Lesson 28 — AI Bias, Privacy & Security: What You Need to Know

As artificial intelligence becomes deeply integrated into our daily lives and workplaces, it brings incredible advantages. However, it also introduces significant risks. To use AI responsibly, we must understand the core challenges surrounding AI bias, privacy, and security.

1. AI Bias and Fairness

AI models learn from human-generated data, which means they can inherit and even amplify human prejudices. If training datasets lack diversity or reflect historical inequalities, the AI’s outputs can lead to biased or discriminatory outcomes in hiring, lending, healthcare, and law enforcement.

2. Data Privacy Concerns

When users input sensitive personal information, proprietary code, or confidential corporate data into public AI chat interfaces, that information may be processed or stored by providers. Understanding data retention policies is critical to prevent accidental leaks of sensitive secrets or personal privacy violations.

3. Security Vulnerabilities

AI systems introduce entirely new attack surfaces. Bad actors can manipulate models through techniques like prompt injection (tricking the AI into ignoring safety guidelines), data poisoning (corrupting training datasets), or generating sophisticated phishing campaigns at scale.

Key Takeaways

• Inherited Bias: AI models can perpetuate and magnify discrimination based on flawed training data.
• Privacy Risks: Entering confidential or personal data into public AI tools can compromise security.
• New Threat Vectors: Modern AI introduces specific security vulnerabilities like prompt injection and data poisoning.

What’s Next?

Understanding these risks is essential as we look toward more advanced capabilities. In the next lesson, we will explore What Is AGI? and examine how it differs from today’s narrow AI systems.