The black box problem revisited. Real and imaginary challenges for automated legal decision making

被引:34
作者
Brozek, Bartosz [1 ,2 ]
Furman, Michal [2 ]
Jakubiec, Marek [1 ]
Kucharzyk, Bartlomiej [1 ]
机构
[1] Jagiellonian Univ, Fac Law & Adm, Krakow, Poland
[2] Jagiellonian Univ, Copernicus Ctr Interdisciplinary Studies, Krakow, Poland
关键词
Black box problem; Explainable AI; AI and law; Legal decision-making; Automated decision-making;
D O I
10.1007/s10506-023-09356-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper addresses the black-box problem in artificial intelligence (AI), and the related problem of explainability of AI in the legal context. We argue, first, that the black box problem is, in fact, a superficial one as it results from an overlap of four different - albeit interconnected - issues: the opacity problem, the strangeness problem, the unpredictability problem, and the justification problem. Thus, we propose a framework for discussing both the black box problem and the explainability of AI. We argue further that contrary to often defended claims the opacity issue is not a genuine problem. We also dismiss the justification problem. Further, we describe the tensions involved in the strangeness and unpredictability problems and suggest some ways to alleviate them.
引用
收藏
页码:427 / 440
页数:14
相关论文
共 59 条
[1]  
Alexy Robert, 2009, A Theory of Legal Argumentation: The Theory of Rational Discourse as Theory of Legal Justification
[2]   Explanation in AI and law: Past, present and future [J].
Atkinson, Katie ;
Bench-Capon, Trevor ;
Bollegala, Danushka .
ARTIFICIAL INTELLIGENCE, 2020, 289 (289)
[3]   The Unconscious Mind [J].
Bargh, John A. ;
Morsella, Ezequiel .
PERSPECTIVES ON PSYCHOLOGICAL SCIENCE, 2008, 3 (01) :73-79
[4]   Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI [J].
Barredo Arrieta, Alejandro ;
Diaz-Rodriguez, Natalia ;
Del Ser, Javier ;
Bennetot, Adrien ;
Tabik, Siham ;
Barbado, Alberto ;
Garcia, Salvador ;
Gil-Lopez, Sergio ;
Molina, Daniel ;
Benjamins, Richard ;
Chatila, Raja ;
Herrera, Francisco .
INFORMATION FUSION, 2020, 58 :82-115
[5]   Legal requirements on explainability in machine learning [J].
Bibal, Adrien ;
Lognoul, Michael ;
de Streel, Alexandre ;
Frenay, Benoit .
ARTIFICIAL INTELLIGENCE AND LAW, 2021, 29 (02) :149-169
[6]  
Bloom Paul., 2004, Descartes' Baby: How the Science of Child Development Explains What Makes Us Human
[7]   The Human Black-Box: The Illusion of Understanding Human Better Than Algorithmic Decision-Making [J].
Bonezzi, Andrea ;
Ostinelli, Massimiliano ;
Melzner, Johann .
JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL, 2022, 151 (09) :2250-2258
[8]   Strengthening legal protection against discrimination by algorithms and artificial intelligence [J].
Borgesius, Frederik J. Zuiderveen .
INTERNATIONAL JOURNAL OF HUMAN RIGHTS, 2020, 24 (10) :1572-1593
[9]  
Broek B., 2018, EXPLAINING MIND, P149
[10]  
Brozek B, 2020, LEGAL MIND: A NEW INTRODUCTION TO LEGAL EPISTEMOLOGY, P1, DOI 10.1017/9781108695084