AI Ethics & Safety
Algorithmic bias, fairness, AI alignment problem, safety, and societal impact.
AI Ethics and Safety
As Artificial Intelligence systems become increasingly integrated into society, addressing the ethical implications and ensuring their safety is paramount.
Algorithmic Bias and Fairness
Algorithmic bias occurs when an AI system produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process.
| Type of Bias | Description | Example |
|---|---|---|
| Historical Bias | Arises when training data reflects past inequalities. | A hiring AI downgrades female candidates because past hires were predominantly male. |
| Representation Bias | Occurs when the training sample underrepresents certain groups. | Facial recognition failing on darker skin tones. |
| Measurement Bias | Happens when the features chosen to measure a target are flawed. | Using arrest rates as a proxy for crime rates. |
Mitigating bias requires careful auditing of both the training data and the model's predictions across different demographic groups.
The AI Alignment Problem
The AI alignment problem refers to the challenge of ensuring that an AI system's goals and behaviors are perfectly aligned with human values.
Misalignment Scenarios
- Reward Hacking: The AI achieves the literal objective in an unintended or harmful way (e.g., a cleaning robot hiding dirt instead of cleaning it).
- Instrumental Convergence: An AI might pursue potentially harmful sub-goals, like resource acquisition or self-preservation, to achieve its primary objective.
Safety and Societal Impact
Ensuring AI safety involves building systems that operate reliably under novel conditions and fail gracefully.
Robustness, transparency, and interpretability are core pillars of AI safety. Users (or \{User\}) must understand how and why an AI makes specific decisions.