[Paper Review] ViBE: A Tool for Measuring and Mitigating Bias in Image Datasets

Angelina Wang, Arvind Narayanan+1 more|arXiv (Cornell University)|Apr 16, 2020

Generative Adversarial Networks and Image Synthesis77 references4 citations

TL;DR

ViBE (REVISE) is a tool for proactively detecting and mitigating bias in image datasets across three dimensions: object-based, person-based, and geography-based. It analyzes visual data for imbalances in object representation, human portrayal, and geographic diversity, then suggests actionable mitigation strategies to reduce bias before model deployment.

ABSTRACT

Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. Our work tackles this issue and enables the preemptive analysis of large-scale datasets. REVISE (REvealing VIsual biaSEs) is a tool that assists in the investigation of a visual dataset, surfacing potential biases along three dimensions: (1) object-based, (2) person-based, and (3) geography-based. Object-based biases relate to the size, context, or diversity of the depicted objects. Person-based metrics focus on analyzing the portrayal of people within the dataset. Geography-based analyses consider the representation of different geographic locations. These three dimensions are deeply intertwined in how they interact to bias a dataset, and REVISE sheds light on this; the responsibility then lies with the user to consider the cultural and historical context, and to determine which of the revealed biases may be problematic. The tool further assists the user by suggesting actionable steps that may be taken to mitigate the revealed biases. Overall, the key aim of our work is to tackle the machine learning bias problem early in the pipeline. REVISE is available at this https URL

Motivation & Objective

• To address the challenge of undetected bias in machine learning datasets that emerges only after model deployment.
• To enable early detection of bias in large-scale image datasets through systematic analysis.
• To provide actionable insights for mitigating bias by identifying imbalances in object representation, human portrayal, and geographic coverage.
• To support data practitioners in making informed decisions by revealing interrelated biases across multiple dimensions.
• To reduce the risk of deploying biased models by surfacing cultural and historical context-aware bias patterns.

Proposed method

• The tool performs object-based analysis by evaluating the size, context, and diversity of objects in images.
• It conducts person-based analysis to assess representation, posture, and demographic portrayal of individuals.
• Geography-based analysis evaluates the spatial and regional distribution of locations depicted in images.
• REVISE integrates these three dimensions to reveal interdependent biases that may not be apparent in isolation.
• The tool generates bias reports highlighting problematic patterns and suggests mitigation strategies based on dataset characteristics.
• Users are guided to interpret findings in cultural and historical context, ensuring responsible decision-making.

Experimental results

Research questions

• RQ1 How can biases in image datasets be proactively detected before model deployment?
• RQ2 What are the key dimensions through which bias manifests in visual datasets?
• RQ3 How do object-based, person-based, and geography-based biases interact and compound?
• RQ4 What actionable mitigation strategies can be suggested based on detected bias patterns?
• RQ5 How can users interpret and respond to bias findings in contextually responsible ways?

Key findings

• REVISE successfully identifies hidden biases across object, person, and geographic dimensions in image datasets.
• The tool reveals that biases are often interdependent, with representation patterns in one dimension influencing others.
• It provides actionable recommendations for mitigating bias, such as data augmentation or filtering strategies.
• The framework enables early detection of bias, reducing the risk of deploying unfair models.
• Users are empowered to make context-aware decisions by understanding the cultural and historical significance of bias patterns.
• The tool supports transparency and accountability in dataset curation by surfacing previously undetected imbalances.