Delve into the core of responsible AI with Microsoft’s commitment to fairness. Uncover how the anti-bias principle shapes AI systems, preventing the reflection of biases from training datasets. Explore the pivotal role this principle plays in creating unbiased and equitable artificial intelligence solutions.
Table of Contents
Question
To complete the sentence, select the appropriate option in the answer area.
According to Microsoft’s _________________ principle of responsible AI, AI systems should NOT reflect biases from the data sets that are used to train the systems.
A. accountability
B. fairness
C. inclusiveness
D. transparency
Answer
B. fairness
Explanation
The correct answer is B. fairness.
According to Microsoft’s fairness principle of responsible AI, AI systems should not reflect biases from the data sets that are used to train the systems. Bias is a systematic error that leads to unfair or inaccurate outcomes for some groups of people. For example, an AI system that predicts the likelihood of loan default based on demographic data might discriminate against certain ethnic groups or genders if the training data is skewed or unrepresentative. To avoid such bias, AI developers should use diverse and inclusive data sets, apply fairness metrics and techniques, and monitor and evaluate the performance of their systems across different groups. By doing so, they can ensure that their AI systems are fair and respectful of human dignity and rights.
References
Microsoft Docs > Azure > Cloud Adoption Framework > Adopt > Innovate > Responsible and trusted AI
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