Summary of Trustworthy and Responsible Ai For Human-centric Autonomous Decision-making Systems, by Farzaneh Dehghani (1 et al.
Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems
by Farzaneh Dehghani, Mahsa Dibaji, Fahim Anzum, Lily Dey, Alican Basdemir, Sayeh Bayat, Jean-Christophe Boucher, Steve Drew, Sarah Elaine Eaton, Richard Frayne, Gouri Ginde, Ashley Harris, Yani Ioannou, Catherine Lebel, John Lysack, Leslie Salgado Arzuaga, Emma Stanley, Roberto Souza, Ronnie de Souza Santos, Lana Wells, Tyler Williamson, Matthias Wilms, Zaman Wahid, Mark Ungrin, Marina Gavrilova, Mariana Bento
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the pressing issue of ensuring that Artificial Intelligence (AI) decision-making processes are trustworthy, transparent, and free from biases. To address these concerns, researchers propose a framework for developing AI systems that prioritize ethics, safety, and reliability. The study highlights the significant impact of AI bias on various sectors, including healthcare and economics, leading to inconsistent findings, unequal access to resources, and perpetuated inequalities. By developing trustworthy AI systems, the authors aim to contribute to advancements in these sectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI has the power to transform decision-making processes across industries, but its “black box” nature raises ethical concerns about bias and transparency. This paper focuses on making AI more trustworthy by addressing these issues. The researchers point out that AI biases can lead to unreliable findings, exacerbate inequalities, and hinder equal access to resources. To overcome these challenges, the authors suggest a framework for developing AI systems that prioritize ethics, safety, and reliability. |
