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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.15550 |
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| _version_ | 1866916376815337472 |
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| author | Dehghani, Farzaneh Dibaji, Mahsa Anzum, Fahim Dey, Lily Basdemir, Alican Bayat, Sayeh Boucher, Jean-Christophe Drew, Steve Eaton, Sarah Elaine Frayne, Richard Ginde, Gouri Harris, Ashley Ioannou, Yani Lebel, Catherine Lysack, John Arzuaga, Leslie Salgado Stanley, Emma Souza, Roberto Santos, Ronnie de Souza Wells, Lana Williamson, Tyler Wilms, Matthias Wahid, Zaman Ungrin, Mark Gavrilova, Marina Bento, Mariana |
| author_facet | Dehghani, Farzaneh Dibaji, Mahsa Anzum, Fahim Dey, Lily Basdemir, Alican Bayat, Sayeh Boucher, Jean-Christophe Drew, Steve Eaton, Sarah Elaine Frayne, Richard Ginde, Gouri Harris, Ashley Ioannou, Yani Lebel, Catherine Lysack, John Arzuaga, Leslie Salgado Stanley, Emma Souza, Roberto Santos, Ronnie de Souza Wells, Lana Williamson, Tyler Wilms, Matthias Wahid, Zaman Ungrin, Mark Gavrilova, Marina Bento, Mariana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15550 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems Dehghani, Farzaneh Dibaji, Mahsa Anzum, Fahim Dey, Lily Basdemir, Alican Bayat, Sayeh Boucher, Jean-Christophe Drew, Steve Eaton, Sarah Elaine Frayne, Richard Ginde, Gouri Harris, Ashley Ioannou, Yani Lebel, Catherine Lysack, John Arzuaga, Leslie Salgado Stanley, Emma Souza, Roberto Santos, Ronnie de Souza Wells, Lana Williamson, Tyler Wilms, Matthias Wahid, Zaman Ungrin, Mark Gavrilova, Marina Bento, Mariana Artificial Intelligence 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. |
| title | Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2408.15550 |