Visual Analysis of Discrimination in Machine Learning

• URL: http://arxiv.org/abs/2007.15182v1
• Date: Thu, 30 Jul 2020 02:07:03 GMT
• Title: Visual Analysis of Discrimination in Machine Learning
• Abstract summary: We propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis of discrimination in machine learning. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately.
• Score: 40.678688013911994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination.

Related papers

• A Review on Discriminative Self-supervised Learning Methods in Computer Vision [5.5547914920738]
  Self-supervised learning (SSL) has rapidly emerged as a transformative approach in computer vision.<n>This review presents a comprehensive analysis of discriminative SSL methods, which focus on learning representations by solving pretext tasks that do not require human labels.
  arXiv  Detail & Related papers  (2024-05-08T11:15:20Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)
• Self-Supervised Visual Representation Learning with Semantic Grouping [50.14703605659837]
  We tackle the problem of learning visual representations from unlabeled scene-centric data. We propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning.
  arXiv  Detail & Related papers  (2022-05-30T17:50:59Z)
• CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging [7.137224324997715]
  Context-Aware instance Discrimination (CAiD) aims to improve instance discrimination learning in medical images. CAiD provides finer and more discriminative information encoded from a diverse local context. As open science, all codes and pre-trained models are available on our GitHub page.
  arXiv  Detail & Related papers  (2022-04-15T06:45:10Z)
• fairadapt: Causal Reasoning for Fair Data Pre-processing [2.1915057426589746]
  This manuscript describes the R-package fairadapt, which implements a causal inference pre-processing method. We discuss appropriate relaxations which assume certain causal pathways from the sensitive attribute to the outcome are not discriminatory.
  arXiv  Detail & Related papers  (2021-10-19T18:48:28Z)
• Adversarial Stacked Auto-Encoders for Fair Representation Learning [1.061960673667643]
  We propose a new fair representation learning approach that leverages different levels of representation of data to tighten the fairness bounds of the learned representation. Our results show that stacking different auto-encoders and enforcing fairness at different latent spaces result in an improvement of fairness compared to other existing approaches.
  arXiv  Detail & Related papers  (2021-07-27T13:49:18Z)
• Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination [53.3082498402884]
  A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. We present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning.
  arXiv  Detail & Related papers  (2020-09-25T05:48:56Z)
• Interpretable Representations in Explainable AI: From Theory to Practice [7.031336702345381]
  Interpretable representations are the backbone of many explainers that target black-box predictive systems. We study properties of interpretable representations that encode presence and absence of human-comprehensible concepts.
  arXiv  Detail & Related papers  (2020-08-16T21:44:03Z)
• Self-supervised Learning from a Multi-view Perspective [121.63655399591681]
  We show that self-supervised representations can extract task-relevant information and discard task-irrelevant information. Our theoretical framework paves the way to a larger space of self-supervised learning objective design.
  arXiv  Detail & Related papers  (2020-06-10T00:21:35Z)
• Fairness by Learning Orthogonal Disentangled Representations [50.82638766862974]
  We propose a novel disentanglement approach to invariant representation problem. We enforce the meaningful representation to be agnostic to sensitive information by entropy. The proposed approach is evaluated on five publicly available datasets.
  arXiv  Detail & Related papers  (2020-03-12T11:09:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.