Fairness
2024 | AI Dictionary
What is Fairness in AI: Ensuring AI systems do not discriminate or produce biased outcomes based on protected characteristics, promoting equity and justice.
What is Fairness?
Fairness in AI refers to the principle of ensuring that AI systems treat all individuals and groups impartially and without bias. It focuses on preventing discrimination based on sensitive attributes such as race, gender, age, or socioeconomic status, and ensuring equitable outcomes for all stakeholders. Achieving fairness in AI requires addressing biases in data, algorithms, and decision-making processes.
Types of Fairness
- Individual Fairness: Ensures that similar individuals are treated similarly by the AI system.
- Group Fairness: Ensures that different groups (e.g., based on gender, race) receive fair and equitable outcomes.
- Subgroup Fairness: Ensures fairness within smaller subgroups or populations, addressing disparities that may exist within certain subsets of the data.
Applications of Fairness
- Hiring Systems: AI used in recruitment should avoid biases in selecting candidates based on race, gender, or other protected characteristics.
- Healthcare: Ensuring fair treatment for all patients by eliminating biases in diagnostic tools or treatment recommendations.
- Criminal Justice: AI used in sentencing or parole decisions must be fair and not perpetuate societal biases, such as racial disparities.
Example of Fairness
An AI-driven recruitment tool might adjust its algorithms to ensure that it does not favor applicants from a particular gender or ethnic background, ensuring equal opportunity for all candidates.
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