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Responsible AI at Google Research: Perception Fairness

🤖 Intelligence Artificielle

✍️ Auteur(s)

Susanna Ricco and Utsav Prabhu

📅 Publication

2023-08-25T10:38:00.003-07:00

📖 Longueur

800 mots

Source: https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjvTD0aWY1G5...

📋 Extrait de l'article

Posted by Susanna Ricco and Utsav Prabhu, co-leads, Perception Fairness Team, Google Research Google’s Responsible AI research is built on a foundation of collaboration — between teams with diverse backgrounds and expertise, between researchers and product developers, and ultimately with the community at large. The Perception Fairness team drives progress by combining deep subject-matter expertise in both computer vision and machine learning (ML) fairness with direct connections to the researchers building the perception systems that power products across Google and beyond. Together, we are working to intentionally design our systems to be inclusive from the ground up, guided by Google’s AI Principles . Perception Fairness research spans the design, development, and deployment of advanced multimodal models including the latest foundation and generative models powering Google's products. Our team's mission is to advance the frontiers of fairness and inclusion in multimodal ML systems, especially related to foundation models and generative AI . This encompasses core technology components including classification, localization, captioning, retrieval, visual question answering, text-to-image or text-to-video generation, and generative image and video editing. We believe that fairness and inclusion can and should be top-line performance goals for these applications. Our research is focused on unlocking novel analyses and mitigations that enable us to proactively design for these objectives throughout the development cycle. We answer core questions, such as: How can we use ML to responsibly and faithfully model human perception of demographic, cultural, and social identities in order to promote fairness and inclusion? What kinds of system biases (e.g., underperforming on images of people with certain skin tones) can we measure and how can we use these metrics to design better algorithms? How can we build more inclusive algorithms and systems and react quickly when failures occur? Measuring representation of people in media ML systems that can edit, curate or create images or videos can affect anyone exposed to their outputs, shaping or reinforcing the beliefs of viewers around the world. Research to reduce representational harms, such as reinforcing stereotypes or denigrating or erasing groups of people, requires a deep understanding of both the content and the societal context . It hinges on how different observers perceive themselves, their communities, or how others are represented. There's considerable debate in the field regarding which social categories should be studied with computational tools and how to do so responsibly. Our research focuses on working toward scalable solutions that are informed by sociology and social psychology, are aligned with human perception, embrace the subjective nature of the problem, and enable nuanced measurement and mitigation. One example is our research on differences in human perception and annotation of skin tone in images using the Monk Skin Tone scale . Our tools are also used to study representation in large-scale content collections. Through our Media Understanding for Social Exploration (MUSE) project, we've partnered with academic researchers, nonprofit organizations, and major consumer brands to understand patterns in mainstream media and advertising content. We first published this work in 2017, with a co-authored study analyzing gender equity in Hollywood movies . Since then, we've increased the scale and depth of our analyses. In 2019, we released findings based on over 2.7 million YouTube advertisements . In the latest study , we examine representation across intersections of perceived gender presentation, perceived age, and skin tone in over twelve years of popular U.S. television shows. These studies provide insights for content creators and advertisers and further inform our own research. An illustration (not actual data) of computational signals that can be analyzed at scale to reveal representational patterns in media collections. [Video Collection / Getty Images] Moving forward, we're expanding the ML fairness concepts on which we focus and the domains in which they are responsibly applied. Looking beyond photorealistic images of people, we are working to develop tools that model the representation of communities and cultures in illustrations, abstract depictions of humanoid characters, and even images with no people in them at all. Finally, we need to reason about not just who is depicted, but how they are portrayed — what narrative is communicated through the surrounding image content, the accompanying text, and the broader cultural context. Analyzing bias properties of perceptual systems Building advanced ML systems is complex, with multiple stakeholders informing various criteria that decide product behavior. Overall quality has historically been defined and measured using summary statistics (like overall accuracy) over a test dataset as a proxy for user experience. But not all users experience products in the same way. Perception Fairness enables practical measurement of nuanced system behavior beyond summary statistics, and makes these metrics core to the system quality that directly informs product behaviors and launch decisions. This is often much harder than it seems. Distilling complex bias issues (e.g., disparities in performance across intersectional subgroups or instances of stereotype reinforcement) to...

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