Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems
<|reference_start|>Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems: 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.<|reference_end|>
@article{dehghani2024trustworthy,
    title={Trustworthy and Responsible AI for Human-Centric Autonomous
  Decision-Making Systems},
    author={Farzaneh Dehghani (1,2), Mahsa Dibaji (3), Fahim Anzum (4), Lily Dey
  (4), Alican Basdemir (5), Sayeh Bayat (1,2,6), Jean-Christophe Boucher (7),
  Steve Drew (3), Sarah Elaine Eaton (8), Richard Frayne (9), Gouri Ginde (3),
  Ashley Harris (9), Yani Ioannou (3), Catherine Lebel (9), John Lysack (9),
  Leslie Salgado Arzuaga (10), Emma Stanley (1), Roberto Souza (2,3), Ronnie de
  Souza Santos (3), Lana Wells (11), Tyler Williamson (12), Matthias Wilms (9),
  Zaman Wahid (4), Mark Ungrin (13), Marina Gavrilova (4), Mariana Bento
  (1,2,3) ((1) Department of Biomedical Engineering, University of Calgary,
  Calgary, Canada, (2) Hotchkiss Brain Institute, University of Calgary,
  Calgary, Canada, (3) Department of Electrical and Software Engineering,
  University of Calgary, Calgary, Canada, (4) Department of Computer Science,
  University of Calgary, Calgary, Canada, (5) Department of Philosophy,
  University of Calgary, Calgary, Canada, (6) Department of Geomatics
  Engineering, University of Calgary, Calgary, Canada, (7) Department of
  Political Science, University of Calgary, Calgary, Canada, (8) Werklund
  School of Education, Specialization, Leadership, University of Calgary,
  Calgary, Canada, (9) Cumming School of Medicine, Department of Radiology,
  University of Calgary, Calgary, Canada, (10) Department of Communication,
  Media, and Film, University of Calgary, Calgary, Canada, (11) Faculty of
  Social Work, University of Calgary, Calgary, Canada, (12) Centre for Health
  Informatics, University of Calgary, Calgary, Canada, (13) Faculty of
  Veterinary Medicine, University of Calgary, Calgary, Canada)},
    journal={arXiv preprint arXiv:2408.15550},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15550},
    primaryClass={cs.AI}
}