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

Mechanism Deduction from Noisy Chemical Reaction Networks

We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semi-accurate but efficient electronic structure calculations. It is designed to (i) accelerate the automated exploration of such networks, and (ii) cope with model-inherent errors in electronic structure calculations on elementary reaction steps. We developed and implemented KiNetX to possess three features. First, KiNetX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism. Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies), which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy. The novelty of KiNetX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KiNetX, we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss assess the importance of rigorous uncertainty propagation in a statistical context. Our results reveal that KiNetX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semi-accurate electronic structure data.Comment: 36 pages, 4 figures, 2 table

On The Stability of Interpretable Models

Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model's construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models

Regulation of intercommunal financial flows with geostatistics and GIS

Concerning the characterisation or classification of municipalities a wide range of different approaches exists. Usually some historical, functional, and/or political indicators are used for such a classification. These indicators are usually structured simple, referring to inhabitant size, political importance, configuration of available infrastructure like hospitals and schools, places of work, or the like. However, an application field, where a quite specific, meaningful, and comprehensible classification for each municipality is of fundamental interest, is the local financial adjustment between communities on the same hierarchical administrative level. Which municipality delivers gladly more money than it is forced to do by law, or which community renounces voluntarily external support? Therefore, well elaborated indicators are needed to define the amount of money which has to be transferred, generally spoken, from rich communities to the poorer ones. However, it is obvious that a pure redistribution of revenues between financially strong and financially weak communities, which would lead in principle to a more or less equal financial configuration of the communities, is not sufficient for a fair system of financial adjustment. Such a redistribution system would not consider the different financial loads of the budgets of different types of communes. These varying financial loads for varying types of commues can be characterised by the following two concepts: 'costs of width' and 'costs of density'. The 'costs of width' are explained mainly by geographical reasons for peripheral and/or mountainous communities with low population density, which implies specific financial load for the particular community. By contrast, 'costs of density' are explained by disproportional high socio-demographic burdens and high costs of infrastructure in central and urban communities. Meaningful indicators for such a financially oriented classification of municipalities need detailed investigations, to be silent completely of that these indicators also need political acceptance, in the end. This paper presents a study carried out for the Canton of Zurich, Switzerland, which made part of the cantonal revision of the system of inter-communal financial adjustment. The aim was to provide means for a cantonal regulation on how the financial adjustment between the communities should be regulated. Therefore, socio-demographic and geographic indicators have been evaluated in order to find rules to reflect the financial load of the municipal budgets. The heuristically driven statistical modelling has been carried out using multiple regression. Besides the presentation of the technical approach, this paper discusses the analysed indicators in the perspective of regional policy and territorial justice.

Decision diagrams in machine learning: an empirical study on real-life credit-risk data.

Decision trees are a widely used knowledge representation in machine learning. However, one of their main drawbacks is the inherent replication of isomorphic subtrees, as a result of which the produced classifiers might become too large to be comprehensible by the human experts that have to validate them. Alternatively, decision diagrams, a generalization of decision trees taking on the form of a rooted, acyclic digraph instead of a tree, have occasionally been suggested as a potentially more compact representation. Their application in machine learning has nonetheless been criticized, because the theoretical size advantages of subgraph sharing did not always directly materialize in the relatively scarce reported experiments on real-world data. Therefore, in this paper, starting from a series of rule sets extracted from three real-life credit-scoring data sets, we will empirically assess to what extent decision diagrams are able to provide a compact visual description. Furthermore, we will investigate the practical impact of finding a good attribute ordering on the achieved size savings.Advantages; Classifiers; Credit scoring; Data; Decision; Decision diagrams; Decision trees; Empirical study; Knowledge; Learning; Real life; Representation; Size; Studies;