Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making

被引:35
作者
Cheong, Ben Chester [1 ,2 ]
机构
[1] Singapore Univ Social Sci, Sch Law, Singapore, Singapore
[2] Univ Cambridge, Cambridge, England
来源
FRONTIERS IN HUMAN DYNAMICS | 2024年 / 6卷
关键词
AI; wellbeing; transparency; accountability and policy; governance; INFORMATION; LIABILITY; SECRETS;
D O I
10.3389/fhumd.2024.1421273
中图分类号
C921 [人口统计学];
学科分类号
摘要
The rapid integration of artificial intelligence (AI) systems into various domains has raised concerns about their impact on individual and societal wellbeing, particularly due to the lack of transparency and accountability in their decision-making processes. This review aims to provide an overview of the key legal and ethical challenges associated with implementing transparency and accountability in AI systems. The review identifies four main thematic areas: technical approaches, legal and regulatory frameworks, ethical and societal considerations, and interdisciplinary and multi-stakeholder approaches. By synthesizing the current state of research and proposing key strategies for policymakers, this review contributes to the ongoing discourse on responsible AI governance and lays the foundation for future research in this critical area. Ultimately, the goal is to promote individual and societal wellbeing by ensuring that AI systems are developed and deployed in a transparent, accountable, and ethical manner.
引用
收藏
页数:11
相关论文
共 91 条
[1]  
AI Incident Database (n.d.), Partnership on AI
[2]   Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability [J].
Ananny, Mike ;
Crawford, Kate .
NEW MEDIA & SOCIETY, 2018, 20 (03) :973-989
[3]  
Angwin J, 2016, ProPublica
[4]  
[Anonymous], 2018, Guidelines on Automated Individual Decision-Making and Profiling for the Purposes of Regulation 2016/679
[5]  
[Anonymous], 2019, ETHICALLY ALIGNED DESIGN: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems
[6]  
Arya Vijay, 2019, arXiv1909.03012
[7]  
Baker S., 2023, J. Artif. Intell. Res, V68, P213, DOI [10.1109/SPMB59478.2023.10372636, DOI 10.1109/SPMB59478.2023.10372636]
[8]   Big Data's Disparate Impact [J].
Barocas, Solon ;
Selbst, Andrew D. .
CALIFORNIA LAW REVIEW, 2016, 104 (03) :671-732
[9]  
Benaich N., 2020, STATE AI REPORT 2020
[10]   The black box problem revisited. Real and imaginary challenges for automated legal decision making [J].
Brozek, Bartosz ;
Furman, Michal ;
Jakubiec, Marek ;
Kucharzyk, Bartlomiej .
ARTIFICIAL INTELLIGENCE AND LAW, 2024, 32 (02) :427-440