Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems

TL;DR

This paper reviews the challenges and methods related to developing trustworthy and responsible AI systems that are fair, transparent, and ethical for human-centric decision-making across various sectors.

Contribution

It provides a comprehensive overview of AI biases, detection and mitigation techniques, evaluation metrics, and guidelines for fostering trustworthy AI models.

Findings

Analysis of AI bias types and their impacts

Discussion of detection and mitigation methods

Identification of open challenges and guidelines

Abstract

Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents significant ethical challenges related to bias and transparency. AI applications are hugely impacted by biases, presenting inconsistent and unreliable findings, leading to significant costs and consequences, highlighting and perpetuating inequalities and unequal access to resources. Hence, developing safe, reliable, ethical, and Trustworthy AI systems is essential. Our team of researchers working with Trustworthy and Responsible AI, part of the Transdisciplinary Scholarship Initiative within the University of Calgary, conducts research on Trustworthy and Responsible AI, including fairness, bias mitigation, reproducibility, generalization, interpretability, and authenticity. In this paper, we review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias. We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making, as well as guidelines to foster Responsible and Trustworthy AI models.