221 research outputs found
Algorithms to estimate Shapley value feature attributions
Feature attributions based on the Shapley value are popular for explaining
machine learning models; however, their estimation is complex from both a
theoretical and computational standpoint. We disentangle this complexity into
two factors: (1)~the approach to removing feature information, and (2)~the
tractable estimation strategy. These two factors provide a natural lens through
which we can better understand and compare 24 distinct algorithms. Based on the
various feature removal approaches, we describe the multiple types of Shapley
value feature attributions and methods to calculate each one. Then, based on
the tractable estimation strategies, we characterize two distinct families of
approaches: model-agnostic and model-specific approximations. For the
model-agnostic approximations, we benchmark a wide class of estimation
approaches and tie them to alternative yet equivalent characterizations of the
Shapley value. For the model-specific approximations, we clarify the
assumptions crucial to each method's tractability for linear, tree, and deep
models. Finally, we identify gaps in the literature and promising future
research directions
Demographic Parity Inspector: Fairness Audits via the Explanation Space
Even if deployed with the best intentions, machine learning methods can
perpetuate, amplify or even create social biases. Measures of (un-)fairness
have been proposed as a way to gauge the (non-)discriminatory nature of machine
learning models. However, proxies of protected attributes causing
discriminatory effects remain challenging to address. In this work, we propose
a new algorithmic approach that measures group-wise demographic parity
violations and allows us to inspect the causes of inter-group discrimination.
Our method relies on the novel idea of measuring the dependence of a model on
the protected attribute based on the explanation space, an informative space
that allows for more sensitive audits than the primary space of input data or
prediction distributions, and allowing for the assertion of theoretical
demographic parity auditing guarantees. We provide a mathematical analysis,
synthetic examples, and experimental evaluation of real-world data. We release
an open-source Python package with methods, routines, and tutorials
xxAI - Beyond Explainable AI
This is an open access book.
Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans.
Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed.
After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.https://digitalcommons.unomaha.edu/isqafacbooks/1000/thumbnail.jp
xxAI - Beyond Explainable AI
This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
Explainability is a crucial requirement for effectiveness as well as the
adoption of Machine Learning (ML) models supporting decisions in high-stakes
public policy areas such as health, criminal justice, education, and
employment, While the field of explainable has expanded in recent years, much
of this work has not taken real-world needs into account. A majority of
proposed methods use benchmark datasets with generic explainability goals
without clear use-cases or intended end-users. As a result, the applicability
and effectiveness of this large body of theoretical and methodological work on
real-world applications is unclear. This paper focuses on filling this void for
the domain of public policy. We develop a taxonomy of explainability use-cases
within public policy problems; for each use-case, we define the end-users of
explanations and the specific goals explainability has to fulfill; third, we
map existing work to these use-cases, identify gaps, and propose research
directions to fill those gaps in order to have a practical societal impact
through ML.Comment: Submitted for review at Communications of the AC
Shapley Computations Using Surrogate Model-Based Trees
Shapley-related techniques have gained attention as both global and local
interpretation tools because of their desirable properties. However, their
computation using conditional expectations is computationally expensive.
Approximation methods suggested in the literature have limitations. This paper
proposes the use of a surrogate model-based tree to compute Shapley and SHAP
values based on conditional expectation. Simulation studies show that the
proposed algorithm provides improvements in accuracy, unifies global Shapley
and SHAP interpretation, and the thresholding method provides a way to
trade-off running time and accuracy
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