The Age of Explainable AI: Improving Trust and Transparency in AI Models
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V1I4P105Keywords:
Explainable AI, Trust, Transparency, AI Models, Interpretability, Accountability, Machine Learning, Decision-Making, Ethical Standards, Regulatory Requirements, High-Stakes Applications, Healthcare, Finance, Legal Systems,, Confidence, Accessibility, Reliability, Explainability, Fairness, Bias Mitigation, Model Interpretability, Model Transparency, Predictive Models, Black-Box Models, White-Box Models, Feature Importance, Algorithmic Accountability, Responsible AI, Human-AI Collaboration, Risk Assessment, Model Validation, Auditability, Explainability Tools, Trustworthy AIAbstract
Artificial Intelligence (AI) is changing the way healthcare, finance & law enforcement work by making them more efficient & creative and making it easier to make these decisions based on their information. As AI models become more complicated, their decision-making processes become less clear, which hurts trust, accountability & ethical use. Explainable AI (XAI) has come up to help with these problems by making AI systems easier to understand and more open, which helps people understand why certain actions were taken. XAI makes AI's decision-making processes easier to understand by employing these approaches including feature significance analysis, model-agnostic methodologies, interpretable models & visualization tools. These methods make sure that important tasks like medical diagnosis, approving loans & finding bias in law enforcement algorithms are all accurate, fair & easy to understand. XAI gives consumers clear, useful information that helps them make informed decisions with confidence, even if they aren't tech-savvy. This research looks at the main ideas of XAI, including its most important methods, uses & the problems it has to solve to live up to its potential. XAI builds trust in AI systems by making AI models easier to understand. This leads to more widespread use & more responsibility in important areas. As AI becomes better, it will be important to explain things in order to deal with moral difficulties, reduce bias & make sure that people follow the rules. This will help people utilize AI technology in a more responsible & sustainable way
References
[1] Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access, 6, 52138-52160.
[2] Felzmann, H., Villaronga, E. F., Lutz, C., & Tamò-Larrieux, A. (2019). Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns. Big Data & Society, 6(1), 2053951719860542.
[3] Allam, Hitesh. Exploring the Algorithms for Automatic Image Retrieval Using Sketches. Diss. Missouri Western State University, 2017.
[4] Samek, W. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.
[5] Immaneni, J. (2020). Using Swarm Intelligence and Graph Databases Together for Advanced Fraud Detection. Journal of Big Data and Smart Systems, 1(1).
[6] Khedkar, S., Subramanian, V., Shinde, G., & Gandhi, P. (2019). Explainable AI in healthcare. In Healthcare (april 8, 2019). 2nd international conference on advances in science & technology (icast).
[7] Jani, Parth. "UM Decision Automation Using PEGA and Machine Learning for Preauthorization Claims." The Distributed Learning and Broad Applications in Scientific Research 6 (2020): 1177-1205.
[8] Patel, Piyushkumar, and Disha Patel. "Blockchain’s Potential for Real-Time Financial Auditing: Disrupting Traditional Assurance Practices." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1468-84.
[9] Hagras, H. (2018). Toward human-understandable, explainable AI. Computer, 51(9), 28-36.
[10] Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019, May). Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1-15).
[11] Manda, Jeevan Kumar. "Cybersecurity strategies for legacy telecom systems: Developing tailored cybersecurity strategies to secure aging telecom infrastructures against modern cyber threats, leveraging your experience with legacy systems and cybersecurity practices." Leveraging your Experience with Legacy Systems and Cybersecurity Practices (January 01, 2017) (2017).
[12] Fox, M., Long, D., & Magazzeni, D. (2017). Explainable planning. arXiv preprint arXiv:1709.10256.
[13] Arugula, Balkishan, and Sudhkar Gade. “Cross-Border Banking Technology Integration: Overcoming Regulatory and Technical Challenges”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 1, Mar. 2020, pp. 40-48
[14] Arnold, M., Bellamy, R. K., Hind, M., Houde, S., Mehta, S., Mojsilović, A., ... & Varshney, K. R. (2019). FactSheets: Increasing trust in AI services through supplier's declarations of conformity. IBM Journal of Research and Development, 63(4/5), 6-1.
[15] Mohammad, Abdul Jabbar. “Sentiment-Driven Scheduling Optimizer”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 2, June 2020, pp. 50-59
[16] Anjomshoae, S., Najjar, A., Calvaresi, D., & Främling, K. (2019). Explainable agents and robots: Results from a systematic literature review. In 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019 (pp. 1078-1088). International Foundation for Autonomous Agents and Multiagent Systems.
[17] Manda, Jeevan Kumar. "Cloud Security Best Practices for Telecom Providers: Developing comprehensive cloud security frameworks and best practices for telecom service delivery and operations, drawing on your cloud security expertise." Available at SSRN 5003526 (2020).
[18] Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018). Metrics for explainable AI: Challenges and prospects. arXiv preprint arXiv:1812.04608.
[19] Jani, Parth. "Modernizing Claims Adjudication Systems with NoSQL and Apache Hive in Medicaid Expansion Programs." JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE) 7.1 (2019): 105-121.
[20] Immaneni, J. (2020). Building MLOps Pipelines in Fintech: Keeping Up with Continuous Machine Learning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 1(2), 22-32.
[21] Nookala, G. (2020). Automation of privileged access control as part of enterprise control procedure. Journal of Big Data and Smart Systems, 1(1).
[22] Fellous, J. M., Sapiro, G., Rossi, A., Mayberg, H., & Ferrante, M. (2019). Explainable artificial intelligence for neuroscience: behavioral neurostimulation. Frontiers in neuroscience, 13, 1346.
[23] Shaik, Babulal. "Network Isolation Techniques in Multi-Tenant EKS Clusters." Distributed Learning and Broad Applications in Scientific Research 6 (2020).
[24] Preece, A. (2018). Asking ‘Why’in AI: Explainability of intelligent systems–perspectives and challenges. Intelligent Systems in Accounting, Finance and Management, 25(2), 63-72.
[25] Patel, Piyushkumar. "The Evolution of Revenue Recognition Under ASC 606: Lessons Learned and Industry-Specific Challenges." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 1485-98.
[26] Sai Prasad Veluru. “Real-Time Fraud Detection in Payment Systems Using Kafka and Machine Learning”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 7, no. 2, Dec. 2019, pp. 199-14
[27] Papenmeier, A., Englebienne, G., & Seifert, C. (2019). How model accuracy and explanation fidelity influence user trust. arXiv preprint arXiv:1907.12652.
[28] Manda, Jeevan Kumar. "AI And Machine Learning In Network Automation: Harnessing AI and Machine Learning Technologies to Automate Network Management Tasks and Enhance Operational Efficiency in Telecom, Based On Your Proficiency in AI-Driven Automation Initiatives." Educational Research (IJMCER) 1.4 (2019): 48-58.
[29] Ahmad, M. A., Eckert, C., & Teredesai, A. (2019). The challenge of imputation in explainable artificial intelligence models. arXiv preprint arXiv:1907.12669.
[30] Mueller, S. T., Hoffman, R. R., Clancey, W., Emrey, A., & Klein, G. (2019). Explanation in human-AI systems: A literature meta-review, synopsis of key ideas and publications, and bibliography for explainable AI. arXiv preprint arXiv:1902.01876.
