Shift the AI development process by starting with workshops for the people who will live with the system, not just those who pay for it. The primary goal is to translate their stories and needs into tangible checks for fairness and feedback before focusing on technical metrics like accuracy and speed.
When designing AI, manage conflicting human values by structuring them as a ladder. Safety laws sit at the top, followed by regional rules, platform policies, and finally individual preferences. When values clash, the higher rung on the ladder wins, creating a clear and debatable decision-making process for ethical alignment.
To balance AI capability with safety, implement "power caps" that prevent a system from operating beyond its core defined function. This approach intentionally limits performance to mitigate risks, prioritizing predictability and user comfort over achieving the absolute highest capability, which may have unintended consequences.
As AI models become more powerful, they pose a dual challenge for human-centered design. On one hand, bigger models can cause bigger, more complex problems. On the other, their improved ability to understand natural language makes them easier and faster to steer. The key is to develop guardrails at the same pace as the model's power.
In a medical AI project, researchers deliberately rolled back a model's accuracy from 94% to 91% after a fairness audit revealed the final performance gains relied on sensitive user data like income. Doctors preferred the slightly less accurate but fairer model, demonstrating that trust and ethical alignment can be more valuable than marginal performance gains.
A comprehensive approach to mitigating AI bias requires addressing three separate components. First, de-bias the training data before it's ingested. Second, audit and correct biases inherent in pre-trained models. Third, implement human-centered feedback loops during deployment to allow the system to self-correct based on real-world usage and outcomes.