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Provable Fairness for Neural Network Models Using
Formal Verification
Georgian BorcaTasciuc1,×,1, Singh Guo1,1, Stanley Bak1 and Steven Skiena1
1Department of Computer Science, Stony Brook University,
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How to fill out provable fairness for neural
How to fill out provable fairness for neural
01
Gather all the necessary data related to the neural network algorithm.
02
Calculate the expected output for a given input using the neural network algorithm.
03
Compare the expected output with the actual output obtained from the neural network.
04
Use statistical methods to determine the level of fairness in the neural network algorithm.
05
Record the results and provide a detailed report on the provable fairness of the neural network algorithm.
Who needs provable fairness for neural?
01
Researchers working on developing neural network algorithms
02
Regulatory bodies overseeing the use of neural networks in various industries
03
Users and consumers concerned about bias and discrimination in neural network decision making
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What is provable fairness for neural?
Provable fairness for neural refers to the ability to demonstrate that a neural network model operates in a manner that is fair and unbiased, often by using mathematical techniques or algorithms to verify that the outcomes are equitable across different demographic groups.
Who is required to file provable fairness for neural?
Organizations and individuals that deploy neural network models in decision-making processes that could impact individuals or groups, especially in regulated sectors like finance, healthcare, or employment, are typically required to file for provable fairness.
How to fill out provable fairness for neural?
To fill out provable fairness for neural, one should gather necessary model data, perform fairness assessments using appropriate metrics and methodologies, document the findings, and submit the required forms detailing the fairness validation process and outcomes.
What information must be reported on provable fairness for neural?
Information to be reported on provable fairness for neural includes the model's performance metrics across different demographic groups, data sources used, methodologies for fairness assessment, and any adjustments made to improve fairness.
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