From the course: Integrating AI into the Product Architecture
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Ethical considerations: Bias, fairness, and responsible use
From the course: Integrating AI into the Product Architecture
Ethical considerations: Bias, fairness, and responsible use
- [Instructor] Picture this, you're building an AI-powered code generation tool, but then you discover that it consistently generates less secure code when it thinks the user's a woman. I'm not making this up. Studies have shown up to 20% bias in security vulnerabilities based on perceived gender. Just like building a house, you shouldn't wait until the product is built to think about safety. Whether you're creating a customer service chatbot or a content recommendation system, you need to think about ethics from day one. LLMs are like sponges, soaking up everything from the internet. They learn from massive datasets, often terabytes in size, scraped from the internet. These datasets often reflect existing societal biases, gender stereotypes, racial prejudices, and cultural misunderstandings. This also means the LLM can unintentionally amplify these biases. But there's hope. We can actively mitigate these biases. Tools like Fairlearn and AIF360 act as quality control checkpoints in…
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