Comparison of Bias and Fairness in AI
Fairness and bias are related concepts. These two topics are very distinct and have many differences in them. Fairness is an intentional goal that works to mitigate bias and bias on the other hand is an unintentional error that occurs in the system. Let’s compare the fairness and bias in AI in different aspects.
Aspect |
Bias |
Fairnes |
---|---|---|
Definition |
Systematic deviation from true value or expectation |
Absence of discrimination or favoritism based on protected characteristics |
Nature |
Can be unintentional and technical |
Inherently deliberate and intentional |
Objective |
Reduce or eliminate systematic deviations |
Ensure equitable treatment and outcomes |
Focus |
Accuracy and reliability of algorithmic output |
Preventing discrimination and promoting equitable treatment |
Impact |
Can lead to unfair outcomes, perpetuate inequalities |
Promotes social justice, equality, and inclusion |
Approaches |
Data preprocessing, algorithmic adjustments, model evaluation |
Fairness-aware algorithms, metrics, and enhancing techniques |
Evaluation |
Accuracy, precision, recall, and fairness-aware metrics |
Fairness metrics like demographic parity, equal opportunity |
Long-term Goals |
Improve performance and reliability of AI systems |
Create inclusive, equitable AI systems promoting social welfare |
Fairness and Bias in Artificial Intelligence
Fairness and bias in artificial intelligence (AI) are critical issues that have gained significant attention in recent years. As AI systems are increasingly being used in various domains and applications, it is crucial to ensure that these systems are fair, unbiased, and equitable. Here’s a detailed overview of fairness and bias in AI.
Table of Content
- What is Bias in AI?
- Types of Bias in AI
- What is Fairness in AI?
- Types of Fairness in AI
- Addressing Fairness and Bias in AI
- Comparison of Bias and Fairness
- Conclusion