurity. 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023. https://doi.org/10.1109/ICCCNT56998.2023.10306460
  26. Birks, M., & Mills, J. (2022). Grounded theory: A practical guide.
  27. Blockeel, H., Devos, L., Frénay, B., Nanfack, G., & Nijssen, S. (2023). Decision trees: from efficient prediction to responsible AI. Frontiers in Artificial Intelligence, 6, 1124553. https://doi.org/10.3389/frai.2023.1124553
  28. Borda, A., Molnar, A., Neesham, C., & Kostkova, P. (2022). Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health. Applied Sciences-Basel, 12(8). https://doi.org/10.3390/app12083890
  29. Boulanin, V., & Lewis, D. A. (2023). Responsible reliance concerning development and use of AI in the military domain. Ethics & Information Technology, 25(1), 1–5. https://doi.org/10.1007/s10676-023-09691-0 WE - Social Science Citation Index (SSCI) WE - Arts & Humanities Citation Index (A&HCI)
  30. Brand, D. J. (2022). Responsible Artificial Intelligence in Government: Development of a Legal Framework for South Africa. EJournal of EDemocracy & Open Government, 14(1), 130–150. https://doi.org/10.29379/jedem.v14i1.678
  31. Brumen, B., Göllner, S., & Tropmann-Frick, M. (2023). Aspects and Views on Responsible Artificial Intelligence. In G. Nicosia, V. Ojha, E. LaMalfa, G. LaMalfa, P. Pardalos, G. DiFatta, G. Giuffrida, & R. Umeton (Eds.), Machine Learning, Optimization, and Data Science, LOD 2022, PT I (Vol. 13810, Issues 8th Annual International Conference on Machine Learning, Optimization and Data science (LOD), pp. 384–398). https://doi.org/10.1007/978-3-031-25599-1_29 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  32. Buchholz, J., Lang, B., & Vyhmeister, E. (2022). The development process of Responsible AI: The case of ASSISTANT*. IFAC-PapersOnLine, 55(10), 7–12. https://doi.org/https://doi.org/10.1016/j.ifacol.2022.09.360
  33. Buijsman, S. (2023). Why and How Should We Explain AI? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13500 LNAI, 196–215. https://doi.org/10.1007/978-3-031-24349-3_11
  34. Cachat-Rosset, G., & Klarsfeld, A. (2023). Diversity, equity, and inclusion in artificial intelligence: An evaluation of guidelines. Applied Artificial Intelligence, 37(1). https://doi.org/10.1080/08839514.2023.2176618
  35. Cai, Z. N., Ren, B. J., Ma, R. H., Guan, H. B., Tian, M. K., & Wang, Y. (2023). Guardian: A Hardware-Assisted Distributed Framework to Enhance Deep Learning Security. IEEE Transactions on Computational Social Systems, 10(6), 3012–3020. https://doi.org/10.1109/TCSS.2023.3262289
  36. Calvo, A., Ortiz, N., Espinosa, A., Dimitrievikj, A., Oliva, I., Guijarro, J., & Sidiqqi, S. (2023). Safe AI: Ensuring Safe and Responsible Artificial Intelligence. NIC Cybersecurity Conference (JNIC), 1–4.
  37. Calvo, R. A., Peters, D., Vold, K., & Ryan, R. M. (2020). Supporting Human Autonomy in AI Systems: A Framework for Ethical Enquiry. In Philosophical Studies Series (Vol. 140, pp. 31–54). https://doi.org/10.1007/978-3-030-50585-1_2
  38. Chang, Y. L., & Ke, J. (2023). Socially responsible artificial intelligence empowered people analytics: A novel framework towards sustainability. Human Resource Development Review, 15344843231200930.
  39. Charalampakos, F., Tsouparopoulos, T., Papageorgiou, Y., Bologna, G., Panisson, A., Perotti, A., Koutsopoulos, I., & IEEE. (2023). Research Challenges in Trustworthy Artificial Intelligence and Computing for Health: The Case of the PRE-ACT project. 2023 Joint European Conference on Networks and Communications & 6g Summit, Eucnc/6g Summit, Joint European Conference on Networks and Communications / 6G Summit (EuCNC/6G Summit), 629–634. https://doi.org/10.1109/EUCNC/6GSUMMIT58263.2023.10188239 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  40. Chen, H., Chan-Olmsted, S., & Thai, M. (2023). Culture Sensitivity and Information Access: A Qualitative Study among Ethnic Groups. Qualitative Report, 28(8), 2504–2522. https://doi.org/10.46743/2160-3715/2023.5981 WE - Emerging Sources Citation Index (ESCI)
  41. Cheng, L., Varshney, K. R., & Liu, H. (2021). Socially responsible AI algorithms: Issues, purposes, and challenges. Journal of Artificial Intelligence Research, 71, 1137–1181. https://doi.org/10.1613/JAIR.1.12814
  42. Christos, S. C., Panagiotis, T., & Christos, G. (2020). Combined multi-layered big data and responsible AI techniques for enhanced decision support in Shipping. 2020 International Conference on Decision Aid Sciences and Application, DASA 2020, 669–673. https://doi.org/10.1109/DASA51403.2020.9317030
  43. Church, K., Schoene, A., Ortega, J. E., Chandrasekar, R., & Kordoni, V. (2022). Emerging trends: Unfair, biased, addictive, dangerous, deadly, and insanely profitable. Natural Language Engineering, 29(2), 483–508. https://doi.org/10.1017/S1351324922000481
  44. Clarke, R. (2019). Principles and business processes for responsible AI. Computer Law & Security Review, 35(4), 410–422. https://doi.org/10.1016/j.clsr.2019.04.007 WE - Social Science Citation Index (SSCI)
  45. Conrad, C. (1982). Grounded theory: An alternative approach to research in higher education. The Review of Higher Education, 5(4), 239–249.
  46. Constantinescu, M., Voinea, C., Uszkai, R., & Vica, C. (2021). Understanding responsibility in Responsible AI. Dianoetic virtues and the hard problem of context. Ethics and Information Technology, 23(4), 803–814. https://doi.org/10.1007/s10676-021-09616-9
  47. Contractor, D., McDuff, D., Haines, J. K., Lee, J., Hines, C., Hecht, B., ... & Li, H. (2022). Behavioral use licensing for responsible AI. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 778–788.
