Is Machine Learning Unsafe and Irresponsible in Social Sciences? Paradoxes and Reconsidering from Recidivism Prediction Tasks
This addresses the problem of ensuring safe and responsible use of machine learning in high-stakes social science applications, such as recidivism prediction, for researchers and practitioners.
The paper tackles the debate over machine learning's safety and responsibility in social sciences, particularly in recidivism prediction, and proposes a new paradigm that integrates computational methods with conventional social science approaches.
The paper addresses some fundamental and hotly debated issues for high-stakes event predictions underpinning the computational approach to social sciences. We question several prevalent views against machine learning and outline a new paradigm that highlights the promises and promotes the infusion of computational methods and conventional social science approaches.