1,720,975 research outputs found
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Full text linkPrevious research in Explainable Artificial Intelligence (XAI) suggests that
a main aim of explainability approaches is to satisfy specific interests,
goals, expectations, needs, and demands regarding artificial systems (we call
these stakeholders' desiderata) in a variety of contexts. However, the
literature on XAI is vast, spreads out across multiple largely disconnected
disciplines, and it often remains unclear how explainability approaches are
supposed to achieve the goal of satisfying stakeholders' desiderata. This paper
discusses the main classes of stakeholders calling for explainability of
artificial systems and reviews their desiderata. We provide a model that
explicitly spells out the main concepts and relations necessary to consider and
investigate when evaluating, adjusting, choosing, and developing explainability
approaches that aim to satisfy stakeholders' desiderata. This model can serve
researchers from the variety of different disciplines involved in XAI as a
common ground. It emphasizes where there is interdisciplinary potential in the
evaluation and the development of explainability approaches
Spare me the details: How the type of information about automated interviews influences applicant reactions
Full text linkApplicants seem to react negatively to artificial intelligence-based automated systems in personnel selection. This study investigates the impact of different pieces of information to alleviate applicant reactions in an automated interview setting. In a 2 (no process information vs. process information) × 2 (no process justification vs. process justification) between-subjects design, participants (N = 124) received respective information and watched a video showing an automated interview. Testing mediation effects via different applicant reaction variables indicated that process justification is better than process information which can even impair applicant reactions. However, information did not increase organizational attractiveness compared to not receiving any information. This study sheds light on what type of information contributes to positive and negative applicant reactions to automated systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods
Full text linkThe 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
Building bridges for better machines : from machine ethics to machine explainability and back
Full text linkBe it nursing robots in Japan, self-driving buses in Germany or automated hiring systems in the USA, complex artificial computing systems have become an indispensable part of our everyday lives. Two major challenges arise from this development: machine ethics and machine explainability. Machine ethics deals with behavioral constraints on systems to ensure restricted, morally acceptable behavior; machine explainability affords the means to satisfactorily explain the actions and decisions of systems so that human users can understand these systems and, thus, be assured of their socially beneficial effects. Machine ethics and explainability prove to be particularly efficient only in symbiosis. In this context, this thesis will demonstrate how machine ethics requires machine explainability and how machine explainability includes machine ethics. We develop these two facets using examples from the scenarios above. Based on these examples, we argue for a specific view of machine ethics and suggest how it can be formalized in a theoretical framework. In terms of machine explainability, we will outline how our proposed framework, by using an argumentation-based approach for decision making, can provide a foundation for machine explanations. Beyond the framework, we will also clarify the notion of machine explainability as a research area, charting its diverse and often confusing literature. To this end, we will outline what, exactly, machine explainability research aims to accomplish. Finally, we will use all these considerations as a starting point for developing evaluation criteria for good explanations, such as comprehensibility, assessability, and fidelity. Evaluating our framework using these criteria shows that it is a promising approach and augurs to outperform many other explainability approaches that have been developed so far.DFG: CRC 248: Center for Perspicuous Computing; VolkswagenStiftung: Explainable Intelligent System
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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