Bias & Fairness in AI

Introduction

Artificial Intelligence (AI) systems are increasingly employed in critical decision-making processes, from hiring to law enforcement. This lesson explores the concepts of bias and fairness in AI, aiming to raise awareness of ethical considerations in AI development and deployment.

Key Concepts

• Bias: Systematic favoritism or prejudice towards a particular group based on characteristics such as race, gender, or age.
• Fairness: The principle that AI systems should make decisions that are free from bias and equitable across different groups.
• Discrimination: Unjust or prejudicial treatment of different categories of people, often as a result of biased AI systems.

Types of Bias

Bias in AI can manifest in various forms:

1. Data Bias: Arises from biased training data.
2. Algorithmic Bias: Results from the design of the algorithm itself.
3. Human Bias: Introduced by human decisions during the data collection process.

Impact of Bias

Examples of bias impact include:

• Discriminatory hiring practices based on biased resume screening algorithms.
• Racial profiling by predictive policing tools.
• Inaccuracies in medical diagnosis AI that affect certain demographic groups more than others.

Measuring Fairness

To evaluate the fairness of an AI model, several metrics can be used:

• Demographic Parity: Ensures that the decision outcomes are independent of protected attributes.
• Equal Opportunity: Requires that true positive rates are equal across groups.
• Calibration: Ensures predicted probabilities are accurate across different groups.

Best Practices for Reducing Bias

To mitigate bias in AI systems, consider these best practices:

1. Conduct regular audits of data for bias.
2. Involve diverse teams in AI development.
3. Implement fairness-aware algorithms.
4. Seek stakeholder feedback during the development process.

FAQ