Key points are not available for this paper at this time.
In contemporary society, decision-making processes have grown increasingly intricate across sectors such as business, healthcare, finance, and technology. To navigate this complexity, sophisticated tools like Decision Support Systems (DSS), Expert Systems (ES), Recommender Systems, and explainable Artificial Intelligence (XAI) have emerged, all aimed at improving decision-making efficiency. DSS, developed in the 1960s and 1970s, strategically integrates automation and data processing to enhance human decision-making. Unlike ES, which attempts to replicate human expertise, DSS collaboratively assists decision-makers. ES excel in specialized domains by mimicking human decision-making processes. Recommender Systems, are user-centric, transform digital interactions by analyzing preferences to provide personalized recommendations. XAI addresses the need for transparency in AI-driven decisions by clarifying outcomes from complex algorithms. In this paper, we aim to bridge the gap between traditional decision-making methods and emerging explanation-driven architectures by conducting a comparative analysis of DSS, ES, Recommender Systems, and XAI. It explores their historical origins, objectives, methodologies, applications, challenges, limitations, and user contexts. By elucidating their strengths and limitations, this analysis offers insights for decision-makers, researchers, and practitioners aiming to improve decision-making across diverse domains. At the end we explain the case study on software defect prediction.
Ravi et al. (Fri,) studied this question.