563 research outputs found
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature
Explaining classifiers’ outputs with causal models and argumentation
We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for mod-els’ outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the ex-tracted bipolar AFs may be used as relation-based explanations for the outputs of causal models. We then evaluate our method empirically when the causal models represent (Bayesian and neural network) machine learning models for classification. The results show advantages over a popular approach from the literature, both in highlighting specific relationships between feature and classification variables and in generating counterfactual explanations with respect to a commonly used metric
Clarity: an improved gradient method for producing quality visual counterfactual explanations
Visual counterfactual explanations identify modifications to an image that
would change the prediction of a classifier. We propose a set of techniques
based on generative models (VAE) and a classifier ensemble directly trained in
the latent space, which all together, improve the quality of the gradient
required to compute visual counterfactuals. These improvements lead to a novel
classification model, Clarity, which produces realistic counterfactual
explanations over all images. We also present several experiments that give
insights on why these techniques lead to better quality results than those in
the literature. The explanations produced are competitive with the
state-of-the-art and emphasize the importance of selecting a meaningful input
space for training
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees
Systems based on artificial intelligence and machine learning models should
be transparent, in the sense of being capable of explaining their decisions to
gain humans' approval and trust. While there are a number of explainability
techniques that can be used to this end, many of them are only capable of
outputting a single one-size-fits-all explanation that simply cannot address
all of the explainees' diverse needs. In this work we introduce a
model-agnostic and post-hoc local explainability technique for black-box
predictions called LIMEtree, which employs surrogate multi-output regression
trees. We validate our algorithm on a deep neural network trained for object
detection in images and compare it against Local Interpretable Model-agnostic
Explanations (LIME). Our method comes with local fidelity guarantees and can
produce a range of diverse explanation types, including contrastive and
counterfactual explanations praised in the literature. Some of these
explanations can be interactively personalised to create bespoke, meaningful
and actionable insights into the model's behaviour. While other methods may
give an illusion of customisability by wrapping, otherwise static, explanations
in an interactive interface, our explanations are truly interactive, in the
sense of allowing the user to "interrogate" a black-box model. LIMEtree can
therefore produce consistent explanations on which an interactive exploratory
process can be built
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to
understand and act on algorithmic predictions. An intriguing class of
explanations is through counterfactuals, hypothetical examples that show people
how to obtain a different prediction. We posit that effective counterfactual
explanations should satisfy two properties: feasibility of the counterfactual
actions given user context and constraints, and diversity among the
counterfactuals presented. To this end, we propose a framework for generating
and evaluating a diverse set of counterfactual explanations based on
determinantal point processes. To evaluate the actionability of
counterfactuals, we provide metrics that enable comparison of
counterfactual-based methods to other local explanation methods. We further
address necessary tradeoffs and point to causal implications in optimizing for
counterfactuals. Our experiments on four real-world datasets show that our
framework can generate a set of counterfactuals that are diverse and well
approximate local decision boundaries, outperforming prior approaches to
generating diverse counterfactuals. We provide an implementation of the
framework at https://github.com/microsoft/DiCE.Comment: 13 page
Achieving descriptive accuracy in explanations via argumentation: the case of probabilistic classifiers
The pursuit of trust in and fairness of AI systems in order to enable human-centric goals has been gathering pace of late, often supported by the use of explanations for the outputs of these systems. Several properties of explanations have been highlighted as critical for achieving trustworthy and fair AI systems, but one that has thus far been overlooked is that of descriptive accuracy (DA), i.e., that the explanation contents are in correspondence with the internal working of the explained system. Indeed, the violation of this core property would lead to the paradoxical situation of systems producing explanations which are not suitably related to how the system actually works: clearly this may hinder user trust. Further, if explanations violate DA then they can be deceitful, resulting in an unfair behavior toward the users. Crucial as the DA property appears to be, it has been somehow overlooked in the XAI literature to date. To address this problem, we consider the questions of formalizing DA and of analyzing its satisfaction by explanation methods. We provide formal definitions of naive, structural and dialectical DA, using the family of probabilistic classifiers as the context for our analysis. We evaluate the satisfaction of our given notions of DA by several explanation methods, amounting to two popular feature-attribution methods from the literature, variants thereof and a novel form of explanation that we propose. We conduct experiments with a varied selection of concrete probabilistic classifiers and highlight the importance, with a user study, of our most demanding notion of dialectical DA, which our novel method satisfies by design and others may violate. We thus demonstrate how DA could be a critical component in achieving trustworthy and fair systems, in line with the principles of human-centric AI
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and
explainable machine learning models. Interpretability and explainability lie at
the core of many machine learning and statistical applications in medicine,
economics, law, and natural sciences. Although interpretability and
explainability have escaped a clear universal definition, many techniques
motivated by these properties have been developed over the recent 30 years with
the focus currently shifting towards deep learning methods. In this review, we
emphasise the divide between interpretability and explainability and illustrate
these two different research directions with concrete examples of the
state-of-the-art. The review is intended for a general machine learning
audience with interest in exploring the problems of interpretation and
explanation beyond logistic regression or random forest variable importance.
This work is not an exhaustive literature survey, but rather a primer focusing
selectively on certain lines of research which the authors found interesting or
informative
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