• Corpus ID: 232403994

A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms

  title={A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms},
  author={Ana Lucic and Madhulika Srikumar and Umang Bhatt and Alice Xiang and Ankur Taly and Qingzi Vera Liao and M. de Rijke},
Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders have different transparency needs [14]. Previous work found that the majority of deployed transparency mechanisms primarily serve technical stakeholders [2]. In our work, we want to investigate how well transparency mechanisms might work in practice for a more diverse set of stakeholders by conducting a large-scale… 
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
  • C. Rudin
  • Computer Science
    Nat. Mach. Intell.
  • 2019
This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications whereinterpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.