Risks Associated with AI Systems: Bias, Explainability, Security, and Misuse

Intermediate

AI systems pose various risks if not carefully managed:

  • Bias and Discrimination: Data-driven bias can lead to unfair treatment. For example, facial recognition systems historically perform worse on minorities.
  • Lack of Explainability: Complex models like deep neural networks are often black boxes, hindering understanding and trust.
  • Security Threats: AI systems can be vulnerable to adversarial attacks that manipulate outputs or expose sensitive data.
  • Misuse: AI can be employed maliciously, such as generating deepfakes or automating cyberattacks.

+--------------+      +--------------+      +--------------+
| Bias in Data | ---> | Black Box   | ---> | Security &   |
| & Models    |      | Explanation |      | misuse Risks |
+--------------+      +--------------+      +--------------+

Recognizing and mitigating these risks is critical for safe AI integration.