Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making

• URL: http://arxiv.org/abs/2103.02071v1
• Date: Tue, 2 Mar 2021 22:50:45 GMT
• Title: Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making
• Authors: Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, and Kalyan Veeramachaneni
• Abstract summary: Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts -- who often have no expertise in ML or data science -- are asked to use ML predictions to make high-stakes decisions. We investigate the ML usability challenges present in the domain of child welfare screening through a series of collaborations with child welfare screeners.
• Score: 67.72855777115772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts -- who often have no expertise in ML or data science -- are asked to use ML predictions to make high-stakes decisions. Multiple ML usability challenges can appear as result, such as lack of user trust in the model, inability to reconcile human-ML disagreement, and ethical concerns about oversimplification of complex problems to a single algorithm output. In this paper, we investigate the ML usability challenges present in the domain of child welfare screening through a series of collaborations with child welfare screeners, which included field observations, interviews, and a formal user study. Through our collaborations, we identified four key ML challenges, and honed in on one promising ML augmentation tool to address them (local factor contributions). We also composed a list of design considerations to be taken into account when developing future augmentation tools for child welfare screeners and similar domain experts.

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