  48. Crockett, K., Colyer, E., Gerber, L., & Latham, A. (2023). Building Trustworthy AI Solutions: A Case for Practical Solutions for Small Businesses. IEEE Transactions on Artificial Intelligence, 4(4), 778–791. https://doi.org/10.1109/TAI.2021.3137091
  49. De, A., Gudipudi, S. S., Panchanan, S., & Desarkar, M. S. (2023). ComplAI: Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models. Proceedings of the ACM Symposium on Applied Computing, 1096–1099. https://doi.org/10.1145/3555776.3577771
  50. De Brito Duarte, R. (2023). Towards Responsible AI: Developing Explanations to Increase Human-AI Collaboration. Frontiers in Artificial Intelligence and Applications, 368, 470–482. https://doi.org/10.3233/FAIA230126
  51. Delecraz, S., Eltarr, L., Becuwe, M., Bouxin, H., Boutin, N., & Oullier, O. (2022). Responsible Artificial Intelligence in Human Resources Technology: An innovative inclusive and fair by design matching algorithm for job recruitment purposes. Journal of Responsible Technology, 11, 100041. https://doi.org/https://doi.org/10.1016/j.jrt.2022.100041
  52. Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y. K., Mäntymäki, M., & Pappas, I. O. (2023). Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI. Information Systems Frontiers, 25(1), 1–7. https://doi.org/10.1007/s10796-022-10365-3
  53. Deshmukh, J., & Srinivasa, S. (2022). Computational Transcendence: Responsibility and agency. Frontiers in Robotics and AI, 9, 977303. https://doi.org/10.3389/frobt.2022.977303
  54. Deshpande, A., & Sharp, H. (2022). Responsible AI Systems: Who are the Stakeholders? 2022 AAAI/ACM Conference on AI, Ethics, and Society, 227–236.
  55. Dholakia, A., Ellison, D., Hodak, M., & Dutta, D. (2023). Benchmarking Considerations for Trustworthy and Responsible AI (Panel). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13860 LNCS, 110–119. https://doi.org/10.1007/978-3-031-29576-8_8
  56. Dignum, V. (2019). Responsible artificial intelligence: how to develop and use AI in a responsible way (Vol. 1). Cham: Springer.
  57. Dishop, C. R., Olenick, J., & DeShon, R. P. (2020). Principles for taking a dynamic perspective. Handbook on the Temporal Dynamics of Organizational Behavior, 26–43.
  58. Domínguez Figaredo, D., Stoyanovich, J., Figaredo, D. D., & Stoyanovich, J. (2023). Responsible AI literacy: A stakeholder-first approach. Big Data & Society, 10(2). https://doi.org/10.1177/20539517231219958
  59. Dominique, B., El Maghraoui, K., Piorkowski, D., Herger, L., Maghraoui, K. E., Piorkowski, D., & Herger, L. (2023). FactSheets for Hardware-Aware AI Models: A Case Study of Analog In Memory Computing AI Models. In C. Ardagna, N. Atukorala, C. Chang, R. Chang, J. Fan, G. Fox, S. Helal, Z. Jin, Q. Lu, T. Seceleanu, & S. S. Yau (Eds.), Proceedings - 2023 IEEE International Conference on Software Services Engineering, SSE 2023 (Issue 1st IEEE International Conference on Software Services Engineering (IEEE SSE), pp. 148–158). https://doi.org/10.1109/SSE60056.2023.00029 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  60. Dong, T., Li, S., Chen, G., Xue, M., Zhu, H., & Liu, Z. (2023). RAI2: Responsible Identity Audit Governing the Artificial Intelligence. 30th Annual Network and Distributed System Security Symposium, NDSS 2023. https://doi.org/10.14722/ndss.2023.241012
  61. Dunne, C. (2011). The place of the literature review in grounded theory research. International Journal of Social Research Methodology, 14(2), 111–124.
  62. El-Haddadeh, R., Fadlalla, A., & Hindi, N. M. (2021). Is There a Place for Responsible Artificial Intelligence in Pandemics? A Tale of Two Countries. Information Systems Frontiers, 25(6), 2221–2237. https://doi.org/10.1007/s10796-021-10140-w
  63. Falco, G. (2019). Participatory AI: Reducing AI bias and developing socially responsible AI in smart cities. IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 154–158.
  64. Farina, L. (2022). Artificial Intelligence Systems, Responsibility and Agential Self-Awareness. In V. C. Muller (Ed.), Philosophy and Theory of Artificial Intelligence 2021 (Vol. 63, Issue Philosophy and Theory of Artificial Intelligence (PT-AI), pp. 15–25). https://doi.org/10.1007/978-3-031-09153-7_2 WE - Conference Proceedings Citation Index - Science (CPCI-S) WE - Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)
  65. Faßbender, J. (2021). Particles of a Whole: Design Patterns for Transparent and Auditable AI-Systems. UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, 272–275. https://doi.org/10.1145/3460418.3479345
  66. Feher, K. (2021). Expectation of smart mentality and citizen participation in technology-driven cities. Smart Structures and Systems, 27(3), 435–445. https://doi.org/10.12989/sss.2021.27.3.435
  67. Fenwick, M., Vermeulen, E. P. M., & Corrales, M. (2018). Business and regulatory responses to artificial intelligence: Dynamic regulation, innovation ecosystems and the strategic management of disruptive technology. In Perspectives in Law, Business and Innovation (pp. 81–103). https://doi.org/10.1007/978-981-13-2874-9_4
  68. Ferreira, R. M. F. D., Grilo, A., & Maia, M. J. (2023). A Maturity Model for Industries and Organizations of All Types to Adopt Responsible AI—Preliminary Results. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14115 LNAI, 67–78. https://doi.org/10.1007/978-3-031-49008-8_6
  69. Fosso Wamba, S., Queiroz, M. M., Wamba, S. F., Queiroz, M. M., Fosso Wamba, S., & Queiroz, M. M. (2021). Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions. Information Systems Frontiers, 25(6), 1–16. https://doi.org/10.1007/s10796-021-10142-8
  70. Fritz-Morgenthal, S., Hein, B., & Papenbrock, J. (2022). Financial Risk Management and Explainable, Trustworthy, Responsible AI. Frontiers in Artificial Intelligence, 5, 779799. https://doi.org/10.3389/frai.2022.779799 WE - Emerging Sources Citation Index (ESCI)
  71. GAON, A., & STEDMAN, I. A. N. (2019). A Call to Action: Moving Forward With the Governance of Artificial Intelligence in Canada. Alberta Law Review, 56(4), 1137–1165. http://10.0.113.245/alr2547
  72. Garg, M. (2023). Mental Health Analysis in Social Media Posts: A Survey. Archives of Computational Methods in Engineering : State of the Art Reviews, 30(3), 1819–1842. https://doi.org/10.1007/s11831-022-09863-z
  73. Gavrilova, M. L. (2023). Responsible Artificial Intelligence and Bias Mitigation in Deep Learning Systems. In E. Banissi, H. Siirtola, A. Ursyn, J. M. Pires, N. Datia, K. Nazemi, B. Kovalerchuk, R. Andonie, M. Nakayama, M. Temperini, F. Sciarrone, Q. V Nguyen, M. S. Mabakane, A. Rusu, U. Cvek, M. Trutschl, H. Mueller, R. Francese, F. Bouali, & G. Venturini (Eds.), Proceedings of the International Conference on Information Visualisation (Issues 27th International Conference on Information Visualisation (IV) / 19th International Conference Computer Graphics, Imaging and Visualization (CGiV), pp. 329–333). https://doi.org/10.1109/IV60283.2023.00062 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  74. Gazzaniga, M. (2013). Understanding layers: From neuroscience to human responsibility. ().. Neurosciences and the Human Person: New Perspectives on Human Activities, 1–14.
