Fdaytalk Homework Help: Questions and Answers: What does the principle of fairness in Gen Al entail?
a) Optimizing model architecture to reduce bias
b) Ensuring equitable treatment and addressing biases
c) Promoting diversity within development teams
d) None of the above
Answer:
The principle of fairness in General Artificial Intelligence (Gen AI) typically revolves around ensuring that AI systems operate without bias and treat all individuals and groups equitably. Let’s analyze the given options step by step to determine which one best suits with this principle:
a) Optimizing model architecture to reduce bias:
While reducing bias in model architecture is important and can contribute to fairness, this option focuses specifically on a technical aspect of model development. Fairness encompasses broader concerns than just the model architecture.
b) Ensuring equitable treatment and addressing biases:
This option directly addresses the principle of fairness. It emphasizes equitable treatment of all individuals and actively addressing any biases that may exist in the system. This aligns well with the core idea of fairness in AI, which is to ensure that AI systems do not discriminate and treat all users fairly.
c) Promoting diversity within development teams:
Promoting diversity within development teams is important for reducing bias in AI systems, as diverse teams are more likely to recognize and mitigate various biases. However, this is an indirect approach to fairness. The principle of fairness itself is more about the outcomes and operations of the AI systems rather than the composition of the development teams.
d) None of the above:
Based on the above analysis, the correct options is:
Correct answer: b) Ensuring equitable treatment and addressing biases
Reason: Fairness in AI fundamentally means that the system should treat all individuals and groups equitably, regardless of their characteristics such as race, gender, age, or socioeconomic status. This is directly addressed in option B. The principle of fairness requires actively identifying and mitigating these biases, which is explicitly mentioned in this option.
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