Purpose : This paper proposed to examine how Responsible AI and Explainable AI jointly contributed to strengthening algorithmic safety. It examined the significant role of ethical governance, transparency, accountability, stakeholder involvement, and explainability to mitigate algorithmic risks and promote trustworthy use of AI decision-making.

Design : This review study conceptualized Responsible and Explainable AI (REAI) by integrating the dimensions of Responsible AI (RAI) and Explainable AI (XAI) and further examined how Responsible and Explainable AI (REAI) mechanisms strengthened algorithmic safety.

Findings : The paper emphasized that Responsible AI offers an ethical, legal, and governance framework for building safe AI systems, and Explainable AI could help achieve interpretability, traceability, auditability, and contestability. Notably, this timely integration could help mitigate bias, fill accountability gaps, enhance human oversight, enable lifecycle risk management, and safeguard human agency in algorithmic decision-making.

Practical Implications : The proposed framework offered significant contributions for AI practitioners, managers, regulators, and policymakers by demonstrating how responsible AI and explainable AI mechanisms could be jointly embedded across the AI lifecycle. It emphasized that continuous risk assessment, human oversight, traceability, stakeholder engagement, and contestability worked as complementary mechanisms for strengthening algorithmic safety.

Originality : This paper offered a contribution by conceptualizing algorithmic safety as a process, not just a product, of a socio-technical process. It provided a conceptual framework to connect Responsible AI principles and Explainable AI mechanisms to more human-centered, accountable, safe, transparent AI systems.

1. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
2. Bach, T. A., Kaarstad, M., Solberg, E., & Babic, A. (2025). Insights into suggested Responsible AI (RAI) practices in real-world settings: A systematic literature review. AI and Ethics, 5(3), 3185–3232. https://doi.org/10.1007/s43681-024-00648-7
3. Baker, S., & Xiang, W. (2023). Explainable AI is responsible AI: How explainability creates trustworthy and socially responsible artificial intelligence. arXiv preprint arXiv:2312.01555. https://doi.org/10.48550/arXiv.2312.01555
4. Batool, A., Zowghi, D., & Bano, M. (2023). Responsible AI governance: A systematic literature review. arXiv preprint arXiv:2401.10896. https://doi.org/10.48550/arXiv.2401.10896
5. Bennetot, A., Donadello, I., El Qadi El Haouari, A., Dragoni, M., Frossard, T., Wagner, B., ... & Diaz-Rodriguez, N. (2024). A practical tutorial on explainable AI techniques. ACM Computing Surveys, 57(2), 1–44. https://doi.org/10.1145/3670685
6. Berman, G., Goyal, N., & Madaio, M. (2024, May). A scoping study of evaluation practices for responsible AI tools: Steps towards effectiveness evaluations. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1–24). https://doi.org/10.1145/3613904.3642398
7. Besinger, P., Vejnoska, D., & Ansari, F. (2024). Responsible AI (RAI) in manufacturing: A qualitative framework. Procedia Computer Science, 232, 813–822. https://doi.org/10.1016/j.procs.2024.01.081
8. Brandao, P. R. (2025). The impact of artificial intelligence on modern society. AI, 6(8), 190. https://doi.org/10.3390/ai6080190
9. Busuioc, M. (2021). Accountable artificial intelligence: Holding algorithms to account. Public Administration Review, 81(5), 825–836. https://doi.org/10.1111/puar.13293
10. Constantinides, M., Bogucka, E., Quercia, D., Kallio, S., & Tahaei, M. (2024). RAI guidelines: Method for generating responsible AI guidelines grounded in regulations and usable by (non-)technical roles. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW2), 1–28. https://doi.org/10.1145/3686927
11. Dam, H. K., Tran, T., & Ghose, A. (2018, May). Explainable software analytics. In Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results (pp. 53–56). https://doi.org/10.1145/3183399.3183424
12. Ding, S., & Magerko, B. (2026, February). Bridging responsible AI and AI literacy: The TEACH-RAI framework and toolkit for education, design, and research. In Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.2 (pp. 1295–1296). https://doi.org/10.1145/3770761.3777259
13. Dixit, A., Jha, R., Baber, R., & Baber, P. (2024). The impact of artificial intelligence on digital employee engagement. Prabandhan: Indian Journal of Management, 17(9), 24–43. https://doi.org/10.17010/pijom/2024/v17i9/173940
14. Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., ... & Ranjan, R. (2023). Explainable AI (XAI): Core ideas, techniques, and solutions. ACM Computing Surveys, 55(9), 1–33. https://doi.org/10.1145/3561048
15. Elia, M., Ziethmann, P., Krumme, J., Schlögl-Flierl, K., & Bauer, B. (2025). Responsible AI, ethics, and the AI lifecycle: How to consider the human influence? AI and Ethics, 5(4), 4011–4028. https://doi.org/10.1007/s43681-025-00666-z
16. Giri, M. (2025). Artificial intelligence in cybersecurity: Opportunities and risks for corporate security. Policy, Law and Governance Insights, 1(1), 30–37. https://doi.org/10.64006/plgi/1103
17. Hameed, Z., Alsaleh, H., Malik, P., & Vishnoi, S. K. (2026). How does responsible AI influence healthcare employee AI collaboration and engagement? The role of AI trust and responsible leadership. Technological Forecasting and Social Change, 227, 124651. https://doi.org/10.1016/j.techfore.2026.124651
18. Henderson, P., & Krass, M. (2023). Algorithmic rulemaking vs. algorithmic guidance. Harvard Journal of Law & Technology, 37(1), 105–150.
19. Kallina, E., Bohné, T., & Singh, J. (2025, June). Stakeholder participation for responsible AI development: Disconnects between guidance and current practice. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 1060–1079). https://doi.org/10.1145/3715275.3732069
20. Kumar, A., & Banerjee, P. (2025). Leading with intelligence: How AI is reshaping creative innovation in organizations. Prabandhan: Indian Journal of Management, 18(9), 54–62. https://doi.org/10.17010/pijom/2025/v18i9/174843
21. Kumar, N., Dwivedi, R., Tripathi, P., & Mishra, R. (2025). Explainable artificial intelligence modelling for Bitcoin price forecasting. Journal of Emerging Technologies and Innovation Management, 1(1), 36–52. https://doi.org/10.64006/jetim/1104
22. Laufer, B., Jain, S., Cooper, A. F., Kleinberg, J., & Heidari, H. (2022, June). Four years of FAccT: A reflexive, mixed-methods analysis of research contributions, shortcomings, and future prospects. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 401–426). https://doi.org/10.1145/3531146.3533107
23. Lu, Q., Zhu, L., Xu, X., Whittle, J., Zowghi, D., & Jacquet, A. (2024). Responsible AI pattern catalogue: A collection of best practices for AI governance and engineering. ACM Computing Surveys, 56(7), 1–35. https://doi.org/10.1145/3626234
24. Mathur, S., Vishnoi, S. K., Bagga, T., Mittal, A., & Mittal, A. (2024). Examining the impact of argument quality and source credibility on consumers' behavioral intention toward green cosmetics: The moderating role of perceived innovativeness. Prabandhan: Indian Journal of Management, 17(3), 26–47. https://doi.org/10.17010/pijom/2024/v17i3/173364
25. Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6(3), 175–183. https://doi.org/10.1007/s10676-004-3422-1
26. Patel, M., & Imran, M. (2025). AI in the courtrooms: Global expert systems, advantages, and ethical challenges. Policy, Law and Governance Insights, 1(2), 1–14. https://doi.org/10.64006/plgi/1201
27. Reyes-Cruz, G., Perez Vallejos, E., Barnard, P., Schneiders, E., Tachiquin, M., Price, D., ... & Fischer, J. (2025, October). Unravelling responsible AI: An umbrella review. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(3), 2177–2188. https://doi.org/10.1609/aies.v8i3.36704
28. Saranya, A., & Subhashini, R. (2023). A systematic review of explainable artificial intelligence models and applications: Recent developments and future trends. Decision Analytics Journal, 7, 100230. https://doi.org/10.1016/j.dajour.2023.100230
29. Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., ... & Sharma, R. (2022). Explainable AI for healthcare 5.0: Opportunities and challenges. IEEE Access, 10, 84486–84517. https://doi.org/10.1109/ACCESS.2022.3197671
30. Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P., ... & Virk, G. (2023, August). Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (pp. 723–741). https://doi.org/10.1145/3600211.3604673
31. Shukla, D., & Singh, M. (2025). Charting the research terrain of artificial intelligence in human resource management: A bibliometric analysis and emerging research horizons. Prabandhan: Indian Journal of Management, 18(8), 51–74. https://doi.org/10.17010/pijom/2025/v18i8/173901
32. Singh, H., Aggarwal, R., Garg, P., & Aggarwal, D. (2025). AI and ESG performance: An empirical study of the high-tech sector. Prabandhan: Indian Journal of Management, 18(6), 8–25. https://doi.org/10.17010/pijom/2025/v18i6/174487
33. Singh, R., & Arora, N. (2025). The application of artificial intelligence in law: A bibliometric analysis. Prabandhan: Indian Journal of Management, 18(7), 58–71. https://doi.org/10.17010/pijom/2025/v18i7/174565
34. Sodhi, S. K., Bansal, S., & Saxena, U. (2025). Artificial intelligence in healthcare: Market dynamics, ethical imperatives, and managerial foresight. Prabandhan: Indian Journal of Management, 18(9), 79–84. https://doi.org/10.17010/pijom/2025/v18i9/174845
35. Sparrow, R. (2007). Killer robots. Journal of Applied Philosophy, 24(1), 62–77. https://doi.org/10.1111/j.1468-5930.2007.00346.x
36. Tripathi, A., & Walia, R. (2025). Predictive analytics and AI decision science in Indian healthcare: A PRISMA-guided synthesis on scaling frameworks. Journal of Emerging Technologies and Innovation Management, 1(2), 34–49. https://doi.org/10.64006/jetim/1204
37. Vishnoi, S. K., Sharma, V. K., Ghosh, S. K., Mathur, S., Mittal, S., Singhal, R., Agarwal, P., & Jain, A. (2026). Unveiling critical success factors for marketing intelligence: A multi-method framework. Prabandhan: Indian Journal of Management, 19(5), 8–38. https://doi.org/10.17010/pijom/2026/v19i5/174928
38. Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., ... & 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
39. Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1), Article 15. https://doi.org/10.1186/s41239-024-00448-3
40. Wong, R. Y., Madaio, M. A., & Merrill, N. (2023). Seeing like a toolkit: How toolkits envision the work of AI ethics. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), 1–27. https://doi.org/10.1145/3579621
41. Xia, B., Lu, Q., Perera, H., Zhu, L., Xing, Z., Liu, Y., & Whittle, J. (2023). Towards concrete and connected AI risk assessment (C²AIRA): A systematic mapping study. arXiv preprint arXiv:2301.11616. https://doi.org/10.48550/arXiv.2301.11616
42. Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019, September). Explainable AI: A brief survey on history, research areas, approaches and challenges. In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 563–574). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-32236-6_51
43. Zhang, Y., & Chen, X. (2020). Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14(1), 1–101. https://doi.org/10.1561/1500000066