  75. Gianni, R., Lehtinen, S., Nieminen, M., Gianni, R., Lehtinen, S., & Nieminen, M., Gianni, R., Lehtinen, S., & Nieminen, M. (2022). Governance of Responsible AI: From Ethical Guidelines to Cooperative Policies. Frontiers in Computer Science, 4, 873437. https://doi.org/10.3389/fcomp.2022.873437
  76. Gilbert, Y., Kayanja, E. W., Kalungi, J. E., Kyagaba, J. M., & Marvin, G. (2023). Explainable AI for Black Sigatoka Detection. Lecture Notes in Networks and Systems, 798 LNNS, 181–196. https://doi.org/10.1007/978-981-99-7093-3_12
  77. Giroux, M., Kim, J., Lee, J. C., & Park, J. (2022). Artificial intelligence and declined guilt: Retailing morality comparison between human and AI. Journal of Business Ethics, 178(4), 1027–1041.
  78. Golbin, I., Rao, A. S., Hadjarian, A., & Krittman, D. (2020). Responsible AI: A Primer for the Legal Community. In X. T. Wu, C. Jermaine, L. Xiong, X. H. Hu, O. Kotevska, S. Y. Lu, W. J. Xu, S. Aluru, C. X. Zhai, E. Al-Masri, Z. Y. Chen, & J. Saltz (Eds.), 2020 IEEE International Conference on Big Data (BIG DATA) (Issue 8th IEEE International Conference on Big Data (Big Data), pp. 2121–2126). https://doi.org/10.1109/BigData50022.2020.9377738 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  79. Gold, N. E. (2021). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Genetic Programming and Evolvable Machines, 22(1), 137–139. https://doi.org/10.1007/s10710-020-09394-1
  80. Grammatikos, N. A., Anagnostopoulou, E., Apostolou, D., & Mentzas, G. (2023). A Conversational Digital Assistant for STEM Education. 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023. https://doi.org/10.1109/IISA59645.2023.10345918
  81. Gupta, S., Kamboj, S., & Bag, S. (2021). Role of Risks in the Development of Responsible Artificial Intelligence in the Digital Healthcare Domain. Information Systems Frontiers, 25(6), 2257–2274. https://doi.org/10.1007/s10796-021-10174-0
  82. Gwagwa, A., Kazim, E., Kachidza, P., Hilliard, A., Siminyu, K., Smith, M., & Shawe-Taylor, J. (2021). Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture. Patterns, 2(12), 100381. https://doi.org/https://doi.org/10.1016/j.patter.2021.100381
  83. Hacker, P., & Passoth, J. H. (2022). Varieties of AI Explanations Under the Law. From the GDPR to the AIA, and Beyond. In A. Holzinger, R. Goebel, R. Fong, T. Moon, K. R. Muller, & W. Samek (Eds.), XXAI - BEYOND EXPLAINABLE AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers (Vol. 13200, Issue International Workshop on Beyond Explainable Artificial Intelligence (xxAI), pp. 343–373). https://doi.org/10.1007/978-3-031-04083-2_17 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  84. Hadley, E. (2022). Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, 5029–5038. https://doi.org/10.1109/BigData55660.2022.10020551
  85. Haidar, A. (2024). An Integrative Theoretical Framework for Responsible Artificial Intelligence. International Journal of Digital Strategy, Governance, & Business Transformation (IJDSGBT), 13(1), 1–23. https://doi.org/10.4018/IJDSGBT.334844
  86. Harfouche, A. L., Petousi, V., & Jung, W. (2024). AI ethics on the road to responsible AI plant science and societal welfare. Trends in Plant Science. https://doi.org/10.1016/j.tplants.2023.12.016
  87. Hernández, A. D., & Galanos, V. (2022). A toolkit of dilemmas: Beyond debiasing and fairness formulas for responsible AI/ML. International Symposium on Technology and Society, Proceedings, 2022-Novem. https://doi.org/10.1109/ISTAS55053.2022.10227133
  88. Herrmann, H. (2023). What’s next for responsible artificial intelligence: a way forward through responsible innovation. Heliyon, 9(3), e14379. https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e14379
  89. Heuillet, A., Couthouis, F., Diaz-Rodriguez, N., & Díaz-Rodríguez, N. (2022). Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values. IEEE Computational Intelligence Magazine, 17(1), 59–71. https://doi.org/10.1109/MCI.2021.3129959
  90. Hu, P. (2023). SatAIOps: Revamping the Full Life-Cycle Satellite Network Operations. Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023. https://doi.org/10.1109/NOMS56928.2023.10154334
