Learn how fairness in artificial intelligence aims to reduce unwanted bias and ensure protected attributes do not lead to discriminatory outcomes. Expert insights for IBM’s AI Fundamentals certification exam.
Table of Contents
Question
Fairness in AI aims to minimize which of the following?
A. Favorable outcomes
B. Protected attributes
C. Unwanted bias
D. Unfavorable predictions
Answer
Fairness in AI aims to minimize unwanted bias (C).
Explanation
By minimizing unwanted bias, the AI model is less likely to give an unfair advantage any group. This makes the results generated by the AI model fairer across all groups.
The goal of fairness in artificial intelligence systems is to ensure that protected attributes like race, gender, age, etc. do not lead to discriminatory or inequitable outcomes. While completely favorable outcomes for all are ideal, the key aim is to mitigate unwanted bias.
Unwanted bias can creep into AI systems in various ways:
- Biased training data that reflects historical discrimination
- Improper selection of features/variables that serve as proxies for protected attributes
- Flawed modeling approaches and evaluation metrics
So fairness techniques focus on assessing and minimizing this unwanted bias through methods like:
- Careful data collection and bias testing of training data
- Exclusion of protected attributes and their close proxies as input features
- Bias testing of model outcomes across groups to detect disparate impact
- Mitigation approaches like reweighting, resampling, or adversarial debiasing
- Ongoing monitoring of live systems for emergent bias
The goal is equitable performance and impact across protected groups, not simply maximizing favorable outcomes overall or minimizing unfavorable individual predictions. Fairness at a group level takes precedence.
So in summary, while there are complexities in defining and achieving AI fairness, the core aim is minimizing unwanted bias (C) to prevent disparate outcomes based on protected attributes. Unbiased AI systems are essential for ethical and responsible deployment of artificial intelligence.
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