AI should treat people equitably and avoid bias or discrimination.

    Example: Making sure a hiring algorithm doesn’t favor one gender or race over another.

    People should be able to understand how and why an AI system makes decisions.

    Especially important in areas like finance, healthcare, or criminal justice.

    AI must respect user privacy and use data responsibly, complying with laws like GDPR.

    Data should be collected and used with consent.

    Clear responsibility must be assigned for AI decisions and actions.

    There should be ways to audit and fix issues if something goes wrong.

    AI systems should be robust, reliable, and protected from misuse or attacks.

    This includes preventing harmful outcomes and ensuring system integrity.

    AI should be designed and used in ways that are accessible and beneficial to all people.

    IBM Watson was used to help doctors diagnose and recommend treatments. Over time, concerns about accuracy and bias led to increased efforts to audit decision-making and ensure that medical professionals remained the final authority, not the AI.

    JPMorgan uses AI for fraud detection and customer service. They have internal policies to ensure fairness in credit scoring models and avoid discrimination, particularly around race or zip codes—areas where traditional data could introduce bias.

    Amazon built an AI to help with hiring but discovered it was penalizing resumes that included the word “women” (e.g., “women’s chess club”). The project was scrapped, and it became a key example in AI ethics discussions. It led to broader adoption of bias mitigation and diverse training data in HR tech.

    Microsoft has a dedicated Office of Responsible AI that enforces standards across its products (like Azure AI, Copilot, etc.). They implement fairness testing, human-in-the-loop designs, and transparency tools for clients using AI in healthcare, government, and education.

    Waymo integrates extensive testing and simulation to ensure their autonomous vehicles make safe, ethical decisions in real-world environments. They publicly release safety reports to maintain transparency and public trust.