  91. Hull, C. L. (1943). Principles of behavior: an introduction to behavior theory.
  92. Inuwa-Dutse, I. (2023). FATE in AI: Towards Algorithmic Inclusivity and Accessibility. arXiv preprint. https://doi.org/10.48550/arXiv.2301.01590
  93. Islam, M. R., Sakib, M. K. H., Ulhaq, A., Akter, S., Zhou, J. L., Asirvathamt, D., & Asirvatham, D. (2023). SIDVis: Designing Visual Interactive System for Analyzing Suicide Ideation Detection. In E. Banissi, H. Siirtola, A. Ursyn, J. M. Pires, N. Datia, K. Nazemi, B. Kovalerchuk, R. Andonie, M. Nakayama, M. Temperini, F. Sciarrone, Q. V Nguyen, M. S. Mabakane, A. Rusu, U. Cvek, M. Trutschl, H. Mueller, R. Francese, F. Bouali, & G. Venturini (Eds.), Proceedings of the International Conference on Information Visualisation (Issues 27th International Conference on Information Visualisation (IV) / 19th International Conference Computer Graphics, Imaging and Visualization (CGiV), pp. 384–389). https://doi.org/10.1109/IV60283.2023.00071 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  94. Iwasawa, M., Kobayashi, M., & Otori, K. (2023). Knowledge and attitudes of pharmacy students towards artificial intelligence and the ChatGPT. Pharmacy Education, 23(1), 665–675. https://doi.org/10.46542/pe.2023.231.665675
  95. Jaipuria, N., Stevo, K., Zhang, X. L., Gaopande, M. L., Garcia, I. C., Jain, J., Murali, V. N., & IEEE. (2022). deepPIC: Deep Perceptual Image Clustering For Identifying Bias In Vision Datasets. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 (Issue IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4792–4801). https://doi.org/10.1109/CVPRW56347.2022.00526 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  96. Jawad, M. S., Mahdin, H., Mohammed Alduais, N. A., Hlayel, M., Mostafa, S. A., & Abd Wahab, M. H. (2021). Recent and Future Innovative Artificial Intelligence Services and Fields. Proceedings - ISAMSR 2021: 4th International Symposium on Agents, Multi-Agents Systems and Robotics, 29–32. https://doi.org/10.1109/ISAMSR53229.2021.9567891
  97. Ji, Z. L., Ma, P. C., Wang, S., Li, Y. H., & IEEE. (2023). Causality-Aided Trade-off Analysis for Machine Learning Fairness. 2023 38TH IEEE/ACM International Conference on Automated Software Engineering, ASE, 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), 371–383. https://doi.org/10.1109/ASE56229.2023.00105
  98. Johnson, M., Albizri, A., & Harfouche, A. (2021). Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing. Information Systems Frontiers, 25(6), 2179–2195. https://doi.org/10.1007/s10796-021-10137-5
  99. Jörg, S., Ziethmann, P., & Breuer, S. (2023). MedAIcine: A Pilot Project on the Social and Ethical Aspects of AI in Medical Imaging. Communications in Computer and Information Science, 1832 CCIS, 455–462. https://doi.org/10.1007/978-3-031-35989-7_58
  100. Kamada, T. (2016). The issues of automated driving vehicle and the expectations for 3D integration technology. In 2015 International 3d Systems Integration Conference (3DIC 2015) (Issue IEEE International 3D Systems Integration Conference (3DIC)).
  101. Kapania, S., Siy, O., Clapper, G., Meena, S. P. A., Sambasivan, N., ACM, Sp, A. M., & Sambasivan, N. (2022). “Because AI is 100% right and safe”: User Attitudes and Sources of AI Authority in India. Proceedings of the 2022 Chi Conference on Human Factors in Computing Systems (CHI’ 22), CHI Conference on Human Factors in Computing Systems (CHI). https://doi.org/10.1145/3491102.3517533
  102. Kapania, S., Taylor, A. S., Wang, D., & ACM. (2023). A hunt for the Snark: Annotator Diversity in Data Practices. Proceedings of the 2023 Chi Conference On Human Factors in Computing Systems, CHI 2023, CHI conference on Human Factors in Computing Systems (CHI). https://doi.org/10.1145/3544548.3580645 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  103. Karagianni, C., Paraschou, E., Yfantidou, S., & Vakali, A. (2023). MINDSET: A benchMarking suIte exploring seNsing Data for SElf sTates inference. 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings. https://doi.org/10.1109/DSAA60987.2023.10302638
  104. Karpagam, G. R., Varma, A., Samrddhi, M., & Shri Shivathmika, V. (2022). Understanding, Visualizing and Explaining XAI Through Case Studies. 8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022, 647–654. https://doi.org/10.1109/ICACCS54159.2022.9785199
  105. Knopp, M. I., Warm, E. J., Weber, D., Kelleher, M., Kinnear, B., Schumacher, D. J., Santen, S. A., Mendonça, E., & Turner, L. (2023). AI-Enabled Medical Education: Threads of Change, Promising Futures, and Risky Realities Across Four Potential Future Worlds. JMIR Medical Education, 9(1), e50373. https://doi.org/10.2196/50373
  106. Krafft, P. M., Young, M., Katell, M., Lee, J. E., Narayan, S., Epstein, M., Dailey, D., Herman, B., Tam, A., Guetler, V., Bintz, C., Raz, D., Jobe, P. O., Putz, F., Robick, B., Barghouti, B., & ACM. (2021). An Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists. In Proceedings of the 2021 Acm Conference on Fairness, Accountability, and Transparency, FACCT 2021 (Issue ACM Conference on Fairness, Accountability, and Transparency (FAccT), pp. 772–781). https://doi.org/10.1145/3442188.3445938 WE - Conference Proceedings Citation Index - Science (CPCI-S) WE - Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)
  107. Krarup, T., & Horst, M. (2023). European artificial intelligence policy as digital single market making. BIG DATA & SOCIETY, 10(1). https://doi.org/10.1177/20539517231153811
  108. Kuck, K. (2023). Generative Artificial Intelligence: A Double-Edged Sword. 2023 IEEE IFEES World Engineering Education Forum and Global Engineering Deans Council: Convergence for a Better World: A Call to Action, WEEF-GEDC 2023 - Proceedings. https://doi.org/10.1109/WEEF-GEDC59520.2023.10343638
  109. Kuennen, C. S. (2023). Developing Leaders of Character for Responsible Artificial Intelligence. Journal of Character & Leadership Development (JCLD), 10(3), 52–59. https://doi.org/10.58315/jcld.v10.273
  110. Kumar, K. P., Thiruthuvanathan, M. M., K.K., S., & Chandra, D. R. (2023). Human AI: Explainable and responsible models in computer vision. In Emotional AI and Human-AI Interactions in Social Networking (pp. 237–254). https://doi.org/10.1016/B978-0-443-19096-4.00006-7
  111. Kumar, P., Dwivedi, Y. K., & Anand, A. (2021). Responsible Artificial Intelligence (AI) for Value Formation and Market Performance in Healthcare: the Mediating Role of Patient’s Cognitive Engagement. Information Systems Frontiers, 25(6), 2197–2220. https://doi.org/10.1007/s10796-021-10136-6
  112. Lee, S. U., Fernando, N., Lee, K., & Schneider, J.-G. (2024). A survey of energy concerns for software engineering. Journal of Systems and Software, 210, 111944. https://doi.org/https://doi.org/10.1016/j.jss.2023.111944
  113. Lescano, L. F., Flores, M. L., & Pico, M. P. (2023). Distributed Facial Recognition Facial Recognition in Visual Internet of Things (VIoT) An Intelligent Approach. Journal of Intelligent Systems and Internet of Things, 10(2), 18–23. https://doi.org/10.54216/JISIoT.100202
