07 · Cross-cultural IDI study
YouTube
Responsible AI image generation study
Cross-cultural depth research on inclusive AI image generation principles
- Status
- Shipped.
- Service
- Behavioral research, responsible AI
- Scope
- Principles for inclusive AI image generation
- Markets
- USA, Brazil, Japan, Nigeria, Indonesia, India
- Studio
- THEFT Studio
YouTube needed first-principles guidance for how inclusive AI image generation should behave by default, not as a post-hoc filter on model output.
The studio ran a cross-cultural depth-interview study: six ninety-minute IDIs anchored by cultural immersions in Lagos, Jakarta, and Mumbai, with coverage across six markets, the USA, Brazil, Japan, Nigeria, Indonesia, and India.
The work produced a five-force framework for the evolving representation landscape and principle-level guidance across four representation dimensions, with homogeneous defaults rejected in every market studied.
Method · effort by phase
01 · Framing
Scoped the six markets, recruited participants with representative backgrounds, and defined a discussion guide grounded in representation rather than raw accuracy.
02 · Depth interviews
Six ninety-minute IDIs explored how participants evaluate AI-generated imagery for representation across race and ethnicity, gender, attire, and other characteristics.
03 · Cultural immersions
In-country fieldwork in Lagos, Jakarta, and Mumbai grounded the IDIs in local visual culture, attire norms, and daily life.
04 · Synthesis
Translated findings into a five-force framework and a set of high-level principles across four representation dimensions.
What the work surfaced
Homogeneous defaults are rejected across every market studied
Across all six markets, participants rejected AI imagery that defaulted to a single majority look, and the reaction held across race, gender, and body dimensions.
Balanced local-population representation is the baseline
Participants expected AI imagery to reflect balanced local-population representation rather than over-indexing on a global majority: locality-aware, not universally uniform.
Attire, age, and body diversity matter as much as race
Race and ethnicity are the most visible axis, but attire, age, body, and ability carried nearly equal weight in whether an image read as genuinely representative.
Representation is an evolving landscape, not a fixed target
The five-force framework describes representation as an active field rather than a static specification.
AI inherits the training set; design choices inherit the AI
Participants expected product teams to treat generation defaults as a design decision carrying responsibility downstream, not as a technical output.
Delivered
- Cross-cultural depth-interview readout
- Five-force framework · evolving landscape of representation
- High-level principles across race / ethnicity, gender, attire, and other characteristics
- Cultural-immersion field notes from Lagos, Jakarta, and Mumbai
- Implications deck for responsible AI product teams
Outcome
A cross-cultural framework for inclusive AI image generation that holds across six markets.
The work gives responsible AI teams a research-backed framework to reason about representation defaults. The five-force landscape and the four-dimension principle set reframe inclusive generation as an active design responsibility rather than a post-hoc filter on model output.
- Framework
- Five forces · four dimensions
- Coverage
- 6 countries · 3 immersions
- Reframe
- Output filter to design default
- Informs
- Responsible AI image generation