Breaking the Wall of Algorithmic Injustice

Breaking the Wall of Algorithmic Injustice

Global Call 2026 Winner Interview: Women's Impact Award

Amelia Fiske is a Senior Researcher at the Institute for the History and Ethics of Medicine at the Technical University of Munich, Germany. She is trained as a cultural anthropologist and has been working in interdisciplinary bioethics settings since 2017. Her work is situated at the intersection of cultural anthropology, science and technology studies, graphic art, and bioethics. 

Which wall does your research or project break?  

Artificial Intelligence (AI) comes with great promises, but more critically, it comes with demonstrated harms, from racial and gender bias, wrongful arrests, defamation, surveillance, or the extractive labor and practices required for its creation. This project aims to break the wall of algorithmic injustice by centering the experiences of marginalized communities and develops interventions into AI concerns that are designed by and for those communities. 

To do so, this project develops the novel approach of participatory algorithmic justice. Participatory algorithmic justice defines a concept and standards of practice for collaborative research to better understand who and what AI harms, and how these harms should be redressed. The project investigates how economic, cultural, and political harms from AI are experienced by structurally marginalized groups through multi-sited, intersectional ethnographic fieldwork on substantially different domains of AI around the world: environmental sustainability in Germany and the UK, global health in Zimbabwe, judicial systems in Colombia, and digital labor in India. This fieldwork informs participatory design workshops to develop specific interventions into problems identified by research collaborators. 

Participatory algorithmic justice brings the voices, priorities, and concerns of those affected by AI to the forefront of debates over what kinds of AI people want to live with. Tackling a critical problem of global technology justice, this project is a crucial intervention for communities, researchers, and developers to redress AI harms.

What is the main goal of your research or project?  

The main goal of this project is to transform debates on algorithmic justice by advancing the novel method of participatory algorithmic justice to investigate AI in context. Participatory algorithmic justice defines a concept and methods for collaborative research to better understand who and what AI harms, but also how these harms should be redressed. Specifically, this means partnering with specific communities that have been marginalized by AI, or are at risk of being marginalized by AI, and working with them to build community-led strategies to support their priorities and needs in a world with AI. 

To give one specific example, this project integrates an intersectional gender-equity approach by examining how AI structurally marginalizes women and non-binary individuals, and explictly seeks out their experiences to co-create “counter AI narratives” as part of a strategy to enact algorithmic justice in local contexts. Counter AI narratives are designed to give voice to the perspectives of those who have been marginalized, creating a space to resist forms of domination and power. For example, one narrative comes from a woman who has led the work of producing algorithmic accountability standards in Latin America. Her work has led her to challenge the hype of AI in the area of public services. For example, she found that despite public promises that an AI program would bring services to marginalized women in a more efficient and cost-effective way, the initiative squandered public money and failed a highly vulnerable group. Together with a graphic artist, we are rendering this woman’s insight as a co-authored visual story. Such visual counter narratives are an emerging strategy to democratize the voices engaged in AI debates and decision-making.

What impact does your research or project have on society?

PARTIALJUSTICE explores how economic, cultural, and political harms from AI are experienced by structurally marginalized groups through multi-sited ethnographic fieldwork that examines how AI harms intersect with multiple sources of oppression. By situating critical questions of justice within specific contexts and histories, the multi-sited ethnographic fieldwork informs participatory design workshops. The project is tackling these questions in several specific areas of AI: judicial processes in Colombia, communities experiencing the environmental health effects of AI in Germany and the UK, and the role of community health workers in relation to AI global health projects in Zimbabwe, and digital labor in India.   Through ethnographic fieldwork in each of these cases, we aim to understand a key concern that a community has with AI and build up approaches to justice from that point of entry. Building from that, we will work together on the participatory design workshops to develop specific interventions which shift the representation of a broader range of public interests in AI. This is an approach that has commitments to social justice and change at its core: community partners are co-researchers who are best positioned to identify and enact tools and practices to combat harm from AI on their own terms and within their own contexts. We aim to bring the research to life through graphic storytelling and a public-facing mapping platform that create interactive engagement on algorithmic justice.

What advice would you give to young scientists or students interested in pursuing a career in research, or to your younger self starting in science?

I believe good science is situated: find ways to engage on issues and questions that matter to you, where you can intervene from your specific position in the world. 

What inspired you to be in the profession you are today?  

The fantastic educators and role models I have had from my undergrad to today: Krista Van Vleet, Nancy Riley, Jen Scanlon, and Kristen Ghodsee at Bowdoin College; Margaret Wiener, Peter Redfield, Rudi Colloredo-Mansfeld, and Barry Saunders at UNC Chapel Hill; Alena Buyx and Barbara Prainsack in Kiel and Munich.

What is one surprising fact about your research or project that people might not know?  

Working with arts-based methods isn't just about communicating research in better ways, its about transforming how we do research in the first place.