93,274 research outputs found
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
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging
Local Rule-Based Explanations of Black Box Decision Systems
The recent years have witnessed the rise of accurate but obscure decision
systems which hide the logic of their internal decision processes to the users.
The lack of explanations for the decisions of black box systems is a key
ethical issue, and a limitation to the adoption of machine learning components
in socially sensitive and safety-critical contexts. %Therefore, we need
explanations that reveals the reasons why a predictor takes a certain decision.
In this paper we focus on the problem of black box outcome explanation, i.e.,
explaining the reasons of the decision taken on a specific instance. We propose
LORE, an agnostic method able to provide interpretable and faithful
explanations. LORE first leans a local interpretable predictor on a synthetic
neighborhood generated by a genetic algorithm. Then it derives from the logic
of the local interpretable predictor a meaningful explanation consisting of: a
decision rule, which explains the reasons of the decision; and a set of
counterfactual rules, suggesting the changes in the instance's features that
lead to a different outcome. Wide experiments show that LORE outperforms
existing methods and baselines both in the quality of explanations and in the
accuracy in mimicking the black box
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
We examine counterfactual explanations for explaining the decisions made by
model-based AI systems. The counterfactual approach we consider defines an
explanation as a set of the system's data inputs that causally drives the
decision (i.e., changing the inputs in the set changes the decision) and is
irreducible (i.e., changing any subset of the inputs does not change the
decision). We (1) demonstrate how this framework may be used to provide
explanations for decisions made by general, data-driven AI systems that may
incorporate features with arbitrary data types and multiple predictive models,
and (2) propose a heuristic procedure to find the most useful explanations
depending on the context. We then contrast counterfactual explanations with
methods that explain model predictions by weighting features according to their
importance (e.g., SHAP, LIME) and present two fundamental reasons why we should
carefully consider whether importance-weight explanations are well-suited to
explain system decisions. Specifically, we show that (i) features that have a
large importance weight for a model prediction may not affect the corresponding
decision, and (ii) importance weights are insufficient to communicate whether
and how features influence decisions. We demonstrate this with several concise
examples and three detailed case studies that compare the counterfactual
approach with SHAP to illustrate various conditions under which counterfactual
explanations explain data-driven decisions better than importance weights
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