Shedding Light on AI Algorithms: a Deep Dive into Explainable Artificial Intelligence

EasyChair Preprint no. 12311

Abstract

Artificial Intelligence (AI) algorithms are increasingly pervasive in various domains, from healthcare to finance, yet their opacity poses significant challenges to their adoption and trustworthiness. Explainable Artificial Intelligence (XAI) has emerged as a critical field aimed at enhancing the transparency and interpretability of AI systems, enabling users to understand the rationale behind their decisions. This paper provides a comprehensive overview and analysis of XAI techniques, methodologies, and challenges. The first part of the paper delves into the importance of XAI in ensuring the accountability, fairness, and reliability of AI systems. It discusses the ethical implications of opaque algorithms and the growing demand for transparency in decision-making processes, particularly in high-stakes applications such as autonomous vehicles and medical diagnosis. The second part offers a taxonomy of XAI methods, categorizing them into model-specific and model-agnostic approaches. Model-specific techniques, including feature importance analysis, attention mechanisms, and decision trees, are examined in detail, highlighting their strengths and limitations. The paper discusses future directions and emerging trends in XAI research, including the integration of human-centric explanations, adversarial robustness, and the development of standardized evaluation metrics for explainability. It underscores the need for interdisciplinary collaboration between computer scientists, ethicists, psychologists, and domain experts to address the multifaceted challenges of XAI comprehensively.

Keyphrases: Artificial Intelligence, Explainable Artificial Intelligence, transparency

@Booklet{EasyChair:12311,
  author = {Jane Elsa and Jack Doruk},
  title = {Shedding Light on AI Algorithms: a Deep Dive into Explainable Artificial Intelligence},
  howpublished = {EasyChair Preprint no. 12311},

  year = {EasyChair, 2024}}