Evaluation of fairness in machine learning

Reference no: EM132636416 , Length: word count:1000

Review of Research Paper

You are required to discuss one of the following research papers:

• Blodgett et al.'s survey on bias in Natural Language Processing
• Holstein et al. CHI'19 paper titled "Improving fairness in machine learning systems: What do industry practitioners need?"
• Olteanu et al.´s survey on biases, methodological pitfalls, and ethical coundaries in social data
• Huthchinson & Margaret's paper on the history of fairness in machine learning:
• Any of the full papers accepted at any of the latest two editions of the ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*)

Provide a 750-1000 words word document addressing the following points:
• What is the state-of-the-art on evaluation of fairness in machine learning?
• What are the main challenges on defining 'fairness' and fairness-aware measures in this context?
• Is it possible to measure fairness in the context of your WIL project? If so, how?

In this question my WIL project was predict the number of cases in COVID-19 in USA for 28 day in future. and we assumed the cost of patients in hospital to calculate the total cost per week(7 days ) of month and answer the question Is POSSIBLE to measure fairness in the context.

Attachment:- Review of Research Paper.rar

Reference no: EM132636416

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