The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a means to ensure that a neural network based classifier bases its predictions solely on human understandable concepts. The concept labels, or rationales as we refer to them, are learned by the concept labeling component of the CBM. Another component learns to predict the target classification label from these predicted concept labels. Unfortunately, these models are heavily reliant on human provided concept labels for each datapoint. To enable CBMs to behave robustly when these labels are not readily available, we show how to equip them with the ability to abstain from predicting concepts when the concept labeling component is uncertain. In other words, our model learns to provide rationales for its predictions, but only whenever it is sure the rationale is correct.

We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called subscales. LAMs can augment any additive binary classification model equipped with a sigmoid link function. Moreover, they afford direct global and local attributions of additive components to the model output in probability space. We argue that LAMs and SubscaleHedge improve the interpretability of their base algorithms. Using rigorous null-hypothesis significance testing on a broad suite of financial modelling data, we show that our algorithms do not suffer from large performance penalties in terms of ROC-AUC and calibration.

Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. Then a downstream model uses these predicted concept labels to predict the target label. The predicted concepts act as a rationale for the target prediction. Model trust issues emerge in this paradigm when soft concept labels are used: it has previously been observed that extra information about the data distribution leaks into the concept predictions. In this work we show how Monte-Carlo Dropout can be used to attain soft concept predictions that do not contain leaked information.

反事实解释为从机器学习模型中获得预期结果的方法提供了信息。但是，这种解释对基础模型的某些现实世界变化（例如，重新训练模型，更改的超参数等）并不强大，质疑其在多种应用程序中的可靠性，例如信用贷款。在这项工作中，我们提出了一种新颖的策略 - 我们称之为Robx，以生成基于树的合奏，例如XGBoost的强大反事实。基于树的合奏在强大的反事实生成中提出了其他挑战，例如，它们具有非平滑和非差异的目标函数，并且在非常相似的数据上，它们可以在RETOR下的参数空间中进行很多更改。我们首先引入了一种新颖的指标（我们称之为反事实稳定性），该指标试图量化反事实的鲁棒性将是为了模拟重新训练下的变化，并具有理想的理论属性。我们提出的策略ROBX使用任何反事实生成方法（基本方法），并通过使用我们的度量反事实稳定性迭代地完善基本方法生成的反事实来搜索强大的反事实。我们将ROBX的性能与基于基准数据集的流行反事实生成方法（对于基于树的合奏）进行了比较。结果表明，我们的策略会产生反事实，这些反事实是强大的（实际模型更改后的有效性近100％），并且在现有最新方法上也是现实的（就局部异常因素而言）。

There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models. Arguably, the most popular among them are local explanations that focus on investigating model behaviour for individual instances. Several methods have been proposed for local analysis, but relatively lesser effort has gone into understanding if the explanations are robust and accurately reflect the behaviour of underlying models. In this work, we present a survey of the works that analysed the robustness of two classes of local explanations (feature importance and counterfactual explanations) that are popularly used in analysing AI/ML models in finance. The survey aims to unify existing definitions of robustness, introduces a taxonomy to classify different robustness approaches, and discusses some interesting results. Finally, the survey introduces some pointers about extending current robustness analysis approaches so as to identify reliable explainability methods.

特征属性是用于模型解释的常见范例，因为它们在为模型分配每个输入特征的单个数字分数时是简单的。在可操作的追索范围中，其中解释的目标是改善模型消费者的结果，通常不清楚应该如何正确使用特征归因。通过这项工作，我们的目标是加强和澄清可操作追索和特征归因之间的联系。具体地，我们提出了一种Shap，CoShap的变种，它使用反事实生成技术来生产背景数据集以便在边缘（A.K.a.介入）福利价值框架内使用。我们在使用朔芙值的特征归属时仔细考虑的可动手追索程序设置中的需求，同时涉及单调的要求，具有许多合成示例。此外，我们通过提出和证明要素归属，反事实能力的定量评分来展示COSHAP的功效，表明如通过该指标测量，Coshap优于使用单调树集合在公共数据集上进行评估时的现有方法。

Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data. Previous studies augmented PLMs with symbolic knowledge for different downstream NLP tasks. However, knowledge bases (KBs) utilized in these studies are usually large-scale and static, in contrast to small, domain-specific, and modifiable knowledge bases that are prominent in real-world task-oriented dialogue (TOD) systems. In this paper, we showcase the advantages of injecting domain-specific knowledge prior to fine-tuning on TOD tasks. To this end, we utilize light-weight adapters that can be easily integrated with PLMs and serve as a repository for facts learned from different KBs. To measure the efficacy of proposed knowledge injection methods, we introduce Knowledge Probing using Response Selection (KPRS) -- a probe designed specifically for TOD models. Experiments on KPRS and the response generation task show improvements of knowledge injection with adapters over strong baselines.

The current trend of applying transfer learning from CNNs trained on large datasets can be an overkill when the target application is a custom and delimited problem with enough data to train a network from scratch. On the other hand, the training of custom and lighter CNNs requires expertise, in the from-scratch case, and or high-end resources, as in the case of hardware-aware neural architecture search (HW NAS), limiting access to the technology by non-habitual NN developers. For this reason, we present Colab NAS, an affordable HW NAS technique for producing lightweight task-specific CNNs. Its novel derivative-free search strategy, inspired by Occam's razor, allows it to obtain state-of-the-art results on the Visual Wake Word dataset in just 4.5 GPU hours using free online GPU services such as Google Colaboratory and Kaggle Kernel.

In this paper we propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics. The approach allows for conditional reasoning over arguments and boolean combination of arguments, with respect to a class of gradual semantics, through the verification of graded (strict or defeasible) implications over a preferential interpretation. As a proof of concept, in the finitely-valued case, an Answer set Programming approach is proposed for conditional reasoning in a many-valued argumentation semantics of weighted argumentation graphs. The paper also develops and discusses a probabilistic semantics for gradual argumentation, which builds on the many-valued conditional semantics.

Weather forecasting centers currently rely on statistical postprocessing methods to minimize forecast error. This improves skill but can lead to predictions that violate physical principles or disregard dependencies between variables, which can be problematic for downstream applications and for the trustworthiness of postprocessing models, especially when they are based on new machine learning approaches. Building on recent advances in physics-informed machine learning, we propose to achieve physical consistency in deep learning-based postprocessing models by integrating meteorological expertise in the form of analytic equations. Applied to the post-processing of surface weather in Switzerland, we find that constraining a neural network to enforce thermodynamic state equations yields physically-consistent predictions of temperature and humidity without compromising performance. Our approach is especially advantageous when data is scarce, and our findings suggest that incorporating domain expertise into postprocessing models allows to optimize weather forecast information while satisfying application-specific requirements.