Learn how to identify and address fairness issues in AI models. Prepare for the IBM Artificial Intelligence Fundamentals certification exam with expert insights.
Table of Contents
Question
An AI model is systematically giving an advantage or disadvantage to a certain group.
This is an issue of which of the following?
A. Privacy
B. Transparency
C. Explainability
D. Robustness
E. Fairness
Answer
E. Fairness
Explanation
When an AI model or system gives one group an advantage over another group, this is an issue of Fairness. Care must be taken in the training and testing of AI systems to mitigate unwanted bias in the data sets or in the outcomes.
Fairness in AI refers to ensuring that AI models do not discriminate against certain groups or individuals based on sensitive attributes such as race, gender, age, or socioeconomic status. If an AI model systematically gives an advantage or disadvantage to a certain group, it is an issue of fairness.
Fairness is a critical ethical consideration in AI development and deployment. Biased data, flawed algorithms, or improper model training can lead to unfair outcomes that perpetuate or amplify societal inequalities. It is essential for AI practitioners to proactively identify and mitigate fairness issues throughout the AI lifecycle.
The other options listed are also important considerations in AI ethics, but do not directly address the scenario described in the question:
- Privacy refers to protecting individuals’ personal information and data.
- Transparency involves being open and clear about how AI systems work and make decisions.
- Explainability is the ability to interpret and understand the reasoning behind an AI model’s outputs.
- Robustness relates to an AI system’s ability to perform well under various conditions and inputs.
By prioritizing fairness in AI development, we can work towards building AI systems that are unbiased, equitable, and beneficial for all.
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