Knowledge Tree
Relations (1)
related 2.58 — strongly supporting 5 facts
Machine learning and fairness are intrinsically linked as the field actively addresses bias and ethical concerns, as evidenced by the survey on fairness in machine learning [1] and the development of tractable models for fairness [2]. Furthermore, fairness is a critical, formalizable challenge within both traditional machine learning [3] and modern large language model architectures [4], often necessitating interpretable representations to mitigate systemic bias [5].
Facts (5)
Sources
A Survey on the Theory and Mechanism of Large Language Models arxiv.org 2 facts
claimThe current landscape of large language models presents new challenges for defining and formalizing concepts like 'robustness', 'fairness', and 'privacy' compared to traditional machine learning, as noted by Chang et al. (2024), Anwar et al. (2024), Dominguez-Olmedo et al. (2025), and Hardt and Mendler-Dünner (2025).
claimTraditional machine learning literature extensively analyzed robustness (Muravev and Petiushko, 2021; Ruan et al., 2021), fairness (Kleinberg et al., 2016; Liu et al., 2019), and privacy (Li et al., 2017; Kairouz et al., 2015) because these concepts were well-defined and formalizable using precise mathematical objectives.
Papers - Dr Vaishak Belle vaishakbelle.github.io 1 fact
referenceThe paper 'Fairness in Machine Learning with Tractable Models' was published in Knowledge-Based Systems in 2021 by authors M. Varley and V. Belle.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com 1 fact
claimMehrabi, Morstatter, Saxena, Lerman, and Galstyan published 'A survey on bias and fairness in machine learning' in ACM Computing Surveys in 2021.
Call for Papers: Special Session on KR and Machine Learning kr.org 1 fact
claimThe success of Machine Learning systems has highlighted issues like explainability, bias, and fairness, which encourages the integration of symbolic or interpretable representations into AI systems.