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Adversarial Attacks: Putting the 'Robustness' Pillar of AI Ethics to the Test

  • 1.  Adversarial Attacks: Putting the 'Robustness' Pillar of AI Ethics to the Test

    Posted yesterday

    Hello everyone,

    Having just completed the AI Ethics module (part of the Artificial Intelligence Fundamentals program), it's clear that ethical AI rests on five key pillars:

    1. Explainability

    2. Fairness

    3. Robustness

    4. Transparency

    5. Privacy

    While all are crucial, I've been particularly reflecting on Robustness. It's not just about performance, but about security and reliability. The module highlights a critical threat: adversarial attacks, where hackers deliberately "poison" training data or create "evasion" inputs to deceive AI models.

    This makes me think: We spend so much time building intelligent models, but are we building them to be resilient? A model that is 99% accurate becomes 100% unreliable if it can be tricked by a subtly altered input.

    My question to the community is: As we integrate AI into critical systems, from healthcare to finance, how can we proactively "stress-test" our models against these adversarial attacks before they are deployed, rather than reacting to a breach afterward?

    AI Ethics
    For a deep dive into these principles, I highly recommend the AI Ethics module.

    #AIEthics #ResponsibleAI #ArtificialIntelligence #IBM #AIEthics #Robustness #AdversarialAI #CyberSecurity #AIFundamentals



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    Eduardo Lunardelli
    Data Scientist
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