Types of Fairness in AI
The various types of fairness present in artificial intelligence are given below,
- Group Fairness: Group fairness ensures that distinct groups are treated equally in the AI system. The main goal of group fairness is that the AI system should not favor or disadvantage any group or disproportionately harm them. Some examples of group fairness metrics are demographic parity, disparate mistreatment, equal opportunity, etc. Demographic parity is a metric used to ensure that the positive outcomes of all the demographic groups are in equal proportion. The disparate mistreatment is a metric that ensures all groups have similar false positives and false negative rates and they also focus on reducing the difference in error rates between the groups.
- Individual Fairness: Individual Fairness ensures that similar individuals are treated equally and similarly by the AI systems, they should not be treated by the group. Unlike group fairness, this mainly focuses on the individual’s attributes. Individual fairness focuses on how the individuals are treated. Individual fairness addresses discrimination towards an individual when the decisions are made based on group characteristics.
- Procedural Fairness: Procedural fairness ensures that the decision-making process is fair and transparent. This is achieved by the implementation of AI systems that have a transparent decision-making process that is fair and accountable. This also involves algorithmic transparency and auditing and also follows accountability. This mainly focuses on the procedure such as the development and deployment of the AI system.
- Counterfactual Fairness: The counterfactual fairness ensures that AI systems are fair in all situations even during hypothetical scenarios. This ensures that an individual receives the same decision regardless of their group membership or even when their attributes are distinct in counterfactual situations. This raises the importance of the impact of the AI system decisions on individuals and groups as well.
- Causal Fairness: Causal fairness ensures that the system doesn’t make decisions based on historical biases and inequalities. This is achieved by developing a system to avoid historical biases. This helps the system focus on the causal relationships rather than historically existing biases. The system developers identify and mitigate historical biases by focusing on the causal relationships between the variables instead of solely replicating the patterns in historical data.
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