Transparency in AI: My Experience with Explainable AI Workshop

 

I’m thrilled to share my learning experience from the insightful workshop “Transparency in AI: Explainable AI”, organized by the IEEE Computer Society Student Branch Chapter at Alliance University.

The session offered a deep dive into how Artificial Intelligence (AI) systems can be made more interpretable, fair, and transparent values that are crucial for building trust between humans and machines.

Why Transparency in AI Matters

As AI systems become part of everyday life from healthcare diagnostics to financial decision-making it’s essential that we understand how AI models arrive at their conclusions.

The concept of Explainable AI (XAI) focuses on:

  • Making AI decisions understandable to humans.

  • Ensuring fairness and accountability in AI-driven results.

  • Allowing developers and researchers to debug and improve AI systems.

Without transparency, even the most accurate AI systems can face distrust and ethical challenges. This workshop emphasized how responsibility, ethics, and explainability go hand-in-hand in AI research and applications.

Key Takeaways from the Workshop

Here are some valuable insights I gained:

  1. Interpretable Models:
    The workshop discussed the difference between “black box” and “glass box” models, highlighting how interpretability can make AI systems more reliable.

  2. Techniques in Explainable AI:
    We explored practical tools like LIME (Local Interpretable Model-Agnostic Explanations), SHAP (SHapley Additive exPlanations), and Feature Importance Analysis, which help explain how models make decisions.

  3. Human-Centered AI Design:
    Building AI systems that communicate their reasoning clearly to users promotes trust and collaboration between humans and technology.

  4. Ethical Implications:
    Transparency is not just a technical goal—it’s a moral responsibility to ensure that AI aligns with human values and does not discriminate or mislead.

About the Organizers

The workshop was hosted by the IEEE Computer Society Student Branch Chapter at Alliance University, which continuously promotes innovation, technical learning, and ethical AI development among students.

It was a pleasure to interact with experts, faculty, and peers who share the same enthusiasm for AI ethics, interpretability, and responsible innovation.

Certificate of Participation


My Reflection

This experience strengthened my understanding that explainability is the foundation of trustworthy AI. As researchers, developers, and innovators, we must not only create high-performing models but also ensure they are transparent, fair, and understandable.

Workshops like these remind us that responsible AI development begins with awareness and education and I’m grateful to have been part of it.


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