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Neural-symbolic integration for fairness in AI

Wagner, B. & d'Avila Garcez, A. S. ORCID: 0000-0001-7375-9518 (2021). Neural-symbolic integration for fairness in AI. In: CEUR Workshop Proceedings. AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), 22-24 Mar 2021, California, USA.


Deep learning has achieved state-of-the-art results in various application domains ranging from image recognition to language translation and game playing. However, it is now generally accepted that deep learning alone has not been able to satisfy the requirement of fairness and, ultimately, trust in Artificial Intelligence (AI). In this paper, we propose an interactive neural-symbolic approach for fairness in AI based on the Logic Tensor Network (LTN) framework. We show that the extraction of symbolic knowledge from LTN-based deep networks combined with fairness constraints offer a general method for instilling fairness into deep networks via continual learning. Explainable AI approaches which otherwise could identify but not fix fairness issues are shown to be enriched with an ability to improve fairness results. Experimental results on three real-world data sets used to predict income, credit risk and recidivism in financial applications show that our approach can satisfy fairness metrics while maintaining state-of-the-art classification performance.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Publisher Keywords: Neurosymbolic AI, Deep Learning with Knowledge Representation, Fairness, Explainability
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Science & Technology > Computer Science
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