Abstract In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intelligence (XAI). With the amount of XAI methods vastly growing, a taxonomy of methods is needed by researchers as well as practitioners: To grasp the breadth of the topic, compare methods, and to select the right XAI method based on traits required by a specific use-case context. Many taxonomies for XAI methods of varying level of detail and depth can be found in the literature. While they often have a different focus, they also exhibit many points of overlap. This paper unifies these efforts and provides a complete taxonomy of XAI methods with respect to notions present in the current state of research. In a structured literature analysis and meta-study, we identified and reviewed more than 50 of the most cited and current surveys on XAI methods, metrics, and method traits. After summarizing them in a survey of surveys, we merge terminologies and concepts of the articles into a unified structured taxonomy. Single concepts therein are illustrated by more than 50 diverse selected example methods in total, which we categorize accordingly. The taxonomy may serve both beginners, researchers, and practitioners as a reference and wide-ranging overview of XAI method traits and aspects. Hence, it provides foundations for targeted, use-case-oriented, and context-sensitive future research. 

https://link.springer.com/content/pdf/10.1007/s10618-022-00867-8.pdf

A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts

In domains such as medical and healthcare, the interpretability and explainability of machine learning and artificial intelligence systems are crucial for building trust in their results. Errors caused by these systems, such as incorrect diagnoses or treatments, can have severe and even life-threatening consequences for patients. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a popular area of research, focused on understanding the black-box nature of complex and hard-to-interpret machine learning models. While humans can increase the accuracy of these models through technical expertise, understanding how these models actually function during training can be difficult or even impossible. XAI algorithms such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) can provide explanations for these models, improving trust in their predictions by providing feature importance and increasing confidence in the systems. Many articles have been published that propose solutions to medical problems by using machine learning models alongside XAI algorithms to provide interpretability and explainability. In our study, we identified 454 articles published from 2018-2022 and analyzed 93 of them to explore the use of these techniques in the medical domain. 

The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review.

We present cut-free deductive systems without labels for the intuitionistic variants of the modal logics obtained by extending IK with a subset of the axioms d, t, b, 4, and 5. For this, we use the formalism of nested sequents, which allows us to give a uniform cut elimination argument for all 15 logic in the intuitionistic S5 cube.

/pdf/cut-elimination-in-nested-sequents-for-intuitionistic-modal-1wvorkvw3x.pdf

Cut elimination in nested sequents for intuitionistic modal logics

Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model. However, explanations can go beyond this one way communication as a mechanism to elicit user control, because once users understand, they can then provide feedback. The goal of this paper is to present an overview of research where explanations are combined with interactive capabilities as a mean to learn new models from scratch and to edit and debug existing ones. To this end, we draw a conceptual map of the state-of-the-art, grouping relevant approaches based on their intended purpose and on how they structure the interaction, highlighting similarities and differences between them. We also discuss open research issues and outline possible directions forward, with the hope of spurring further research on this blooming research topic.

/pdf/leveraging-explanations-in-interactive-machine-learning-an-1tb46w2e.pdf

Leveraging explanations in interactive machine learning: An overview

We present deductive systems for various modal logics that can be obtained from the constructive variant of the normal modal logic CK by adding combinations of the axioms d, t, b, 4, and 5. This includes the constructive variants of the standard modal logics K4, S4, and S5. We use for our presentation the formalism of nested sequents and give a syntactic proof of cut elimination.

pdf/on-nested-sequents-for-constructive-modal-logics-stvr5oubje.pdf

On Nested Sequents for Constructive Modal Logics

This work explores some aspects of a new and natural semantical dimension that can be accommodated within the syntax of description logics which opens up when passing from the classical truth-value interpretation to a constructive interpretation. We argue that such a strengthened interpretation is essential to represent applications with partial information adequately and to achieve consistency under abstraction as well as robustness under refinement. We introduce a constructive version of $\mathcal{ALC}$ , called ${c\mathcal{ALC}}$ , for which we give a sound and complete Hilbert axiomatisation and a Gentzen tableau calculus showing finite model property and decidability.

Towards Constructive DL for Abstraction and Refinement

/pdf/towards-constructive-dl-for-abstraction-and-refinement-18aj1unf3f.pdf

Towards Constructive DL for Abstraction and Refinement.

Explaining Image Classifications with Near Misses, Near Hits and Prototypes - Supporting Domain Experts in Understanding Decision Boundaries

In the last years, XAI research has mainly been concerned with developing new technical approaches to explain deep learning models. Just recent research has started to acknowledge the need to tailor explanations to different contexts and requirements of stakeholders. Explanations must not only suit developers of models, but also domain experts as well as end users. Thus, in order to satisfy different stakeholders, explanation methods need to be combined. While multi-modal explanations have been used to make model predictions more transparent, less research has focused on treating explanation as a process, where users can ask for information according to the level of understanding gained at a certain point in time. Consequently, an opportunity to explore explanations on different levels of abstraction should be provided besides multi-modal explanations. We present a process-based approach that combines multi-level and multi-modal explanations. The user can ask for textual explanations or visualizations through conversational interaction in a drill-down manner. We use Inductive Logic Programming, an interpretable machine learning approach, to learn a comprehensible model. Further, we present an algorithm that creates an explanatory tree for each example for which a classifier decision is to be explained. The explanatory tree can be navigated by the user to get answers of different levels of detail. We provide a proof-of-concept implementation for concepts induced from a semantic net about living beings.

Explanation as a Process: User-Centric Construction of Multi-level and Multi-modal Explanations.

Traditional imperative synchronous programming languages heavily rely on a strict separation between data memory and communication signals. Signals can be shared between computational units but cannot be overwritten within a synchronous reaction cycle. Memory can be destructively updated but cannot be shared between concurrent threads. This incoherence makes traditional imperative synchronous languages cumbersome for the programmer. The recent definition of sequentially constructive synchronous languages offers an improvement. It removes the separation between data memory and communication signals and unifies both through the notion of clock synchronised shared memory . However, it still depends on global causality analyses which precludes procedural abstraction. This complicates reuse and composition of software components. This paper shows how procedural abstraction can be accommodated inside the sequentially constructive model of computation. We present the Sequentially Constructive Procedural Language (SCPL) and its semantic theory of policy-constructive synchronous processes. SCPL supports procedural abstractions using policy interfaces to ensure that procedure calls are memory safe, wait-free and their scheduling is determinate and causal. At the same time, a policy interface constrains the level of freedom for the implementation and subsequent refactoring of a procedure. As a result, policies enable separate compilation and composition of procedures. We present our extensions abstractly as a formal semantics for SCPL and motivate it concretely in the context of the open-source, embedded, real-time language Blech.

Synchronized Shared Memory and Procedural Abstraction: Towards a Formal Semantics of Blech

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