Xuan Yang
With over 4 years of data science experience in the auditing industry, I have a proven track record of developing data science approaches for audit in various product areas. I am also keen in developing noval DS approach for audits especially related to model evaluation, statistical testing, and anomaly detection using Python, SQL, and visualization tools. With skills in data mining, machine learning, natural language processing, predictive analytics, business intelligence, and data visualization, I am passionate about using data to improve the efficiency and effectiveness of auditing.
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Large technology firms face the problem of moderating content on their platforms for compliance with laws and policies. To accomplish this at the scale of billions of pieces of content per day, a combination of human and machine review are necessary to label content. However, human error and subjective methods of measure are inherent in many audit procedures. This paper introduces statistical analysis methods and mathematical techniques to determine, quantify, and minimize these sources of risk. Through these methodologies it can be shown that we are able to reduce reviewer bias.
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