Culturally-Inclusive AI: Enhancing User Experience With Global Perspectives
summary
Shows concrete comparisons like Digikala vs Amazon and Netflix Japan vs US to prove UI, color and recommendation differences by region, demos ChatGPT 3.5 and Microsoft Bing answering differently in Farsi vs English, highlights Gemini prompt‑alteration failures and facial‑recognition risks (Coded Bias, DeepFace). Recommends recruiting communities in person, recording user videos for stakeholders, using synthetic data with continuous validation alongside data scientists, and running privacy audits to reduce biased outcomes.
AI-written summary · about
chapters (9)
- 0:00Introduction to culturally-inclusive AI
- 1:12Impact of cultural diversity in design
- 3:40Cultural nuances and user preferences
- 6:48Leveraging AI for cultural inclusivity
- 13:30Addressing biases in AI systems
- 17:08Synthetic data and UX research
- 20:16AI privacy and consent across cultures
- 24:09Future of culturally-inclusive AI
- 26:59Q&A session
related talks
28mUser Research GenAI Better Together
35mDesigning Inclusive and Accessible Experiences in the Era of AI
21mBe more responsible around AI - Less Bias, More Ethics
30mInside AI: What Every Professional Needs to Know to Stay Relevant, Protected, and Ahead
41mGhost in the Machine: The Unexpected Results of Not Understanding Your Users
23mOpening up the box: the complexity of human interaction with AI systems
45mEthical use of AI: seeking, understanding, and countering the risks
43mReducing Bias in AI with Open Source