Designing AI Using a Human-Centered Approach: Explainability and Accuracy Toward Trustworthiness

Abstract

One of the major criticisms of Artificial Intelligence is its lack of explainability. A claim is made by many critics that without knowing how an AI may derive a result or come to a given conclusion, it is impossible to trust in its outcomes. This problem is especially concerning when AI-based systems and applications fail to perform their tasks successfully. In this Special Issue Editorial, we focus on two main areas, explainable AI (XAI) and accuracy, and how both dimensions are critical to building trustworthy systems. We review prominent XAI design themes, leading to a reframing of the design and development effort that highlights the significance of the human, thereby demonstrating the importance of human-centered AI (HCAI). The HCAI approach advocates for a range of deliberate design-related decisions, such as those pertaining to multi-stakeholder engagement and the dissolving of disciplinary boundaries. This enables the consideration and integration of deep interdisciplinary knowledge, as evidenced in our example of social cognitive approaches to AI design. This Editorial then presents a discussion on ways forward, underscoring the value of a balanced approach to assessing the opportunities, risks and responsibilities associated with AI design. We conclude by presenting papers in the Special Issue and their contribution, pointing to future research endeavors.

The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation

“…In addition, AI systems are not always transparent, and it can be challenging to understand their decision-making processes. This lack of transparency makes errors difficult to detect and fix [67]. To address AI unpredictability, researchers have proposed various strategies.…”

“…[66] pointed out that unpredictable errors in AI systems can adversely affect user experience and societal impact. The authors of [67] emphasize the importance of designing AI with a human-centered approach to ensure explainability and accuracy, enhance trustworthiness, and mitigate the risks associated with unpredictable outcomes and unintended biases.…”

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

“…Finally, a human-centered approach helps to build trust in the information assessment detection. Users are more likely to trust a system that is transparent, accountable, and open to feedback (Schoenherr et al, 2023). A system that incorporates human expertise can help to provide these qualities, by ensuring that the decision-making process is transparent and that decisions are made based on sound ethical principles.…”

Formation of an Original Database and Development of Innovative Deep Learning Algorithms for Detecting Face Impersonation in Online Exams

“…For instance, studies conducted at the Massachusetts Institute of Technology (MIT) Media Lab have shown that facial recognition technology is more effective at detecting individuals with lighter skin, especially men, than those with darker skin and women [7]. According to the authors of [8], poorly written algorithms, the use of poisoned, incomplete, or biased training datasets could contribute to the marginalization of certain users.…”