Enlightened XAI
P. Hemalatha, J. Manikandan, B. Balaji, V. Sujitha · 2025
As artificial intelligence (AI) algorithms become increasingly integral parts of society, their opaqueness and inaccessibility raise significant ethical concerns. Explainable AI (XAI) addresses these challenges by providing insight into how AI models make decisions. In this chapter, we explore some ethical considerations surrounding XAI systems and the associated fairness concerns. This paper highlights the significance of transparency and interpretability for artificial intelligence algorithms and their associated risks such as biased decision making. Additionally, this chapter addresses the challenges of attaining fairness within XAI systems and their need to address algorithmic biases. Furthermore, frameworks and guidelines that ensure such systems’ responsible development and deployment uphold ethical principles while providing greater fairness are also reviewed. This chapter focuses on understanding the impact of artificial intelligence (AI) on society and individuals and emphasizes ethical guidelines in AI development. Additionally, its contents examine the potential consequences of biased AI decision making and real-world examples involving fairness issues related to AI development. This study investigates the trade-off between fairness and interpretability, assessing fairness in explainable AI models and successful implementations of ethical XAI through case studies and best practices. Also included are practical measures for incorporating ethics and fairness into XAI projects and public concerns about AI systems used for healthcare, finance, or criminal justice purposes. This chapter discusses emerging trends and research directions in ethical AI, placing special emphasis on interdisciplinary when developing ethical XAI applications while emphasizing fairness and transparency as vital ingredients of shaping its future, calling for responsible use and development of XAI so that its beneficial societal effects may be maximized.