The recent surge in publications related to explainable artificial intelligence (XAI) has led to an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this reason, articles and reviews that present taxonomies of XAI methods seem to be a welcomed way to get an overview of the field. Building on this idea, there is currently a trend of producing such taxonomies, leading to several competing approaches to construct them. In this paper, we will review recent approaches to constructing taxonomies of XAI methods and discuss general challenges concerning them as well as their individual advantages and limitations. Our review is intended to help scholars be aware of challenges current taxonomies face. As we will argue, when charting the field of XAI, it may not be sufficient to rely on one of the approaches we found. To amend this problem, we will propose and discuss three possible solutions: a new taxonomy that incorporates the reviewed ones, a database of XAI methods, and a decision tree to help choose fitting methods.

/pdf/a-review-of-taxonomies-of-explainable-artificial-1e0fzb2z.pdf

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

The explainability paradox: Challenges for xAI in digital pathology

Existing and planned legislation stipulates various obligations to provide information about machine learning algorithms and their functioning, often interpreted as obligations to “explain”. Many researchers suggest using post-hoc explanation algorithms for this purpose. In this paper, we combine legal, philosophical and technical arguments to show that post-hoc explanation algorithms are unsuitable to achieve the law’s objectives. Indeed, most situations where explanations are requested are adversarial, meaning that the explanation provider and receiver have opposing interests and incentives, so that the provider might manipulate the explanation for her own ends. We show that this fundamental conflict cannot be resolved because of the high degree of ambiguity of post-hoc explanations in realistic application scenarios. As a consequence, post-hoc explanation algorithms are unsuitable to achieve the transparency objectives inherent to the legal norms. Instead, there is a need to more explicitly discuss the objectives underlying “explainability” obligations as these can often be better achieved through other mechanisms. There is an urgent need for a more open and honest discussion regarding the potential and limitations of post-hoc explanations in adversarial contexts, in particular in light of the current negotiations of the European Union’s draft Artificial Intelligence Act.

/pdf/post-hoc-explanations-fail-to-achieve-their-purpose-in-13dz7jr0.pdf

Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. 

pdf/explainable-artificial-intelligence-xai-2-0-a-manifesto-of-4n80iy3nzx.pdf

Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions

Abstract We argue that explainable artificial intelligence (XAI), specifically reason-giving XAI, often constitutes the most suitable way of ensuring that someone can properly be held responsible for decisions that are based on the outputs of artificial intelligent (AI) systems. We first show that, to close moral responsibility gaps (Matthias 2004), often a human in the loop is needed who is directly responsible for particular AI-supported decisions. Second, we appeal to the epistemic condition on moral responsibility to argue that, in order to be responsible for her decision, the human in the loop has to have an explanation available of the system’s recommendation. Reason explanations are especially well-suited to this end, and we examine whether—and how—it might be possible to make such explanations fit with AI systems. We support our claims by focusing on a case of disagreement between human in the loop and AI system. 

/pdf/from-responsibility-to-reason-giving-explainable-artificial-2czccwhw.pdf

From Responsibility to Reason-Giving Explainable Artificial Intelligence

How Do We Assess the Trustworthiness of AI? Introducing the Trustworthiness Assessment Model (TrAM)

Recently, requirements for the explainability of software systems have gained prominence. One of the primary motivators for such requirements is that explainability is expected to facilitate stakeholders' trust in a system. Although this seems intuitively appealing, recent psychological studies indicate that explanations do not necessarily facilitate trust. Thus, explainability requirements might not be suitable for promoting trust. 
One way to accommodate this finding is, we suggest, to focus on trustworthiness instead of trust. While these two may come apart, we ideally want both: a trustworthy system and the stakeholder's trust. In this paper, we argue that even though trustworthiness does not automatically lead to trust, there are several reasons to engineer primarily for trustworthiness -- and that a system's explainability can crucially contribute to its trustworthiness.

On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness.

https://link.springer.com/content/pdf/10.1007/s10670-023-00714-2.pdf

Review of: Knauff, M., & Spohn, W. (Eds.), 2021, The Handbook of Rationality, MIT Press

The European AI Act is a new, legally binding instrument that will enforce certain requirements on the development and use of AI technology potentially affecting people in Europe. It can be expected that the stipulations of the Act, in turn, are going to affect the work of many software engineers, software testers, data engineers, and other professionals across the IT sector in Europe and beyond. The 113 articles, 180 recitals, and 13 annexes that make up the Act cover 144 pages. This paper aims at providing an aid for navigating the Act from the perspective of some professional in the software domain, termed"the working programmer", who feels the need to know about the stipulations of the Act.

AI Act for the Working Programmer

Recently, requirements for the explainability of software systems have gained prominence. One of the primary motivators for such requirements is that explainability is expected to facilitate stakeholders’ trust in a system. Although this seems intuitively appealing, recent psychological studies indicate that explanations do not necessarily facilitate trust. Thus, explainability requirements might not be suitable for promoting trust.One way to accommodate this finding is, we suggest, to focus on trustworthiness instead of trust. While these two may come apart, we ideally want both: a trustworthy system and the stakeholder’s trust. In this paper, we argue that even though trustworthiness does not automatically lead to trust, there are several reasons to engineer primarily for trustworthiness – and that a system’s explainability can crucially contribute to its trustworthiness.

On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Sarah Sterz

Author Tools

Chat about Author