Innovation Management Module 7 Trust in AI

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Arms race in AI

Ai security vs skill of adversarial attack

Main Limitations of AI

Bias Adversarial attack

Why does AI bias matter

‒ Hiring tool that discriminated against women ‒ Facial recognition working better for light-skinned than dark-skinned individuals ‒ Bank loan approvals ‒ Dangerous effect of reinforcing unhealthy stereotype

How to generate trust in AI

‒ Importance of data ‒ Correct selection ‒ High quality data ‒ Maintenance to counter model drift ‒ Building the right system ‒ Trade-off between simple systems and complex deep learning-based systems (?) ‒ Black boxes ‒ System design choices ‒ Cognitive and emotional trust ‒ Technical capabilities and form of AI representation

How to combat biases

‒ Technical solutions ‒ E.g., "zero out" the bias in words ‒ Use less biased and / or more inclusive data ‒ Transparency and / or auditing processes ‒ Diverse workforce ‒ Creates less biased applications

Goldilocks Rule

‒ Too optimistic: Sentient / super-intelligent AI Killer robots coming soon ‒ Too pessimistic: AI cannot do everything Another AI winter is coming ‒ Just right: AI can't do everything But it will transform industries


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