2,168 research outputs found
The Relations Between Pedagogical and Scientific Explanations of Algorithms: Case Studies from the French Administration
The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology based on field work, which should go beyond the in-vitro analysis of UX to examine in-vivo problems emerging in the field. Our methodology is also comparative, as it chooses to steer away from the almost exclusive focus on ML to compare its challenges with those faced by more vintage algorithms. Finally, it is also philosophical, as we defend the relevance of the philosophical literature to define the epistemic desiderata of a good explanation. This study was conducted in collaboration with Etalab, a Task Force of the French Prime Minister in charge of Open Data & Open Government Policies, dealing in particular with the enforcement of the right to an explanation. In order to illustrate and refine our methodology before going up to scale, we conduct a preliminary work of case studies on the main different types of algorithms used by the French administration: computation, matching algorithms and ML. We study the merits and drawbacks of a recent approach to explanation, which we baptize input-output black box reasoning or BBR for short. We begin by presenting a conceptual framework including the distinctions necessary to a study of pedagogical explainability. We proceed to algorithmic case studies, and draw model-specific and model-agnostic lessons and conjectures
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
Generation of Policy-Level Explanations for Reinforcement Learning
Though reinforcement learning has greatly benefited from the incorporation of
neural networks, the inability to verify the correctness of such systems limits
their use. Current work in explainable deep learning focuses on explaining only
a single decision in terms of input features, making it unsuitable for
explaining a sequence of decisions. To address this need, we introduce
Abstracted Policy Graphs, which are Markov chains of abstract states. This
representation concisely summarizes a policy so that individual decisions can
be explained in the context of expected future transitions. Additionally, we
propose a method to generate these Abstracted Policy Graphs for deterministic
policies given a learned value function and a set of observed transitions,
potentially off-policy transitions used during training. Since no restrictions
are placed on how the value function is generated, our method is compatible
with many existing reinforcement learning methods. We prove that the worst-case
time complexity of our method is quadratic in the number of features and linear
in the number of provided transitions, . By applying
our method to a family of domains, we show that our method scales well in
practice and produces Abstracted Policy Graphs which reliably capture
relationships within these domains.Comment: Accepted to Proceedings of the Thirty-Third AAAI Conference on
Artificial Intelligence (2019
Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder
In visual object classification, humans often justify their choices by
comparing objects to prototypical examples within that class. We may therefore
increase the interpretability of deep learning models by imbuing them with a
similar style of reasoning. In this work, we apply this principle by
classifying Alzheimer's Disease based on the similarity of images to training
examples within the latent space. We use a contrastive loss combined with a
diffusion autoencoder backbone, to produce a semantically meaningful latent
space, such that neighbouring latents have similar image-level features. We
achieve a classification accuracy comparable to black box approaches on a
dataset of 2D MRI images, whilst producing human interpretable model
explanations. Therefore, this work stands as a contribution to the pertinent
development of accurate and interpretable deep learning within medical imaging
Logic-Based Explainability in Machine Learning
The last decade witnessed an ever-increasing stream of successes in Machine
Learning (ML). These successes offer clear evidence that ML is bound to become
pervasive in a wide range of practical uses, including many that directly
affect humans. Unfortunately, the operation of the most successful ML models is
incomprehensible for human decision makers. As a result, the use of ML models,
especially in high-risk and safety-critical settings is not without concern. In
recent years, there have been efforts on devising approaches for explaining ML
models. Most of these efforts have focused on so-called model-agnostic
approaches. However, all model-agnostic and related approaches offer no
guarantees of rigor, hence being referred to as non-formal. For example, such
non-formal explanations can be consistent with different predictions, which
renders them useless in practice. This paper overviews the ongoing research
efforts on computing rigorous model-based explanations of ML models; these
being referred to as formal explanations. These efforts encompass a variety of
topics, that include the actual definitions of explanations, the
characterization of the complexity of computing explanations, the currently
best logical encodings for reasoning about different ML models, and also how to
make explanations interpretable for human decision makers, among others
Deep learning systems as complex networks
Thanks to the availability of large scale digital datasets and massive
amounts of computational power, deep learning algorithms can learn
representations of data by exploiting multiple levels of abstraction. These
machine learning methods have greatly improved the state-of-the-art in many
challenging cognitive tasks, such as visual object recognition, speech
processing, natural language understanding and automatic translation. In
particular, one class of deep learning models, known as deep belief networks,
can discover intricate statistical structure in large data sets in a completely
unsupervised fashion, by learning a generative model of the data using
Hebbian-like learning mechanisms. Although these self-organizing systems can be
conveniently formalized within the framework of statistical mechanics, their
internal functioning remains opaque, because their emergent dynamics cannot be
solved analytically. In this article we propose to study deep belief networks
using techniques commonly employed in the study of complex networks, in order
to gain some insights into the structural and functional properties of the
computational graph resulting from the learning process.Comment: 20 pages, 9 figure
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A Model Theoretic Perspective on Phonological Feature Systems
This paper uses model theory to analyze the formal properties of three phonological feature systems: privative, full binary, and underspecified. By systematically manipulating the choice of logical language and representational primitives, it is shown that logical negation effectively converts any feature system into a full binary one. This further implies that in order to have underspecification or non-binary feature oppositions, valuation should be encoded into the representational primitives rather than derived through the logical connectives. These results are obtained by comparing the predicted natural classes of each formalization
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