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Exploring Fairness and Bias in Algorithms and Word Embedding Michael A. So snick Thesis Advisor: Aaron Roth, PhD Engineering Advisor: Sam path Kennan, PhD Senior Capstone Thesis (EAS 499) University
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Start by gathering relevant data related to the subject you want to explore fairness and bias for.
02
Identify the key variables in the data that can potentially introduce bias or impact fairness.
03
Clean and preprocess the data to ensure consistency and validity.
04
Conduct a thorough exploratory data analysis to understand the patterns and distributions in the data.
05
Apply appropriate statistical techniques or machine learning algorithms to identify biases or fairness issues.
06
Interpret the results and analyze the extent to which fairness and bias may be present.
07
Take corrective actions if biases or fairness issues are detected.
08
Document the entire process and results to ensure transparency and reproducibility.

Who needs exploring fairness and bias?

01
Researchers who want to study the impact of biases and fairness in various domains.
02
Data scientists and analysts who want to develop fair and unbiased algorithms or models.
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Organizations and companies that want to ensure their decision-making processes are free from biases and promote fairness.
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Policy makers who need to understand and address biases in policies and regulations.
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Social scientists who want to analyze the societal impact of biases and promote fairness.
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Exploring fairness and bias involves examining potential biases and ensuring fairness in decision-making processes.
Organizations and entities that make decisions affecting individuals or groups.
Fill out exploring fairness and bias by conducting thorough analysis and documentation of decision-making processes.
The purpose of exploring fairness and bias is to promote transparency and equity in decision-making.
Information on potential biases, measures taken to address biases, and outcomes of decision-making processes.
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