Explainability, Interpretability, and Accountability in Explainable AI: A Qualitative Analysis of XAI's Sectoral Usability

The world that we live in today, is dominated by technological advancements. Many breakthroughs dominate our society today. Among them, Artificial Intelligence (AI) has emerged to be a prominent one. It is no longer relegated to being strictly a sci-fi Hollywood blockbuster project of the future. Today, it is a part and parcel of our daily life and human decision- making processes. It is slowly finding its imprint on several sectors with each passing moment. This, in turn, directly affects human well-being, but also poses us a growing question of trust in these AI systems. With time, this inquiry has grown even more urgent, one which requires an immediate addressal. Artificial Intelligence serves a lot of purposes but has made substantial contributions in crucial sectors like education, healthcare, and finance. Within these, the incorporation of Artificial Intelligence can have direct consequences on individuals' lives. However, despite holding a life-changing potential, there exists an inadvertent issue of public trust in AI and its related technologies. This is primarily due to the "black-box" nature of many models. This makes their decision- making processes opaque. It also results in them being highly difficult to interpret. 1 In order to tackle this challenge, Explainable AI (XAI) has emerged as a crucial response. The purpose of XAI is to make algorithmic outcomes more transparent, interpretable, and accountable. In simpler terms, Explainable AI focuses on making the Artificial Intelligence technology more comprehensible for humans. 2 The aim of this paper is to explore the role of Explainable AI in building and sustaining public trust. It will focus specifically on the applications of XAI in fields like education, healthcare, and finance. Via it, the paper seeks to demonstrate how enhancing transparency and accountability through XAI can foster greater trust and responsible adoption of AI in these critical sectors. To achieve the same, the paper will adopt a qualitative approach. It will be informed by published literature, case examples and policy briefings. This will make it possible to critically consider how explainability affects perceptions of fairness, dependability, and liability. In the education sector, the paper will delve into how transparent grading and admission algorithms can enhance acceptance among students, parents, and educators. In the field of healthcare, it will take a look into the significance of interpretability. This allows for enhanced clinical decision support systems. In turn, it impacts life-altering judgements. These not only require accuracy but also human comprehension. 3 Likewise, explainability in finance can lead to higher credit scoring, fraud detection, and robo-advisory systems. This enables streamlined mechanisms to safeguard consumer trust and compliance with regulatory frameworks. 4 Lastly, the paper will identify cross-sectoral themes. These include themes related to the balance between accuracy and interpretability, ethical dangers of oversimplified explanations, and the role of cultural and social contexts in trust-building. At the end, the paper will also outline future directions. Moreover, it will also emphasise on the need for standardised frameworks, policy interventions, and greater public engagement in shaping trustworthy AI systems. In discussing XAI in relation to the technology discourse, with a focus on ethics and accountability, this paper will further contextualise its significance for responsible innovation and a sustainable public trust in AI decision-making.