Enhancing Transparency and Understanding in AI Decision-Making Processes

@article{1706039,
      author = {Vinayak Pillai},
      title = {Enhancing Transparency and Understanding in AI Decision-Making Processes},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {1},
      pages = {168-172},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706039.pdf},
      abstract = {Artificial Intelligence (AI) systems are integral to sectors like healthcare, finance, and criminal justice, offering superior decision-making capabilities. However, the opacity of these processes can undermine user trust and accountability. This paper explores methods to enhance transparency and understanding in AI, including model-agnostic approaches like LIME and SHAP, and intrinsically interpretable models such as decision trees and rule-based systems. It also proposes strategies like hybrid models, user-centric design, and regulatory frameworks to enforce transparency. Case studies in healthcare and finance demonstrate these strategies' practical applications, aiming to balance AI's technical performance with transparency and ethical deployment.},
      keywords = {AI transparency, explainable AI, interpretability, decision-making processes},
      month = {July},
  }