International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Why AI Transparency Fails: Real-World Lessons from Tech Leaders

Author(s) Sudheer Peddineni
Country United States
Abstract AI systems have become increasingly complex, creating major challenges in keeping them transparent and accountable. Deep learning models pose unique technical barriers that make traditional explainability methods fall short. Many organizations don't deal very well with implementing transparency measures because of limited resources and resistance to change.
This paper breaks down the core reasons behind AI transparency failures by looking at real-life examples from leading tech companies. Complex model designs and data privacy needs often clash with transparency goals. On top of that, it explores organizational hurdles like misaligned leadership and resource problems that block transparency initiatives.
Our findings show that current transparency frameworks don't work well enough because of technical limits in explaining models and practical constraints that development teams face. This creates a growing divide between what stakeholders expect and what's actually possible. The paper looks at specific cases to identify common patterns in transparency failures and how they affect stakeholder trust.
We wrap up by sharing the most important lessons learned and suggest practical ways to tackle both technical and organizational barriers to AI transparency. This research gives tech leaders and practitioners practical insights to build AI systems that are more transparent and accountable.
Keywords AI Explainability, Model Transparency, Technical Barriers, Stakeholder Trust, Organizational Change, Deep Learning Interpretability, AI Accountability, Implementation Challenges, Privacy Constraints, Resource Management
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-03
Cite This Why AI Transparency Fails: Real-World Lessons from Tech Leaders - Sudheer Peddineni - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.37227
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.37227
Short DOI https://doi.org/g847z4
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