Transparent AI: The Case for Interpretability and Explainability

Dhanesh Ramachandram, Himanshu Joshi, Judy Zhu, Dhari Gandhi, Lucas Hartman +1 more
7/31/2025
cs.LGcs.AIcs.CY

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation. Leveraging our role as a leading institute in advancing AI research and enabling industry adoption, we present key insights and lessons learned from practical interpretability applications across diverse domains. This paper offers actionable strategies and implementation guidance tailored to organizations at varying stages of AI maturity, emphasizing the integration of interpretability as a core design principle rather than a retrospective add-on.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Interpretability as Alignment: Making Internal Understanding a Design Principle

86% match2025

Explainable AI does not provide the explanations end-users are asking for

86% match2023

(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility

85% match2023

ClarifAI: Enhancing AI Interpretability and Transparency through Case-Based Reasoning and Ontology-Driven Approach for Improved Decision-Making

84% match2025

A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow

84% match2025

Transparent Adaptive Learning via Data-Centric Multimodal Explainable AI

83% match2025

Cite this paper

@article{ramachandram2025transparent,
  title  = {Transparent AI: The Case for Interpretability and Explainability},
  author = {Dhanesh Ramachandram and Himanshu Joshi and Judy Zhu and Dhari Gandhi and Lucas Hartman and Ananya Raval},
  year   = {2025},
  eprint = {2507.23535},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.23535v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2507.23535.svg)](https://codeofpaper.com/paper/2507.23535)

Discussion