  114. Lewis, G. A., Echeverría, S., Pons, L., Chrabaszcz, J., & IEEE. (2022). Augur: A Step Towards Realistic Drift Detection in Production ML Systems. In 2022 IEEE/ACM 1ST International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI 2022) (Issue 1st IEEE/ACM International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI), pp. 37–44). https://doi.org/10.1145/3526073.3527590 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  115. Li, C., & Yang, L. (2021). Responsible AI: The Revolution in Governance Technology in China. Proceedings - 2021 2nd International Conference on Artificial Intelligence and Education, ICAIE 2021, 76–80. https://doi.org/10.1109/ICAIE53562.2021.00023
  116. Li, K., Lau, B. P. L., Yuan, X., Ni, W., Guizani, M., & Yuen, C. (2023). Toward Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities. IEEE Internet of Things Journal, 10(24), 21855–21872. https://doi.org/10.1109/JIOT.2023.3302159
  117. Liao, Q. V, Zhang, Y., Luss, R., Doshi-Velez, F., & Dhurandhar, A. (2022). Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 10, 147–159. https://doi.org/10.1609/hcomp.v10i1.21995
  118. Liao, W., Liu, Z., Dai, H., Xu, S., Wu, Z., Zhang, Y., Huang, X., Zhu, D., Cai, H., Li, Q., Liu, T., & Li, X. (2023). Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study. JMIR Medical Education, 9, e48904. https://doi.org/10.2196/48904
  119. Lin, Z. (2024). Towards an ai policy framework in scholarly publishing. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2023.12.002
  120. Liu, J., Chen, H., Shen, J., & Choo, K. R. (2023). FairCompass: Operationalising Fairness in Machine Learning. IEEE Transactions on Artificial Intelligence, 1–10. https://doi.org/10.1109/TAI.2023.3348429
  121. Liu, R., Gupta, S., & Patel, P. (2021). The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health. Information Systems Frontiers, 25(6), 2275–2299. https://doi.org/10.1007/s10796-021-10191-z
  122. Lo, S. K., Liu, Y., Lu, Q. H., Wang, C., Xu, X. W., Paik, H.-Y. Y., & Zhu, L. M. (2023). Toward Trustworthy AI: Blockchain-Based Architecture Design for Accountability and Fairness of Federated Learning Systems. IEEE Internet of Things Journal, 10(4), 3276–3284. https://doi.org/10.1109/JIOT.2022.3144450 WE - Science Citation Index Expanded (SCI-EXPANDED)
  123. Lu, Q., Zhu, L., Xu, X., Whittle, J., Douglas, D., & Sanderson, C., Lu, Q. H., Zhu, L. M., Xu, X. W., Whittle, J., Douglas, D., Sanderson, C., & Soc, I. C. (2022). Software engineering for responsible AI: An empirical study and operationalised patterns. Software Engineering in Practice, ACM/IEEE 44th International Conference on Software Engineering-Software Engineering in Practice (ICSE-SEIP), 241–242. https://doi.org/10.1145/3510457.3513063 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  124. Lu, Q., Luo, Y., Zhu, L., Tang, M., Xu, X., & Whittle, J. (2023). Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible Artificial Intelligence Engineering Approach. IEEE Intelligent Systems, 38(6), 42–51. https://doi.org/10.1109/MIS.2023.3320437
  125. Lukkien, D. R. M., Nap, H. H., Buimer, H. P., Peine, A., Boon, W. P. C., Ket, J. C. F., Minkman, M. M. N., & Moors, E. H. M. (2023). Toward Responsible Artificial Intelligence in Long-Term Care: A Scoping Review on Practical Approaches. Gerontologist, 63(1), 155–168. https://doi.org/10.1093/geront/gnab180
  126. Mahoney, C., Gronvall, P., Huber-Fliflet, N., & Zhang, J. (2022). Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, 2044–2051. https://doi.org/10.1109/BigData55660.2022.10020626
  127. Maki, H. A., Al Mubarak, M., & Bakir, A. (2023). Understanding Artificial Intelligence Through Its Applications and Concerns. In Internet of Things: Vol. Part F1270 (pp. 135–152). https://doi.org/10.1007/978-3-031-35525-7_9
  128. Mallinger, K., & Baeza-Yates, R. (2024). Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design. Applied Sciences-Basel, 14(1). https://doi.org/10.3390/app14010437 WE - Science Citation Index Expanded (SCI-EXPANDED)
  129. Man Li, R. Y., & Crabbe, M. J. C. (2022). Artificial Intelligence Robot Safety: A Conceptual Framework and Research Agenda Based on New Institutional Economics and Social Media. In Current State of Art in Artificial Intelligence and Ubiquitous Cities (pp. 41–61). https://doi.org/10.1007/978-981-19-0737-1_3
  130. Marcoux, A. M., & Martineau, J. T. (2022). From Principled to Applied AI Ethics in Organizations: A Scoping Review. Communications in Computer and Information Science, 1655 CCIS, 641–646. https://doi.org/10.1007/978-3-031-19682-9_81
  131. Maree, C., Modal, J. E., Omlin, C. W., & IEEE. (2020). Towards Responsible AI for Financial Transactions. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (Issue IEEE Symposium Series on Computational Intelligence (IEEE SSCI), pp. 16-21 WE-Conference Proceedings Citation Index).
  132. Maruyama, Y. (2022). Categorical Artificial Intelligence: The Integration of Symbolic and Statistical AI for Verifiable, Ethical, and Trustworthy AI. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13154 LNAI, 127–138. https://doi.org/10.1007/978-3-030-93758-4_14
  133. Marvin, G., Nakatumba-Nabende, J., Hellen, N., & Alam, M. G. R. (2022). Responsible Artificial Intelligence for Preterm Birth Prediction in Vulnerable Populations. Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022. https://doi.org/10.1109/CSDE56538.2022.10089301
  134. Marzouk, M., Zitoun, C., Belghith, O., & Skhiri, S. (2023). The Building Blocks of a Responsible Artificial Intelligence Practice: An Outlook on the Current Landscape. IEEE Intelligent Systems, 38(6), 9–18. https://doi.org/10.1109/MIS.2023.3320438
  135. Meerveld, H. W., Lindelauf, R. H. A., Postma, E. O., & Postma, M. (2023). The irresponsibility of not using AI in the military. Ethics & Information Technology, 25(1), 1–6. https://doi.org/10.1007/s10676-023-09683-0 WE - Social Science Citation Index (SSCI) WE - Arts & Humanities Citation Index (A&HCI)
  136. Merhi, M. I. (2023). An Assessment of the Barriers Impacting Responsible Artificial Intelligence. Information Systems Frontiers, 25(3), 1147–1160. https://doi.org/10.1007/s10796-022-10276-3
  137. Meske, C., Abedin, B., Klier, M., & Rabhi, F., Meske, C., Abedin, B., Klier, M., & Rabhi, F. (2022). Explainable and responsible artificial intelligence. Electronic Markets, 32(4), 2103–2106. https://doi.org/10.1007/s12525-022-00607-2
  138. Methnani, L., Aler Tubella, A., Dignum, V., Theodorou, A., Tubella, A. A., Dignum, V., & Theodorou, A. (2021). Let Me Take Over: Variable Autonomy for Meaningful Human Control. Frontiers in Artificial Intelligence, 4, 737072. https://doi.org/10.3389/frai.2021.737072
  139. Methnani, L., Brännström, M., & Theodorou, A. (2023). Operationalising AI Ethics: Conducting Socio-technical Assessment. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13500 LNAI, 304–321. https://doi.org/10.1007/978-3-031-24349-3_16
  140. Mikalef, P., Conboy, K., Lundström, J. E., & Popovic, A. (2022). Thinking responsibly about responsible AI and “the dark side” of AI. European Journal of Information Systems, 31(3), 257–268. https://doi.org/10.1080/0960085X.2022.2026621
  141. Minkkinen, M., Niukkanen, A., & Mantymaki, M. (2024). What about investors? ESG analyses as tools for ethics-based AI auditing. AI & SOCIETY. https://doi.org/10.1007/s00146-022-01415-0
  142. Minkkinen, M., Zimmer, M. P., & Mäntymäki, M. (2023). Co-Shaping an Ecosystem for Responsible AI: Five Types of Expectation Work in Response to a Technological Frame. Information Systems Frontiers, 25(1), 103–121. https://doi.org/10.1007/s10796-022-10269-2
