Towards Responsible Data-driven Decision Making in Score-Based Systems

Abolfazl Asudeh, H. V. Jagadish, Julia Stoyanovich
2019 IEEE Data Engineering Bulletin  
Human decision makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating the quality of items such as products, services, or individuals. These scores can be obtained by combining different features either through a process learned by ML models, or using a weight vector designed by human experts, with their past experience and notions of what constitutes item quality. The scores can be used for different evaluation purposes such as ranking or
more » ... fication. In this paper, we view the design of these scores through the lens of responsibility. We present technical methods (i) to assist human experts in designing fair and stable score-based rankings and (ii) to assess and (if needed) enhance the coverage of a training dataset for machine learning tasks such as classification.
dblp:journals/debu/AsudehJS19 fatcat:hfrvdqq3ojdtpblymatk6wbrhe