Transparent Machine Learning: Building Trust in Data Analytics

Venkata Tadi

Abstract: This study investigates the role of transparency and accountability in machine learning, emphasizing their importance in building trust and ensuring ethical data practices. The rapid adoption of machine learning models has raised critical concerns about their transparency and accountability. We examine the challenges associated with opaque algorithms and the potential biases they can introduce. Furthermore, we propose a comprehensive framework for enhancing transparency and accountability in the development and deployment of machine learning models. This framework includes best practices for algorithmic transparency, mechanisms for accountability, and strategies for mitigating bias. By integrating these elements, we aim to foster a more ethical and trustworthy landscape for data analytics. Our findings underscore the necessity of clear and accountable machine learning processes to maintain public trust and ensure fair outcomes. This study contributes to the ongoing discourse on ethical AI, providing actionable insights for researchers, practitioners, and policymakers committed to responsible data analytics.

Keywords: Transparency, Accountability, Machine Learning, Ethical AI, Bias Mitigation, Interpretability, Governance

How to Cite?: Venkata Tadi, "Transparent Machine Learning: Building Trust in Data Analytics", Volume 9 Issue 12, December 2020, International Journal of Science and Research (IJSR), Pages: 1850-1857, https://www.ijsr.net/getabstract.php?paperid=SR24709205430, DOI: https://dx.doi.org/10.21275/SR24709205430

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