  143. Morse, J. M. (2016). Tussles, tensions, and resolutions. In Developing grounded theory. Routledge, 13–22.
  144. Morse, S. J. (2006). Moral and legal responsibility and the new neuroscience. Neuroethics: Defining the Issues in Theory, Practice, and Policy, 33–50.
  145. Müftüoğlu, Z., Kızrak, M. A., & Yıldırım, T. (2022). Privacy-Preserving Mechanisms with Explainability in Assistive AI Technologies. In Learning and Analytics in Intelligent Systems (Vol. 28, pp. 287–309). https://doi.org/10.1007/978-3-030-87132-1_13
  146. Murray-Rust, D., Lupetti, M. L., Nicenboim, I., & Hoog, W. V. (2023). Grasping AI: experiential exercises for designers. AI & SOCIETY. https://doi.org/10.1007/s00146-023-01794-y
  147. Narayanan, S. (2023). Developing Responsible AI Business Model. In CSR, Sustainability, Ethics and Governance (pp. 205–217). https://doi.org/10.1007/978-3-031-09245-9_10
  148. Nedunuri, U. (2023). Factors Influencing the Adoption of Responsible AI. Proceedings - 2023 10th International Conference on Electrical and Electronics Engineering, ICEEE 2023, 214–218. https://doi.org/10.1109/ICEEE59925.2023.00046
  149. Oliver, D., & Jacobs, C. (2007). Developing guiding principles: an organizational learning perspective. Journal of Organizational Change Management, 813–828.
  150. Ong, L. M., & Findlay, M. (2023). A Realist’s Account of AI for SDGs: Power, Inequality and AI in Community. In Philosophical Studies Series (Vol. 152, pp. 43–64). https://doi.org/10.1007/978-3-031-21147-8_4
  151. Onyejegbu, L. N., Okengwu, U. A., Oghenekaro, L. U., Musa, M. O., & Ugbari, A. O. (2023). AI-Based QOS/QOE Framework for Multimedia Systems. Lecture Notes in Networks and Systems, 559 LNNS, 248–259. https://doi.org/10.1007/978-3-031-18461-1_16
  152. Öz, B., Wang, R. J., Chandler, C., Karran, A. J., Coursaris, C., & Léger, P. M. (2022). Responsible Artificial Intelligence in Knowledge Work: User Experience Design Problems and Implications. In J. Y. C. Chen, G. Fragomeni, H. Degen, & S. Ntoa (Eds.), HCI International 2022 - Late Breaking Papers: Interacting With Extended Reality and Artificial Intelligence (Vol. 13518, Issues 24th International Conference on Human-Computer Interaction (HCII), pp. 461–470). https://doi.org/10.1007/978-3-031-21707-4_32 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  153. Ozmen Garibay, O., Winslow, B., Andolina, S., Antona, M., Bodenschatz, A., Coursaris, C., Falco, G., Fiore, S. M., Garibay, I., Grieman, K., Havens, J. C., Jirotka, M., Kacorri, H., Karwowski, W., Kider, J., Konstan, J., Koon, S., Lopez-Gonzalez, M., Maifeld-Carucci, I., & McGregor, S. (2023). Six Human-Centered Artificial Intelligence Grand Challenges. International Journal of Human-Computer Interaction, 39(3), 391–437. http://10.0.4.56/10447318.2022.2153320
  154. Pak, C. (2022). Responsible AI and algorithm governance: An institutional perspective. In Human-Centered Artificial Intelligence: Research and Applications (pp. 251–270). https://doi.org/10.1016/B978-0-323-85648-5.00018-9
  155. Papagiannidis, E., Mikalef, P., Krogstie, J., & Conboy, K. (2022). From Responsible AI Governance to Competitive Performance: The Mediating Role of Knowledge Management Capabilities. In S. Papagiannidis, E. Alamanos, S. Gupta, Y. K. Dwivedi, M. Mantymaki, & I. O. Pappas (Eds.), Role of Digital Technologies in Shaping The Post-Pandemic World (Vol. 13454, Issues 21st International-Federation-of-Information-Processing (IFIP)-Working-Group-6.11 Conference on e-Business, e-Services and e-Society (I3E), pp. 58–69). https://doi.org/10.1007/978-3-031-15342-6_5 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  156. Pisoni, G., & Molnár, B. (2023). Data Management and Enterprise Architectures for Responsible AI Services. Lecture Notes in Networks and Systems, 763 LNNS, 879–884. https://doi.org/10.1007/978-3-031-42467-0_83
  157. Porayska-Pomsta, K. (2023). A Manifesto for a Pro-Actively Responsible AI in Education. International Journal of Artificial Intelligence in Education, 1–11.
  158. Prabhudesai, S., Hauth, J., Guo, D. K., Rao, A. R., Banovic, N., & Huan, X. (2023). Lowering the computational barrier: Partially Bayesian neural networks for transparency in medical imaging AI. Frontiers in Computer Science, 5. https://doi.org/10.3389/fcomp.2023.1071174 WE - Emerging Sources Citation Index (ESCI)
  159. Puaschunder, J. M. (2019). Artificial Intelligence evolution: On the virtue of killing in the artificial age. Scientia Moralitas-International Journal of Multidisciplinary Research, 4(2), 51–72.
