Short answer

Proactively integrate diverse user perspectives and employ intersectional analysis throughout the design process to identify and mitigate biases in AI-driven technologies.

Field: User-Centred Design
Source: Frontiers in Sociology (2025)
Method: Participatory Action Research
Evidence: Strong effect

Generative AI systems often embed societal biases, leading to exclusionary outcomes for users with intersecting marginalized identities, such as migrants with disabilities. This user-centred design research insight is drawn from a 2025 study published in Frontiers in Sociology. Using Participatory action research, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Proactively integrate diverse user perspectives and employ intersectional analysis throughout the design process to identify and mitigate biases in AI-driven technologies.

Study

User-Centred DesignNew This WeekStrong effect

Generative AI perpetuates bias, demanding inclusive design for marginalized users.

Generative AI systems often embed societal biases, leading to exclusionary outcomes for users with intersecting marginalized identities, such as migrants with disabilities.

Frontiers in Sociology · 2025

Key Findings

01Participants faced significant challenges in navigating digital accessibility and advocating for their needs.
02Generative AI tools demonstrated biases and misrepresentations concerning intersecting identities.
03Participants actively utilized everyday technologies to enhance agency, learning, caregiving, and cultural connections.

Application

Design takeaway

Proactively integrate diverse user perspectives and employ intersectional analysis throughout the design process to identify and mitigate biases in AI-driven technologies.

How to apply

When designing AI-powered features, conduct user research with individuals from diverse backgrounds, paying close attention to how intersecting identities might influence their experience and potential for exclusion.

Project actions

01Consider the intersectionality of user identities when defining your target audience.
02Explore participatory design methods to involve users in the research and development process.
03Critically evaluate potential biases in AI tools or datasets you might use in your project.

Method & Evidence

AimHow can participatory and intersectionality-informed methods be employed to understand and address the exclusionary impacts of everyday consumer technologies, particularly generative AI, on migrants with disabilities?

MethodParticipatory Action Research

ProcedureThe research involved individual interviews, creative workshops, guided discussions, post-workshop reflections, and co-creation of AI-generated e-books with participants from migrant backgrounds with disabilities.

ContextEveryday consumer technologies, digital inclusion, disability justice, migrant justice, generative AI

Variables

IVParticipatory and intersectionality-informed methods, generative AI features

DVUser experience, digital accessibility, representation, agency

CVParticipant demographics (disability, migrant status, cultural background)

Strengths & Limitations

Strengths

+Employs participatory action research, centering user voices.
+Addresses the critical intersection of disability and migrant identities in technology use.

Limitations

It can be challenging to find and recruit participants with specific intersecting identities for user research.

Reliability & validity

The study's validity is strengthened by its use of multiple data collection methods (interviews, workshops, co-creation) and its focus on real-world technology use. Reliability could be enhanced by larger sample sizes and triangulation across more diverse participant groups.

Think critically

How can designers proactively identify and mitigate biases in AI systems before they are deployed, rather than reacting to exclusionary outcomes?

Design Principles

"Design for inclusion by actively seeking out and centering the experiences of marginalized users, especially at the intersection of multiple identities."

As AI becomes more integrated into everyday technologies, designers must proactively address and mitigate inherent biases. Failing to do so risks creating systems that further marginalize already vulnerable populations, hindering their autonomy and participation.

What This Means for Your Design

AI can be biased because the data it learns from has biases. This can make it hard for people with disabilities who are also from minority groups to use technology, as it might not understand or represent them properly. Designers need to work with these users to fix this.

How to use in your project

1.Reference this study when discussing the importance of inclusive design and the potential for AI bias in your design project's research or evaluation sections.

Add to My Project

Quick Cite

Paragraph starter

Research indicates that generative AI systems can perpetuate societal biases, leading to exclusionary outcomes for users with intersecting marginalized identities, such as migrants with disabilities. This necessitates a move towards participatory and intersectionality-informed design methods to ensure technologies are truly inclusive and equitable.

Source

Frontiers in Sociology

Rethinking digital and AI inclusion: participatory and intersectionality-informed methods for disability and migrant justice

journal · 2025

View source

Questions About This Research

What does the research say about generative ai perpetuates bias, demanding inclusive design for marginalized users?: Proactively integrate diverse user perspectives and employ intersectional analysis throughout the design process to identify and mitigate biases in AI-driven technologies. Evidence: Frontiers in Sociology (2025).
Why does "Generative AI perpetuates bias, demanding inclusive design for marginalized users." matter for design?: As AI becomes more integrated into everyday technologies, designers must proactively address and mitigate inherent biases. Failing to do so risks creating systems that further marginalize already vulnerable populations, hindering their autonomy and participation.
How can designers apply this research?: Proactively integrate diverse user perspectives and employ intersectional analysis throughout the design process to identify and mitigate biases in AI-driven technologies.
What were the main findings?: Participants faced significant challenges in navigating digital accessibility and advocating for their needs.. Generative AI tools demonstrated biases and misrepresentations concerning intersecting identities.. Participants actively utilized everyday technologies to enhance agency, learning, caregiving, and cultural connections.
What research method was used?: Participatory Action Research.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2025 journal from Frontiers in Sociology.
What should I do differently in my next project?: When designing AI-powered features, conduct user research with individuals from diverse backgrounds, paying close attention to how intersecting identities might influence their experience and potential for exclusion.
What are the limitations?: The findings are based on specific case studies and may not be universally generalizable without further research across different cultural and technological contexts.