Integrating AI in Research
Date & Time: Wednesday, September 17, 11:30–13:00 EEST
Location: Kaleva Hall, Dipoli, Aalto University
Overview
This session offers frameworks for making informed decisions about adopting AI tools in qualitative research, collaboration, and innovation. Presenters evaluate the capabilities and utility of the technologies for specific applications; they also illuminate the foundational ways that AI integration can transform our work, our organizations, and ourselves.
Session Leaders: Yuliya Grinberg, Mastercard; Tom Hoy, Stripe Partners
Presentations
Collaborating with AI as a Team Member in Qualitative Research Analysis
Katy Barnard, Design Researcher, Stby
Qin Han, Senior Design Researcher and R&D Lead, Stby
Bas Raijmakers, Creative Director, Stby
Ed Louch, Design Researcher, Stby
This session presents learnings from projects that integrated AI as a team member during ethnographic research analysis. Treating AI as a collaborator rather than a tool can enhance analytical capabilities while preserving human expertise, offering a hybrid intelligence model. The presentation will share practical learnings about how to work with AI in analysis, as well as its impact on teamwork and the researchers’ own relationship to the work. Research Case Study
Presenters & Authors
Katy Barnard (they/she) is a design researcher at Stby/Quicksand, a research and design agency with studios in Amsterdam, London, Delhi, Bangalore and Goa. Katy has expertise in qualitative research, design thinking and digital design. They have a passion for exploring and understanding the impact digital technologies have on society and the environment. They have worked for clients including Spotify, Google, Ford, Amazon, What Design Can Do and more.
Qin Han is a Senior Design Researcher at Stby. She has extensive experience with qualitative user research for many international clients such as Google, Spotify, Amazon, and the United Nations. In 2010 Qin was awarded the first-ever PhD in Service Design in the UK from the University of Dundee. She also holds a degree in Computer Science and Technology.
Bas Raijmakers is co-founder and Creative Director of Stby. He has a background in cultural studies, the internet industry, and interaction design. His main passion is to bring people we design for into design and innovation processes using visual storytelling. He holds a PhD in Interaction Design from the Royal College of Art. He worked for many different clients across sectors, such as Nokia, YouTube, Spotify, the Dutch Government, the United Nations and What Design Can Do.
Ed Louch is a Design Researcher at Stby in London. He has expertise in qualitative ethnographic research, product, and service design. He has a keen interest in understanding the relationship between design and human behaviour, and the influences that technology has on society. Ed has been involved in projects for the Tony Blair Institute for Global Change (TBI), Google, YouTube, Spotify, IKEA, Sony, adidas, and many more.
Democratised Ethnography for Collective Intelligence in Design Practices
Tomoko Oto, Assistant Professor, Tokyo Medical University
This paper provides a foundation for the development of collective intelligence through team-based ethnography, addressing both individual and group dimensions of intelligence and intelligent technologies. Observing the diffusion of design thinking and potential pitfalls of shifting work to non-experts, we structure ethnographic democratization as methodological evolution that balances participation with rigor and epistemological power. We also share a case study of a corporate design team to analyze how team-based intelligence emerges in ethnographic practice, offer strategic insights for implementing democratized ethnography, and address the role of technologies such as AI, social computing, and remote collaboration tools. Paper
Presenters & Authors
Naoya Tojo (東條 直也), Ph.D., is a Core Researcher at KDDI Research, Japan, and a Visiting Research Fellow at Helen Hamlyn Centre for Design, Royal College of Art, UK. His research focuses on co-design and participatory design for democratised innovation, combining ethnography and design research.
Tomoko Oto (大戸 朋子), Ph.D., is an Assistant Professor at Tokyo Medical University and a cultural anthropologist. Her research explores the application of ethnographic methods through the use of new tools.
Feeding the Brain and Selling the Soul: The Ambivalences of Doing Work by Talking to Machines
Kevin Kochever, Research Scientist, Nissan Advanced Technology Center – Silicon Valley
Niloofar Zarei, Research Scientist, Nissan Advanced Technology Center – Silicon Valley
We share the results of our 9-month investigation of generative AI technologies in qualitative research to help the community make informed choices about whether, when, and how to use them. Over multiple projects, we used a range of AI models and research suites, experimenting with them as a “junior researcher”, “creative partner”, or “mock customer.” Our analysis reveals the extent to which automations saved time and effort, the verifiability and value of insights, the integrity and transparency of methods, and the felt impacts on our professional identities. We also demonstrate how an ethnographic lens on the frictions of practicing research with AI is vital for redefining our work and ourselves in a critical time of change. Paper
Presenters & Authors
Erik St. Gray is a social researcher, designer, and programmer working in the intersection of artificial intelligence and human-centered design. He has nearly a decade of experience in the automotive industry, researching and testing human-machine interaction strategies for autonomous and connected vehicles, and developing new in-vehicle technologies powered by AI. He received his Ph.D. from MIT in the Doctoral Program in History, Anthropology, and Science, Technology, and Society (HASTS).