  160. Pushkarna, M., Zaldivar, A., & Kjartansson, O. (2022). Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI. ACM International Conference Proceeding Series, 1776–1826. https://doi.org/10.1145/3531146.3533231
  161. Raffoul, F. (2010). The origins of responsibility. Indiana University Press.
  162. Ramirez-Amaro, K., Torre, I., Diehl, M., & Dean, E. (2023). The Importance of Human Factors for Trusted Human-Robot Collaborations. ACM International Conference Proceeding Series, 502–503. https://doi.org/10.1145/3623809.3623981
  163. Rantanen, E. M., Lee, J. D., Darveau, K., Miller, D. B., Intriligator, J., & Sawyer, B. D. (2021). Ethics Education of Human Factors Engineers for Responsible AI Development. Proceedings of the Human Factors and Ergonomics Society, 65(1), 1034–1038. https://doi.org/10.1177/1071181321651038
  164. Reis, S., Coelho, L., Sarmet, M., Araujo, J., & Corchado, J. M. (2023). The Importance of Ethical Reasoning in Next Generation Tech Education. 2023 5th International Conference of the Portuguese Society for Engineering Education, CISPEE 2023. https://doi.org/10.1109/CISPEE58593.2023.10227651
  165. Rivas, P., Thompson, C., Tafur, B., Khanal, B., Ayoade, O., Jui, T. D., Sooksatra, K., Orduz, J., & Bejarano, G. (2023). AI ethics for earth sciences. In Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges (pp. 379–396). https://doi.org/10.1016/B978-0-323-91737-7.00007-4
  166. Rizinski, M., Peshov, H., Mishev, K., Chitkushev, L. T., Vodenska, I., & Trajanov, D. (2022). Ethically Responsible Machine Learning in Fintech. IEEE ACCESS, 10, 97531–97554. https://doi.org/10.1109/ACCESS.2022.3202889 WE - Science Citation Index Expanded (SCI-EXPANDED)
  167. Robbins, S. (2020). AI and the path to envelopment: knowledge as a first step towards the responsible regulation and use of AI-powered machines. AI & SOCIETY, 35, 391–400.
  168. Rodier, C., Millar, J., Deisinger, W., & Hodgson, S. J. (2023). Art Critically Examining Generative AI. 2023 IEEE IFEES World Engineering Education Forum and Global Engineering Deans Council: Convergence for a Better World: A Call to Action, WEEF-GEDC 2023 - Proceedings. https://doi.org/10.1109/WEEF-GEDC59520.2023.10343903
  169. Roemmich, K., & Andalibi, N. (2021). Data Subjects’ Conceptualizations of and Attitudes Toward Automatic Emotion Recognition-Enabled Wellbeing Interventions on Social Media. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2). https://doi.org/10.1145/3476049
  170. Roy, S., & Salimi, B. (2023). Causal Inference in Data Analysis with Applications to Fairness and Explanations. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13759 LNCS, 105–131. https://doi.org/10.1007/978-3-031-31414-8_3
  171. Rudd, J., & Igbrude, C. (2023). A global perspective on data powering responsible AI solutions in health applications. AI and Ethics, 1–11. https://doi.org/10.1007/s43681-023-00302-8
  172. Ruttkamp-Bloem, E. (2023). Intergenerational Justice as Driver for Responsible AI. Communications in Computer and Information Science, 1976 CCIS, 18–30. https://doi.org/10.1007/978-3-031-49002-6_2
  173. Sætra, H. S., & Danaher, J. (2022). To each technology its own ethics: The problem of ethical proliferation. Philosophy & Technology, 35(4), 93.
  174. Sanderson, C., Douglas, D., Lu, Q. H., & IEEE. (2023). Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects. 2023 International Joint Conference on Neural Networks, IJCNN, 2023-June(International Joint Conference on Neural Networks (IJCNN)). https://doi.org/10.1109/IJCNN54540.2023.10191274 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  175. Sanderson, C., Lu, Q., Douglas, D., Xu, X., Zhu, L., Whittle, J., & Sanderson, C., Lu, Q., Douglas, D., Xu, X., Zhu, L., & Whittle, J. (2022). Towards Implementing Responsible AI. IEEE International Conference on Big Data (Big Data), 5076–5081. https://doi.org/10.1109/BigData55660.2022.10021121
  176. Santoni de Sio, F., & Mecacci, G. (2021). Four responsibility gaps with artificial intelligence: Why they matter and how to address them. Philosophy & Technology, 34(4), 1057–1084.
  177. Sartori, L., & Bocca, G. (2022). Minding the gap(s): public perceptions of AI and socio-technical imaginaries. AI and Society. https://doi.org/10.1007/s00146-022-01422-1
  178. Satish, M., Babu, S. M., Kumar, P. P., Devi, S., & Reddy, K. P. (2023). Artificial Intelligence (AI) and the Prediction of Climate Change Impacts. 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023, 660–664. https://doi.org/10.1109/ICCCMLA58983.2023.10346636
  179. Satornino, C. B., Du, S., & Grewal, D. (2024). Using artificial intelligence to advance sustainable development in industrial markets: A complex adaptive systems perspective. Industrial Marketing Management, 116, 145–157. https://doi.org/https://doi.org/10.1016/j.indmarman.2023.11.011
  180. Saxena, D., Wall, P. J., Lewis, D., & IEEE. (2023). Artificial Intelligence (AI) Ethics: A Critical Realist Emancipatory Approach. 2023 IEEE International Symposium on Technology and Society, ISTAS, 29th Annual IEEE International Symposium on Technology and Society (ISTAS). https://doi.org/10.1109/ISTAS57930.2023.10305995 WE - Conference Proceedings Citation Index - Science (CPCI-S) WE - Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)
  181. Scepanovic, S., Bogucka, E. P., Quercia, D., & Nattero, C. (2023). Responsible AI for Earth Observation: Attitides among Experts. International Geoscience and Remote Sensing Symposium (IGARSS), 2023-July, 1934–1936. https://doi.org/10.1109/IGARSS52108.2023.10282983
  182. Schiff, D., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2020). Principles to practices for responsible AI: closing the gap. ArXiv Preprint.
  183. Shneiderman, B. (2021). Responsible AI: Bridging from ethics to practice. Communications of the ACM, 64(8), 32–35.
  184. Siala, H., & Wang, Y. C. (2022). SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Social Science & Medicine, 296. https://doi.org/10.1016/j.socscimed.2022.114782 WE - Science Citation Index Expanded (SCI-EXPANDED) WE - Social Science Citation Index (SSCI)
  185. Simon, H. A. (1995). Artificial intelligence: an empirical science. Artificial Intelligence, 77(1), 95–127.
  186. Sinde, R., Diwani, S., Leo, J., Kondo, T., Elisa, N., & Matogoro, J. (2023). AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector. Frontiers in Artificial Intelligence, 6, 1133677. https://doi.org/10.3389/frai.2023.1133677
  187. Sivarajah, U., Wang, Y. C., Olya, H., & Mathew, S. (2023). Responsible Artificial Intelligence (AI) for Digital Health and Medical Analytics. Information Systems Frontiers, 25(6), 2117–2122. https://doi.org/10.1007/s10796-023-10412-7
  188. Sothilingam, R. (2023). A Requirements-Driven Conceptual Modeling Framework for Responsible AI. Proceedings of the IEEE International Conference on Requirements Engineering, 2023-Septe, 391–395. https://doi.org/10.1109/RE57278.2023.00061
  189. Souza, R., Skluzacek, T. J., Wilkinson, S. R., Ziatdinov, M., & Da Silva, R. F. (2023). Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability. Proceedings 2023 IEEE 19th International Conference on E-Science, e-Science 2023. https://doi.org/10.1109/e-Science58273.2023.10254822
  190. STAHL, B. C. (2022). Responsible innovation ecosystems: Ethical implications of the application of the ecosystem concept to artificial intelligence. International Journal of Information Management, 62, N.PAG-N.PAG. https://doi.org/10.1016/j.ijinfomgt.2021.102441
  191. Stough, L. M., & Lee, S. (2021). Grounded theory approaches used in educational research journals. International Journal of Qualitative Methods, 20, 16094069211052204.
