10 Ways to avoid “Computer says no"

Techniques to help make AI More Human-Centric

1. Start with Human Needs, Not Data

Anchor every project in real user problems, not just available datasets. Ask: “Whose problem am I solving, and how does AI make it easier?”

2. Design for Understanding, Not Obedience

Build AI that explains itself. Users should understand recommendations, not just follow them. → Include plain-language rationales or “why” features.

3. Include Diverse Perspectives

Involve people from varied backgrounds and disciplines in design and testing. → Diversity reduces blind spots and surfaces hidden biases.

4. Keep Humans in Control

Ensure users can override, question, or pause AI decisions. → Make “human-in-the-loop” a design principle, not an afterthought.

5. Prioritize Transparency

Be honest about what the system can and cannot do — and how it makes decisions. → Explain data sources, limitations, and intended use clearly.

6. Audit for Fairness and Bias

Regularly test outputs for bias and unequal performance across groups. → Use frameworks like model cards, bias audits, or your “Bias Buster” cards.

7. Design for Emotional Intelligence

Consider tone, empathy, and trust in interactions. → Avoid overly robotic or manipulative communication styles.

8. Protect Privacy and Dignity

Minimize data collection and use only what’s essential. → Treat user data as borrowed, not owned.

9. Reflect Human Values

Align AI behavior with social, ethical, and cultural values relevant to context. → Embed ethics reviews early, not post-launch.

10. Continuously Learn from Users

Use feedback loops to refine models and interfaces. → Human-centric AI is a moving target — keep adapting as people and contexts change.

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