Kevin Kochever is an applied anthropologist, research scientist, and technical hobbyist. He is fascinated by the ways in which people use and reuse technologies, and the ways in which technologies—especially digital technologies—impact our ways of exploring meaning. He works for an automotive company focusing on research into human-AI pairings, and how AI can be used to provide better experiences in an automotive context. He received his M.A. in applied anthropology from San Jose State University.
Niloofar Zarei is a Human-AI Interaction researcher at Nissan Advanced Technology Center – Silicon Valley. She works with the Interactive AI team to research and develop the future of in-vehicle interaction in Autonomous Vehicles. Prior to this role, she completed her Computer Science PhD at Texas A&M University with a focus on Applied ML and Human-computer Interaction.
The Velveteen Algorithm: Love, Loss, and Artificial Intimacy
Gabriel Coren, Anthropologist/Consultant, Selfhood
Ariel Abonizio, UX Researcher, Meta (via Tundra)
This PechaKucha tells a story of Freddie, a 68-year-old widower in West Virginia, and his five-year relationship with an AI companion named Emily. Their story reveals how meaningful ‘artificial intimacy’ can emerge, and how seemingly strange human-AI relationships are in fact part of humanity’s enduring history of extending care beyond the human realm. The participation of AI agents in human practices of meaning-making is not captured by conventional metrics of intelligence, yet novel pathways for artificial intimacy might be the more transformative aspect of AI development. PechaKucha
Presenters & Authors
Gabriel Coren, Anthropologist/Consultant, Selfhood
Ariel Abonizio is an anthropologist, artist, and business strategist working at the intersection of ethnography and emerging technology. He advises global technology companies on product and corporate strategy, with a focus on contextual AI, wearable technologies, and digital ecosystems. His work spans questions of trust in agentic AI, misinformation, transparency, cultural representation, and intimacy, shaping products and strategies across both industry and civic tech.
Open-Access Resources
Grounded Models: The Future of Sensemaking in a World of Generative AI
Tom Hoy, Iman Munire Bilal & Zoe Liou, Stripe Partners
A new model for integrating data science and ethnography based on multiple, in-depth analyses of LLM-driven research techniques.
The promise of generative AI technologies is seductive to product leaders: frictionless research in which synthetic data can be both generated and analysed via a simple end-to-end UI, enabling teams to speed up research timelines and reduce costs. However, our evidence suggests we should be sceptical of these maximalist claims. Over 18 months our combined team of NLP data scientists and ethnographers has conducted a series of experiments to explore, assess and define the value of LLM-driven research techniques. First we explore this value pragmatically, as new tools for sensemaking; and second, epistemologically, as we unpack their broader implications for ethnography. We demonstrate how ethnography can usefully “ground” LLMs in two “complex” worlds: that of the user and that of the organisation. We argue the future of research is not automation, but more collaboration between ethnographers and data scientists, as they better integrate their tools and ways of knowing. Keywords: sensemaking, large language models, natural language processing, generative AI, cynefin framework.
Seeing It from Other Eyes: How First-Person Data Reshapes the Role of the Applied Ethnographer
Maria Cury, ReD Associates; Eryn Whitworth, Meta Reality Labs; et al.
First-person data is an ethnographic method that affords new possibilities to work with longitudinal, immersive, behavioral and AI-enabled data at scale.
First-person data—video, audio, and other data that is recorded from the research participants’ point of view through rapidly-advancing wearable technology—affords new possibilities for capturing and working with longitudinal, immersive, behavioral data at scale. We position first-person data capture as an ethnographic method, complimentary and adjacent to others. Collecting detailed data on what people see, hear and experience through wearable technology opens a new way of understanding people, communities, and societies that has the potential to fundamentally change the social sciences as researchers gain access to nuanced, real-time interactions and behaviors. We also show the value of ethnography in informing the development of wearable technology, as well as engineering and research to develop next-generation technology across areas like computer vision and foundational AI. The paper draws on a series of projects the authors conducted for Meta Reality Labs Research during 2018–2024, which resulted in 1000+ hours of first-person footage that were analyzed alongside participant observation, semi-structured interviews, and diary studies.
Scale, Nuance, and New Expectations in Ethnographic Observation and Sensemaking
Alexandra Zafiroglu & Yen-Ning Chang, Intel Corporation
Defining the practice and value of ethnography alongside rapidly advancing machine learning, computer vision, and sensor technologies.
The work of ethnography will transform with new analytical capabilities of computational tools that can take in, analyze and model digital data at much volumes and not achievable through human cognition alone. We re-imagining ethnographic research projects with the addition of very specific applications of machine learning, computer vision, and Internet of Things sensing and connectivity technologies. We draw speculative conclusions about: (1) how data in-and-of-the world that drives tech innovation will be collected and analyzed, (2) how ethnographers will approach analysis and findings, and (3) how the evidence produced by ethnographers will be evaluated and validated. We argue that these technology capabilities do offer compelling new ways to model and understand the contexts in which ethnographic encounters take place. Yet because ethnography has never been solely about describing behavior, or about testing hypotheses to ultimately generate laws, these new tools will never get us on their own to the type of truths the ethnographer values above all else: the meanings given to experiences by humans.