  192. Szabo, L., Raisi-Estabragh, Z., Salih, A., McCracken, C., Pujadas, E. R., Gkontra, P., Kiss, M., Maurovich-Horvath, P., Vago, H., Merkely, B., Lee, A. M., Lekadir, K., & Petersen, S. E. (2022). Clinician’s guide to trustworthy and responsible artificial intelligence in cardiovascular imaging. Frontiers in Cardiovascular Medicine, 9. https://doi.org/10.3389/fcvm.2022.1016032 WE - Science Citation Index Expanded (SCI-EXPANDED)
  193. Szczekocka, E., Tarnec, C., & Pieczerak, J. (2022). Standardization on Bias in Artificial Intelligence as Industry Support. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, 5090–5099. https://doi.org/10.1109/BigData55660.2022.10020735
  194. Tahaei, M., Constantinides, M., Quercia, D., Kennedy, S., Muller, M., Stumpf, S., Liao, Q. V, Baeza-Yates, R., Aroyo, L., Holbrook, J., Luger, E., Madaio, M., Blumenfeld, I. G., De-Arteaga, M., Vitak, J., & Olteanu, A. (2023). Human-Centered Responsible Artificial Intelligence: Current & Future Trends. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3544549.3583178
  195. Tayebi, A., & Garibay, O. O. (2023). Improving Fairness via Deep Ensemble Framework Using Preprocessing Interventions. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14050 LNAI, 477–489. https://doi.org/10.1007/978-3-031-35891-3_29
  196. Teixeira, S., Veloso, B., Rodrigues, J. C., & Gama, J. (2023). Ethical and Technological AI Risks Classification: A Human Vs Machine Approach. Communications in Computer and Information Science, 1752 CCIS, 150–166. https://doi.org/10.1007/978-3-031-23618-1_10
  197. Thornberg, R. (2012). Informed grounded theory. Scandinavian Journal of Educational Research, 56(3), 243–259.
  198. Timmermans, S., & Tavory, I. (2007). Advancing ethnographic research through grounded theory practice. The Sage Handbook of Grounded Theory, 493–512.
  199. Trocin, C., Mikalef, P., Papamitsiou, Z., & Conboy, K. (2023). Responsible AI for Digital Health: a Synthesis and a Research Agenda. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10146-4
  200. Tutun, S., Harfouche, A., Albizri, A., Johnson, M. E., & He, H. Y. (2022). A Responsible AI Framework for Mitigating the Ramifications of the Organ Donation Crisis. Information Systems Frontiers, 25(6), 2301–2316. https://doi.org/10.1007/s10796-022-10340-y
  201. Upreti, K., Verma, A., Mittal, S., Vats, P., Haque, M., & Ali, S. (2023). A Novel Framework for Harnessing AI for Evidence-Based Policymaking in E-Governance Using Smart Contracts. Communications in Computer and Information Science, 1921 CCIS, 231–240. https://doi.org/10.1007/978-3-031-45124-9_18
  202. Wach, K., Duong, C. D., Ejdys, J., Kazlauskaite, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7–30. https://doi.org/10.15678/EBER.2023.110201 WE - Emerging Sources Citation Index (ESCI)
  203. Walsh, P., Bera, J., Sharma, V. S., Kaulgud, V., Rao, R. M., Ross, O., & Soc, I. C. (2021). Sustainable AI in the Cloud Exploring Machine Learning Energy Use in the Cloud. In 2021 36TH IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW 2021) (Issue 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 265–266). https://doi.org/10.1109/ASEW52652.2021.00058 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  204. Wang, W. S., Chen, L., Xiong, M. R., & Wang, Y. C. (2021). Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care. Information Systems Frontiers, 25(6), 2239–2256. https://doi.org/10.1007/s10796-021-10154-4
  205. Wang, Y., Xiong, M., & Olya, H. G. T. (2020). Toward an understanding of responsible artificial intelligence practices. Proceedings of the Annual Hawaii International Conference on System Sciences, 2020-Janua, 4962–4971. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087789376&partnerID=40&md5=65b91535aa6d5c0afb18152435cbd358
  206. Weekes, T. R., & Eskridge, T. C. (2022). Responsible Human-Centered Artificial Intelligence for the Cognitive Enhancement of Knowledge Workers. In J. Y. C. Chen, G. Fragomeni, H. Degen, & S. Ntoa (Eds.), HCI International 2022 - Late Breaking Papers: Interacting With Extended Reality and Artificial Intelligence (Vol. 13518, Issues 24th International Conference on Human-Computer Interaction (HCII), pp. 568–582). https://doi.org/10.1007/978-3-031-21707-4_41 WE - Conference Proceedings Citation Index - Science (CPCI-S)
  207. Weiss, A., Vrecar, R., Zamiechowska, J., & Purgathofer, P. (2023). It’s Only a Bot! How Adversarial Chatbots can be a Vehicle to Teach Responsible AI. In CSR, Sustainability, Ethics and Governance (pp. 235–250). https://doi.org/10.1007/978-3-031-09245-9_12
  208. Wellnhofer, E. (2022). Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging. Frontiers in Cardiovascular Medicine, 9, 890809. https://doi.org/10.3389/fcvm.2022.890809
  209. Wolfswinkel, J. F., Furtmueller, E., & Wilderom, C. P. (2013). Using grounded theory as a method for rigorously reviewing literature. European Journal of Information Systems, 22(1), 45–55.
  210. Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., & Yu, H. (2020). Federated Learning. In Synthesis Lectures on Artificial Intelligence and Machine Learning (Vol. 13, Issue 3, pp. 1–207). https://doi.org/10.2200/S00960ED2V01Y201910AIM043
  211. Yudkowsky, E. (2008). Artificial intelligence as a positive and negative factor in global risk. Global Catastrophic Risks, 1(303), 184.

How to Cite

Memarian, B., & Doleck, T. (2024). A review of caveats and future considerations of research on responsible AI across disciplines. Human Technology, 20(3), 488–524. https://doi.org/10.14254/1795-6889.2024.20-3.4