Learn how to answer the question of what should be done to prevent bias from entering an AI system when training it. Find out why importing diverse training data is the correct answer and how it can help prevent bias.
Table of Contents
Question
What should be done to prevent bias from entering an AI system when training it?
A. Use alternative assumptions.
B. Import diverse training data.
C. Include Proxy variables.
Answer
B. Import diverse training data.
Explanation
Using diverse training data is what should be done to prevent bias from entering an AI system when training it. Diverse training data means that the data covers a wide range of features andpatterns that are relevant for the AI task. Diverse training data can help prevent bias by ensuring that the AI system learns from a balanced and representative sample of the target population or domain. Diverse training data can also help improve the accuracy and generalization of the AI system by capturing more variations and scenarios in the data.
Bias is a deviation from the expected or desired outcome that harms or disadvantages some individuals or groups. Bias can enter an AI system when training it due to various factors, such as the data, the algorithms, the context, or the human factors involved in the AI system. One of the most common sources of bias is the data used to train the AI system, which can reflect the existing prejudices, stereotypes, or inequalities in the real world.
To prevent bias from entering an AI system when training it, it is important to import diverse training data. Diverse training data refers to data that represents the diversity and distribution of the target population or the intended users of the AI system, such as different genders, races, ethnicities, ages, incomes, education levels, and so on. Importing diverse training data can help prevent bias by:
- Increasing the accuracy and precision of the AI system, leading to correct or relevant outputs for different groups or individuals.
- Reducing or eliminating the biases in the AI system, resulting in fair or equitable outcomes for different groups or individuals.
- Enhancing the generalizability and robustness of the AI system, making it able to handle diverse or novel inputs or situations.
Option A is not correct because using alternative assumptions is not a sufficient or effective way to prevent bias from entering an AI system when training it. Alternative assumptions refer to different ways of interpreting or explaining the data or the results of the AI system, such as causal, probabilistic, or counterfactual reasoning. Using alternative assumptions can help understand or justify the AI system’s outputs, but it does not address the underlying causes or sources of bias in the data or the algorithms.
Option C is not correct because including proxy variables is not a desirable or ethical way to prevent bias from entering an AI system when training it. Proxy variables refer to variables that are correlated with or indicative of other variables that are not directly observable or measurable, such as gender, race, or income. Including proxy variables can introduce or amplify bias in the AI system, as it can influence the AI system’s outputs in ways that are unfair or discriminatory for certain groups or individuals.
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