Personal Anecdotes of AI Bias and where we go from here

Artificial intelligence systems increasingly shape our daily experiences, from voice assistants to image generation tools, yet these technologies often fail to recognize or fairly represent diverse users. This talk combines personal narratives with empirical research to examine how AI bias manifests in real-world interactions and its psychological impact on marginalized communities. Drawing from studies on accent bias in voice cloning services and representation gaps in image search results, we explore how current AI systems perpetuate exclusion through both technical limitations and training data skewed toward Western, predominantly white perspectives. The presentation reveals the human cost of algorithmic bias—from feelings of erasure to impacts on self-esteem—while also highlighting emerging efforts to create more inclusive AI systems. Through case studies ranging from voice recognition failures to beauty standard reinforcement, we demonstrate how personal experiences of bias reflect broader systemic issues in AI development and deployment.

About our speaker
Dr. Avijit Ghosh is an Applied Policy Researcher at Hugging Face. He works at the intersection of machine learning, ethics, and policy, aiming to implement fair ML algorithms into the real world. He has published and peer-reviewed several research papers in top ML and AI Ethics venues, and has organized academic workshops as a member of QueerInAI. His work has been covered in the press, including articles in The New York Times, Forbes, The Guardian, Propublica, Wired, and the MIT Tech Review. Dr. Ghosh has been an invited speaker as a Responsible AI expert, at various high impact events such as SXSW, MIT Sloan AI Conference and the Summit on State AI Legislation. He has also engaged with policymakers at various levels in the United States, United Kingdom, and Singapore. His research and outreach have led to real-world impact, such as helping shape regulation in New York City and causing Facebook to remove their biased ad targeting algorithm.

Cultivating Ecosystems of Intelligence for Community-Driven Creativity

Humanity’s symbiotic relationship with technology has provided unprecedented access to collective wisdom. Through our devices, the Internet, and now AI, humans have never been more capable. However, smart individuals alone are not enough to solve the wicked problems of the world; we need collective creativity and decision-making. This talk explores new opportunities for AI systems to support the social dynamics of problem-solving — to cultivate ecosystems of intelligence. I will share our work from the Protolab research group on how human-AI interactions can be designed to make individuals, teams, and communities more adaptive, empowered, and wiser.

About our speaker
Steven Dow is a Professor in the Cognitive Science Department and the Design Lab at UC San Diego, Director of the ProtoLab research group, and Co-Founder of the Design for San Diego initiative. His research on human-computer interaction, creativity, social computing and collective intelligence seeks to engage diverse teams and communities to co-create better, more inclusive, and more sustainable outcomes. Prof. Dow received the NSF CAREER Award for research on “advancing collective innovation.” His research has been funded by multiple National Science Foundation grants, a Google Faculty Grant, a Yankelovich Center for Social Science Research Award, Stanford’s Postdoctoral Research Award, and a Hasso Plattner Design Thinking Research Grant. He holds an MS-HCI and PhD in Human-Centered Computing from the Georgia Institute of Technology and a BS in Industrial Engineering from the University of Iowa.

Overview
The widespread adoption of platformized gig work has produced major disruptions to labor patterns and mobility. Underpinning transformative technology are workers, who face challenges to well-being in their labor, including a lack of data transparency, social isolation, and inadequate protections. In this talk, I present Gig2Gether, a prototype data-sharing system evaluated within the context of workers’ everyday labor to explore the possibilities of fostering solidarity, financial reflection, and policy influence across participants of different platforms. I discuss the results of a 7-day field study with 16 active workers spanning three work domains, which reveal existing and envisioned affordances of cross-platform data-sharing for collectivism and policy impact. Workers leveraged shared data to provide mutual support, improve financial planning, and envision new forms of collectivism and policy advocacy around issues like safety and pay. These findings highlight both the potential and the challenges of data-sharing for the gig work ecosystem. We discuss how data-sharing tools can complement existing structures to enhance worker empowerment and drive meaningful policy change.

About the speaker

Jane is a final year software engineering PhD at Carnegie Mellon. Her research seeks to empower and collectivize platform-based laborers by bridging information asymmetries and gaps between worker communities. Specifically, she works with large-scale data and multiple stakeholder groups to (1) draw insights and design interventions to improve the gig work condition and (2) subsequently leverage such design guidelines to build systems that digitally support platform-based laborers in their work. She also engages with regulators and policy experts to inform relevant policymaking.