Stop Explaining Black Box Machine Learning Models

A recent paper by Cynthia Rudin claims Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Using classification as example.

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Black box machine learning models are currently being used for high stakes decision-making throughout society causing problems throughout healthcare criminal justice and in other domains.

Stop explaining black box machine learning models. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and use Interpretable Models Instead Cynthia Rudin Professor Computer Science. Black box machine learning models are currently being used for high stakes decision-making throughout society causing problems throughout healthcare criminal justice and in other domains. But machine learning is dynamic.

Even for classic domains of machine learning where latent representations of data need to be constructed there could exist interpretable models that are as accurate as black box models. A black box model is either a function that is too complicated for any human. With widespread use of machine learning there have been serious societal consequences from using black box models for high-stakes decisions including flawed bail and parole decisions in criminal justice.

2019 Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. When we started machine learning at first three years ago we had. 2 Key Issues with Explainable ML.

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. There has been an increasing trend in healthcare and criminal justice to leverage machine learning ML. Nature Machine Intelligence 1 206-215.

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and use Interpretable Models Instead Cynthia Rudin PhD DU About this Event With widespread use of machine learning there have been serious societal consequences from using black box models for high-stakes decisions including flawed bail and parole decisions in criminal justice. Being so dynamic and having this internal ability to adapt and to learn improved the data extraction greatly. In a new editorial published in Nature Machine Intelligence Cynthia Rudin associate professor of computer science electrical and computer engineering mathematics and statistical science at Duke University argues that black box models must be abandoned for high-stakes decisions.

Explanations for black box models are not reliable and can be misleading. Please Stop Explaining Black Box Models for High-Stakes Decisions 1 Introduction. Professor of Computer Science.

The network must then make decisions by reasoning about parts of the image so that the explanations are real and not posthoc. Even when so-called explanation models are created she says decision-makers should be opting for. Its a sort of black box - you put the documents as input you say what you want as expected output and the black box itself adapts.

People have hoped that creating methods for explaining these black box models will alleviate some of these problems but trying to textit explain black box models rather than creating models.

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