The PSyKE Technology for Trustworthy Artificial Intelligence

被引:6
作者
Calegari, Roberta [1 ]
Sabbatini, Federico [2 ]
机构
[1] Alma Mater Studiorum Univ Bologna, Alma AI Alma Mater Res Inst Human, Ctr Artificial Intelligence, Bologna, Italy
[2] Univ Urbino, Dept Pure & Appl Sci DiSPeA, Via S Chiara 27, I-61029 Urbino, Italy
来源
AIXIA 2022 - ADVANCES IN ARTIFICIAL INTELLIGENCE | 2023年 / 13796卷
基金
欧盟地平线“2020”;
关键词
Trustworthy Artificial Intelligence; Transparency; Explainability; Symbolic knowledge extraction; PSyKE; KNOWLEDGE EXTRACTION; ALGORITHM;
D O I
10.1007/978-3-031-27181-6_1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Transparency is one of the "Ethical Principles in the Context of AI Systems" as described in the Ethics Guidelines for Trustworthy Artificial Intelligence (TAI). It is closely linked to four other principles - respect for human autonomy, prevention of harm, traceability and explainability - and involves numerous ways in which opaqueness can have undesirable impacts, such as discrimination, inequality, segregation, marginalisation, and manipulation. The opaqueness of many AI tools and the inability to understand the underpinning black boxes contradicts these principles as well as prevents people from fully trusting them. In this paper we discuss the PSyKE technology, a platform providing general-purpose support to symbolic knowledge extraction from different sorts of black-box predictors via many extraction algorithms. The extracted knowledge results are easily injectable into existing AI assets making them meet the transparency TAI requirement.
引用
收藏
页码:3 / 16
页数:14
相关论文
共 22 条
[1]  
Baesens Bart, 2001, P INT C INF SYST ICI, P159
[2]  
Breiman L., 1984, CLASSIFICATION REGRE, DOI DOI 10.1201/9781315139470
[3]   Logic-based technologies for multi-agent systems: a systematic literature review [J].
Calegari, Roberta ;
Ciatto, Giovanni ;
Mascardi, Viviana ;
Omicini, Andrea .
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 2021, 35 (01)
[4]   On the integration of symbolic and sub-symbolic techniques for XAI: A survey [J].
Calegari, Roberta ;
Ciatto, Giovanni ;
Omicini, Andrea .
INTELLIGENZA ARTIFICIALE, 2020, 14 (01) :7-32
[5]   2P-Kt: A logic-based ecosystem for symbolic AI [J].
Ciatto, Giovanni ;
Calegari, Roberta ;
Omicini, Andrea .
SOFTWAREX, 2021, 16
[6]  
Craven M.W., 1994, Proceedings of the Eleventh International Conference on Machine Learning, P37, DOI [DOI 10.1016/B978-1-55860-335-6.50013-1, 10.1016/B978-1-55860-335-6.50013-1]
[7]  
Craven MW, 1996, ADV NEUR IN, V8, P24
[8]  
European Commission, 2021, AI ACT PROP REG EUR
[9]  
European Commission Directorate-General for Communications Networks C., 2019, Technology: Ethics Guidelines for Trustworthy Artificial Intelligence, DOI DOI 10.2759/346720
[10]  
Franco L, 2007, LECT NOTES COMPUT SC, V4507, P1004