Societal biases in vision and language applications
Project/Area Number |
22K12091
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 61010:Perceptual information processing-related
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Research Institution | Osaka University |
Principal Investigator |
GARCIA・DOCAMPO NOA 大阪大学, データビリティフロンティア機構, 特任助教(常勤) (80870005)
|
Project Period (FY) |
2022-04-01 – 2026-03-31
|
Project Status |
Granted (Fiscal Year 2022)
|
Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2025: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2024: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2022: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
|
Keywords | bias in computer vision / computer vision / image captioning / vision and language / ethical ai / bias in machine learning / fairness |
Outline of Research at the Start |
Artificial intelligence models are being used in the decision-making process of many daily-life applications, having a direct impact on people’s lives. Generally, it is assumed that AI-based decisions are fairer than human-based decisions, however, recent studies have shown the contrary: AI applications not only reproduce the inequalities of society but amplifies them. This project aims to analyze and find solutions to address bias in visual-linguistic models, with the aim of contributing towards making AI fairer.
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Outline of Annual Research Achievements |
In this project, we have investigated the problem of societal bias in image captioning and multimodal vision and language models. As an outcome of the first year of research: * We have shown that captioning models encode gender and racial bias. * We have proposed a new metric to measure societal bias. * We have annotated a dataset to study and mitigate societal bias. * We have designed a bias mitigation method for image captioning.
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Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
In the first year, we have achieved more milestones than originally planned, and have published our work on top conferences in the field.
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Strategy for Future Research Activity |
Next, we will explore societal bias in text-to-image generation models.
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Report
(1 results)
Research Products
(5 results)