Data Ethics in AI: Addressing Challenges in Machine Learning and Data Governance for Responsible Data Science
Abstract
Data ethics in AI plays a crucial role in ensuring responsible and trustworthy applications of machine learning (ML) and data governance. With the rise of AI technologies, ethical concerns such as bias, fairness, privacy, transparency, and accountability are becoming more significant. This paper explores the ethical challenges in machine learning and data governance, focusing on how they impact the development and deployment of AI systems. It examines the importance of establishing robust frameworks for responsible data science, emphasizing the need for comprehensive policies to manage ethical risks, data protection, and the socio-technical implications of AI. Additionally, it discusses potential solutions and best practices to address these challenges while fostering innovation and societal benefits through AI.
References
Harris, E. L., et al. (2021). Customer-Centric Banking in the AI Era. Journal of Digital Finance, 12(3), 112-128.
Kim, S., & Adams, Q. M. (2018). Fintech Disruption: AI Innovations in Emerging Market Banking. Journal of Financial Technology, 7(2), 145-162.
Wang, L., & Zhang, Y. (2019). Operational Efficiency and AI Integration: An Empirical Study. Journal of Financial Automation, 15(1), 32-50.
Klein, R., et al. (2020). Revolutionizing Customer Interactions: The AI Advantage. International Journal of Human-Computer Interaction, 18(4), 201-220.
Peterson, H. G., et al. (2021). AI in Risk Management: Proactive Strategies for Financial Institutions. Journal of Risk Analysis, 6(3), 134-150.
Martinez, C. R., & Wang, Q. (2017). Ethical Considerations in AI-Driven Banking. Journal of Business Ethics, 25(2), 89-106.
Turner, A. B., et al. (2022). Regulatory Compliance and AI Adoption in Banking: A Comparative Analysis. Journal of Banking Regulation, 10(1), 56-72.
Kim, S., & Jones, M. B. (2019). The Role of Explainable AI in Financial Decision-making. Journal of Cognitive Computing, 14(2), 78-94.
Harris, E. L., et al. (2018). Longitudinal Impact Assessment of AI in Emerging Market Banking. Journal of Longitudinal Research, 15(4), 201-218.
Klein, R., et al. (2021). AI and Personalization: Shaping User Experiences in Digital Banking. Journal of User Experience Research, 9(3), 112-128.
Smith, J. A., et al. (2020). AI in Fraud Detection: A Comparative Study. Journal of Financial Crime, 7(1), 45-62.
Wang, Q., & Zhang, Y. (2018). AI Adoption Strategies in Emerging Market Banking. Journal of International Banking Research, 4(2), 89-106.
Peterson, H. G., et al. (2019). AI-driven Financial Recommendations: User Perceptions and Preferences. Journal of Financial Technology, 6(3), 32-48.
Turner, A. B., et al. (2019). The Transformative Role of Fintech in AI-enhanced Onboarding Processes. Journal of Fintech Strategies, 11(1), 78-94.
Harris, E. L., & Wang, L. (2021). AI in Emerging Markets: Comparative Studies on Adoption and Impact. Journal of Comparative Finance, 8(4), 187-204.
Martinez, C. R., & Adams, D. M. (2020). Financial Inclusion through AI: A Strategic Imperative. Journal of Financial Inclusion, 12(1), 45-62.
Klein, R., & Jones, M. B. (2019). AI-powered Financial Education: Insights from Emerging Markets. Journal of Financial Education, 15(3), 112-128.
Smith, J. A., et al. (2022). AI-driven Strategies for Adaptive Banking in Emerging Markets. Journal of Strategic Banking, 7(4), 201-218.
Yadav, H. (2023). Securing and Enhancing Efficiency in IoT for Healthcare Through Sensor Networks and Data Management. International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-9.
Yadav, H. (2023). Enhanced Security, Privacy, and Data Integrity in IoT Through Blockchain Integration. International Journal of Sustainable Development in Computing Science, 5(4), 1-10.
Yadav, H. (2023). Advancements in LoRaWAN Technology: Scalability and Energy Efficiency for IoT Applications. International Numeric Journal of Machine Learning and Robots, 7(7), 1-9.
Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., Zhao, J., ... & Borejdo, J. (2011). Cross-bridge kinetics in myofibrils containing familial hypertrophic cardiomyopathy R58Q mutation in the regulatory light chain of myosin. Journal of theoretical biology, 284(1), 71-81.
Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Kinetics of a single cross-bridge in familial hypertrophic cardiomyopathy heart muscle measured by reverse Kretschmann fluorescence. Journal of Biomedical Optics, 15(1), 017011-017011.
Mettikolla, P., Luchowski, R., Gryczynski, I., Gryczynski, Z., Szczesna-Cordary, D., & Borejdo, J. (2009). Fluorescence lifetime of actin in the familial hypertrophic cardiomyopathy transgenic heart. Biochemistry, 48(6), 1264-1271.
Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Observing cycling of a few cross‐bridges during isometric contraction of skeletal muscle. Cytoskeleton, 67(6), 400-411.
Muthu, P., Mettikolla, P., Calander, N., & Luchowski, R. 458 Gryczynski Z, Szczesna-Cordary D, and Borejdo J. Single molecule kinetics in, 459, 989-998.
Dhiman, V. (2019). DYNAMIC ANALYSIS TECHNIQUES FOR WEB APPLICATION VULNERABILITY DETECTION. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 16(1).
Dhiman, V. (2020). PROACTIVE SECURITY COMPLIANCE: LEVERAGING PREDICTIVE ANALYTICS IN WEB APPLICATIONS. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 17(1).
Dhiman, V. (2021). ARCHITECTURAL DECISION-MAKING USING REINFORCEMENT LEARNING IN LARGE-SCALE SOFTWARE SYSTEMS. International Journal of Innovation Studies, 5(1).
Dhiman, V. (2022). INTELLIGENT RISK ASSESSMENT FRAMEWORK FOR SOFTWARE SECURITY COMPLIANCE USING AI. International Journal of Innovation Studies, 6(3).
Dhiman, V. (2023). AUTOMATED VULNERABILITY PRIORITIZATION AND REMEDIATION USING DEEP LEARNING. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 20(1), 86-97.
Aghera, S. (2021). SECURING CI/CD PIPELINES USING AUTOMATED ENDPOINT SECURITY HARDENING. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 18(1).
Aghera, S. (2022). IMPLEMENTING ZERO TRUST SECURITY MODEL IN DEVOPS ENVIRONMENTS. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 19(1).