Author:
Marinucci Ludovica,Mazzuca Claudia,Gangemi Aldo
Abstract
AbstractBiases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes serve a multifarious set of cognitive goals, while at the same time stressing their potential harmfulness. Recently, biases and stereotypes became the purview of heated debates in the machine learning community too. Researchers and developers are becoming increasingly aware of the fact that some biases, like gender and race biases, are entrenched in the algorithms some AI applications rely upon. Here, taking into account several existing approaches that address the problem of implicit biases and stereotypes, we propose that a strategy to cope with this phenomenon is to unmask those found in AI systems by understanding their cognitive dimension, rather than simply trying to correct algorithms. To this extent, we present a discussion bridging together findings from cognitive science and insights from machine learning that can be integrated in a state-of-the-art semantic network. Remarkably, this resource can be of assistance to scholars (e.g., cognitive and computer scientists) while at the same time contributing to refine AI regulations affecting social life. We show how only through a thorough understanding of the cognitive processes leading to biases, and through an interdisciplinary effort, we can make the best of AI technology.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Human-Computer Interaction,Philosophy
Reference120 articles.
1. Allemang D, Hendler J (2011) Semantic web for the working ontologist: effective modeling in RDFS and OWL. Elsevier
2. Amodio DM, Devine PG (2006) Stereotyping and evaluation in implicit race bias: evidence for independent constructs and unique effects on behavior. J Pers Soc Psychol 91(4):652–661
3. Araque O, Gatti L, Staiano J, Guerini M (2022) Depechemood++: a bilingual emotion lexicon built through simple yet powerful techniques. IEEE Trans Affect Comput 13(1):496–507
4. Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) DBpedia: a nucleus for a web of open data. In: Aberer K et al (eds) The Semantic Web. ISWC 2007, ASWC 2007. Lecture notes in computer science, vol 4825. Springer, Heidelberg, pp 722–735
5. Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA)
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献