Ethical AI Development: Leveraging Human Centered Design for Inclusive and Trustworthy Solutions
summary
A four-part, human-centered framework claims to cut common failure modes—unclear objectives, poor process integration, bad data—by starting with pain discovery, then building an experience/journey map, doing AI solution mapping, and early ethical evaluation. Methods include user interviews, observation, UX mapping (Mural templates), and inventorying systems like ServiceNow, Jira, Teams and Power Platform. Cites a startling 70–85% fail rate, reports two projects advancing to pilot after applying the workshop and finding where AI is — and isn’t — fit.
AI-written summary · about
chapters (8)
- 0:00Introduction and background
- 1:07AI project failure causes
- 6:34Human-centered design workshop overview
- 10:27Interactive pain point discovery
- 23:52Prioritizing task management pain points
- 27:09AI solution mapping exercise
- 38:47Ethical considerations discussion
- 50:25Key takeaways and conclusion
related talks
25mHumanizing Technology: Ethics and Design in the Age of AI
16mTrust by Design: Building Reliable AI for Sensitive Environments
05mToward Ethical AI
32mSo you want to develop ethical AI. What does that mean in practice?
24mA Call for Human-Centered AI
37mFinding the Problem to Solve: Human-centered AI
31mA more human-centered approach to AI
39mBuilding a Responsible AI Governance Program