100 research outputs found
Shapley Values with Uncertain Value Functions
We propose a novel definition of Shapley values with uncertain value
functions based on first principles using probability theory. Such uncertain
value functions can arise in the context of explainable machine learning as a
result of non-deterministic algorithms. We show that random effects can in fact
be absorbed into a Shapley value with a noiseless but shifted value function.
Hence, Shapley values with uncertain value functions can be used in analogy to
regular Shapley values. However, their reliable evaluation typically requires
more computational effort.Comment: 12 pages, 1 figure, 1 tabl
Explaining Drift using Shapley Values
Machine learning models often deteriorate in their performance when they are
used to predict the outcomes over data on which they were not trained. These
scenarios can often arise in real world when the distribution of data changes
gradually or abruptly due to major events like a pandemic. There have been many
attempts in machine learning research to come up with techniques that are
resilient to such Concept drifts. However, there is no principled framework to
identify the drivers behind the drift in model performance. In this paper, we
propose a novel framework - DBShap that uses Shapley values to identify the
main contributors of the drift and quantify their respective contributions. The
proposed framework not only quantifies the importance of individual features in
driving the drift but also includes the change in the underlying relation
between the input and output as a possible driver. The explanation provided by
DBShap can be used to understand the root cause behind the drift and use it to
make the model resilient to the drift
Socio-economic disparities and COVID-19 in the USA
COVID-19 is not a universal killer. We study the spread of COVID-19 at the
county level for the United States up until the 15 of August, 2020. We
show that the prevalence of the disease and the death rate are correlated with
the local socio-economic conditions often going beyond local population density
distributions, especially in rural areas. We correlate the COVID-19 prevalence
and death rate with data from the US Census Bureau and point out how the
spreading patterns of the disease show asymmetries in urban and rural areas
separately and is preferentially affecting the counties where a large fraction
of the population is non-white. Our findings can be used for more targeted
policy building and deployment of resources for future occurrence of a pandemic
due to SARS-CoV-2. Our methodology, based on interpretable machine learning and
game theory, can be extended to study the spread of other diseases.Comment: 10 pages, 5 figures and 1 tabl
A Baseline for Shapley Values in MLPs: from Missingness to Neutrality
Being able to explain a prediction as well as having a model that performs
well are paramount in many machine learning applications. Deep neural networks
have gained momentum recently on the basis of their accuracy, however these are
often criticised to be black-boxes. Many authors have focused on proposing
methods to explain their predictions. Among these explainability methods,
feature attribution methods have been favoured for their strong theoretical
foundation: the Shapley value. A limitation of Shapley value is the need to
define a baseline (aka reference point) representing the missingness of a
feature. In this paper, we present a method to choose a baseline based on a
neutrality value: a parameter defined by decision makers at which their choices
are determined by the returned value of the model being either below or above
it. Based on this concept, we theoretically justify these neutral baselines and
find a way to identify them for MLPs. Then, we experimentally demonstrate that
for a binary classification task, using a synthetic dataset and a dataset
coming from the financial domain, the proposed baselines outperform, in terms
of local explanability power, standard ways of choosing them
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