Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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The transition from adolescence to adulthood also has been described as a window of opportunity or vulnerability when developmental and contextual changes converge to support positive turnarounds and redirections (Masten, Long, Kuo, McCormick, & Desjardins, 2009; Masten, Obradović, & Burt, 2006). The transition years also are a criminological crossroads, as major changes in criminal careers often occur at these ages as well. For some who began their criminal careers during adolescence, offending continues and escalates; for others involvement in crime wanes; and yet others only begin serious involvement in crime at these ages. There are distinctive patterns of offending that emerge during the transition from adolescence to adulthood. One shows a rise of offending in adolescence and the persistence of high crime rates into adulthood; a second reflects the overall age-crime curve pattern of increasing offending in adolescence followed by decreases during the transition years; and the third group shows a late onset of offending relative to the age-crime curve. Developmental theories of offending ought to be able to explain these markedly different trajectories
A musical improvisation inspired by a beautifulsummer day or by a song by Elvis; for patientsadmitted in hospital for an operation, music canhave healing powers. With the research projectMeaningful Music in Health Care (MiMiC), thattook place from autumn 2015 until 2018, the researchgroup Lifelong Learning in Music (LLM), togetherwith the department of surgery of the UniversityMedical Center Groningen (UMCG), researched thepractice of live music for hospital patients and theirhealth care professionals. For the research groupLifelong Learning in Music the focus of the researchwas on the meaning of this musical practice formusicians and health care professionals, and onthe development of this practice.The research of UMCG concentrated on the effectsof live music on the recovery and wellbeing of patients
