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Human-Centered Artificial Intelligence for Intelligent Decision Support

Harshwardhansinh Chauhan * , Rocky Upadhyay , Sanjay Sharma , Shivam Upadhyay
* Corresponding author: Harshwardhansinh Chauhan ( hwkc392@gmail.com )
Available online July 1, 2026
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Abstract

Artificial Intelligence (AI) is now becoming prevalent in other important sectors such as healthcare, finance, governance, industry, etc., where decision support systems (DSS) are utilized. AI models can analyze massive amounts of data and identify patterns that are not easily noticeable to humans, but their black-box nature can make them hard to understand, explain, and trust. Human-Centered Artificial Intelligence (HCAI) has come to be a design paradigm where humans, not algorithms, are at the heart of the decision-making process with a focus on explainability, calibrated trust, meaningful human control, and collaborative human--AI interaction. In this paper, a theoretical and conceptual research on the HCAI for Intelligent Decision Support Systems (IDSS) is presented. It summarizes the basic concepts of HCAI, provides a discussion of mechanisms of explainability and trust calibration, and presents a five-layer conceptual framework (Data and Knowledge, AI Reasoning, Decision Logic, Human Interaction, Governance and Trust layers) for designing human-centric intelligent decision support architectures. Prominent design principles, typical application scenarios, and open research questions are explored, as well as future tracts such as neuro symbolic reasoning, reinforcement learning from human feedback, and agentic orchestration with human supervision. This paper does not present an implementation or empirical evaluation, but is more of a conceptual synthesis to support researchers and practitioners in designing intelligent decision support systems that are more transparent, trustworthy, and human-aligned.

Keywords

Human-Centered AI decision support systems explainable AI trust calibration intelligent systems

Introduction

In the past few years, the use of Artificial Intelligence for human decision making has increased at a drastic rate in almost every sector of the society. In the last decade, investments in AI have skyrocketed worldwide, and companies from all regions are already using AI tools to boost their operations. Over the last decade, global investment in AI has grown significantly, with AI tools reported to be widely used by companies across regions. Use of intelligent decision support systems (IDSS) that integrate AI-based inference with interactive interfaces is growing to assist clinicians in disease diagnosis, assist operators of critical infrastructure, assist in resource allocation and assist managers for financial risk assessment. These systems offer quicker analysis, greater scope of potential solutions, and less reliance on scarce human resources.

While AI-based decision support can offer these benefits, it also has its drawbacks. Many successful predictive models are actually black box models, which give out predictions without any explanation or justification of how or why the prediction was made. This opacity has led to overreliance and underreliance in trust with users relying too much on the AI recommendation and rejecting useful suggestions [1], [5]. Moreover, biases found in the training data of AI systems can be amplified, raising ethical concerns when they make mistakes, and leaving users uneasy when their interactions with the system are beyond their control and without explanation. In high-stakes environments like healthcare, criminal justice, and safety critical systems, a poorly researched recommendation can have significant repercussions, which is particularly troublesome.

Human-Centered Artificial Intelligence (HCAI) has become a solution to these challenges. HCAI is not about full automation; it is about having systems that have high automation levels, with high levels of human control and human judgment, values and oversight, so that human judgment, values and oversight become part of the decision process [2] and [3]. When it comes to decision support, this approach is not to view AI as a decision maker, but as an assistant that supports the human thought process and keeps the human well informed, accountable, and in charge of the decision.

This paper provides theoretical and conceptual analysis of HCAI for intelligent decision making support. There is no implementation, prototype, or empirical evaluation offered; the goal is to take concepts from the HCI, EAI, and decision support fields and combine them into a unified concept model. The rest of the paper is organized as follows. Section 2 presents the review methodology, followed by related work in Section 3. Section 4 presents the foundations of HCAI. Explainability, trust, and human–AI collaboration are discussed in Section 5. Section 6 presents the five-layer conceptual model for HCAI-based intelligent decision support. The design principles are described in Section 7, representative application areas in Section 8, open challenges in Section 9, future research directions in Section 10, and the conclusion in Section 11.

Complete Article

The complete article, including all figures, tables, equations and algorithms, is available in the official publication PDF.

Conclusion

In this paper, a theoretical and conceptual discussion of Human-Centered Artificial Intelligence for Intelligent Decision Support has been provided. A conceptual framework with five layers (Data and Knowledge, AI Reasoning, Decision Logic, Human Interaction and Governance and Trust layers) was then suggested as a reference model for designing human-centered decision support architectures, after reviewing the foundational principles of HCAI and the mechanisms of explainability, trust calibration, and human–AI collaboration. The design principles, example application areas, and open questions were discussed, as well as the future directions of research such as: neuro-symbolic reasoning, reinforcement learning from human feedback, and human supervised agentic orchestration. The key takeaway from this synthesis is that technical accuracy does not automatically result in effective or trustworthy decision support, but that trust must be built and earned through the design of systems that are transparent, accountable, and collaborative, and that human oversight is needed to make effective use of AI.

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