From Network Inequality to Network Fairness: A Perspective on Responsible Decision-Making
Read the original on arXiv Machine Learning →The article discusses how social networks influence decision-making and opportunity distribution, noting that network-generating mechanisms often mirror existing inequalities and can amplify disparities when used in technology. It identifies ten network effects that bias the link between intended measurements and observed data, using academic hiring as a case study to show that network biases can be neither inherently harmful nor beneficial. The authors argue for a comprehensive, networked fairness framework that incorporates both distributive and procedural justice and involves all stakeholders.
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