Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work

The increasing prevalence of Artificial Intelligence (AI) in safety-critical contexts such as air-traffic control leads to systems that are practical and efficient, and to some extent explainable to humans to be trusted and accepted. The present structured literature analysis examines n = 236 articles on the requirements for the explainability and acceptance of AI. Results include a comprehensive review of n = 48 articles on information people need to perceive an AI as explainable, the information needed to accept an AI, and representation and interaction methods promoting trust in an AI. Results indicate that the two main groups of users are developers who require information about the internal operations of the model and end users who require information about AI results or behavior. Users' information needs vary in specificity, complexity, and urgency and must consider context, domain knowledge, and the user's cognitive resources. The acceptance of AI systems depends on information about the system's functions and performance, privacy and ethical considerations, as well as goal-supporting information tailored to individual preferences and information to establish trust in the system. Information about the system's limitations and potential failures can increase acceptance and trust. Trusted interaction methods are human-like, including natural language, speech, text, and visual representations such as graphs, charts, and animations. Our results have significant implications for future human-centric AI systems being developed. Thus, they are suitable as input for further application-specific investigations of user needs

Timbral effects on consonance disentangle psychoacoustic mechanisms and suggest perceptual origins for musical scales

Acknowledgements: FD was supported by an Erasmus+ scholarship.Funder: This work was funded by the Max Planck Institute for Empirical Aesthetics.The phenomenon of musical consonance is an essential feature in diverse musical styles. The traditional belief, supported by centuries of Western music theory and psychological studies, is that consonance derives from simple (harmonic) frequency ratios between tones and is insensitive to timbre. Here we show through five large-scale behavioral studies, comprising 235,440 human judgments from US and South Korean populations, that harmonic consonance preferences can be reshaped by timbral manipulations, even as far as to induce preferences for inharmonic intervals. We show how such effects may suggest perceptual origins for diverse scale systems ranging from the gamelan’s slendro scale to the tuning of Western mean-tone and equal-tempered scales. Through computational modeling we show that these timbral manipulations dissociate competing psychoacoustic mechanisms underlying consonance, and we derive an updated computational model combining liking of harmonicity, disliking of fast beats (roughness), and liking of slow beats. Altogether, this work showcases how large-scale behavioral experiments can inform classical questions in auditory perception

Timbral effects on consonance disentangle psychoacoustic mechanisms and suggest perceptual origins for musical scales

The phenomenon of musical consonance is an essential feature in diverse musical styles. The traditional belief, supported by centuries of Western music theory and psychological studies, is that consonance derives from simple (harmonic) frequency ratios between tones and is insensitive to timbre. Here we show through five large-scale behavioral studies, comprising 235,440 human judgments from US and South Korean populations, that harmonic consonance preferences can be reshaped by timbral manipulations, even as far as to induce preferences for inharmonic intervals. We show how such effects may suggest perceptual origins for diverse scale systems ranging from the gamelan’s slendro scale to the tuning of Western mean-tone and equal-tempered scales. Through computational modeling we show that these timbral manipulations dissociate competing psychoacoustic mechanisms underlying consonance, and we derive an updated computational model combining liking of harmonicity, disliking of fast beats (roughness), and liking of slow beats. Altogether, this work showcases how large-scale behavioral experiments can inform classical questions in auditory perception

Timbral effects on consonance disentangle psychoacoustic mechanisms and suggest perceptual origins for musical scales

Abstract The phenomenon of musical consonance is an essential feature in diverse musical styles. The traditional belief, supported by centuries of Western music theory and psychological studies, is that consonance derives from simple (harmonic) frequency ratios between tones and is insensitive to timbre. Here we show through five large-scale behavioral studies, comprising 235,440 human judgments from US and South Korean populations, that harmonic consonance preferences can be reshaped by timbral manipulations, even as far as to induce preferences for inharmonic intervals. We show how such effects may suggest perceptual origins for diverse scale systems ranging from the gamelan’s slendro scale to the tuning of Western mean-tone and equal-tempered scales. Through computational modeling we show that these timbral manipulations dissociate competing psychoacoustic mechanisms underlying consonance, and we derive an updated computational model combining liking of harmonicity, disliking of fast beats (roughness), and liking of slow beats. Altogether, this work showcases how large-scale behavioral experiments can inform classical questions in auditory perception

Applying the Assessment List for Trustworthy Artificial Intelligence on the development of AI supported Air Traffic Controller Operations

The advance of AI in safety- and security-critical domains such as aviation needs high standards for its trustworthy development. In this context the EASA introduced the Assessment List for Trustworthy AI (ALTAI) as an helpful tool. This paper presents an approach for applying the ALTAI in the development of an AI-based digital Air Traffic Control Operator (ATCO). Specifically, the aim was using the ALTAI to derive a set of high-level requirements for the AI-based system to guarantee trustworthiness in the early development stages. The focus is thus given on how the ALTAI questions can be processed in order to yield system requirements. However, the necessity for a structured approach becomes apparent when confronted with the abundance of diverse perspectives within the ALTAI. Accordingly, various filtering, prioritization, and grouping methods were implemented in an usable framework. Consequently, the applicability of the ALTAI is analyzed, discovering a divergence between technical and ethical requirements. It is illustrated that technical questions often lead to highly applicable specific requirements, compared to ethical questions. Especially due to their importance, the challenges of deriving specific requirements for certain ethical aspects are emphasized and discussed. Additionally, suggestions on future versions of the ALTAI are given in order to strengthen its application during the development of AI-based systems. By showcasing our method and specific requirements obtained for the digital ATCO system, the objective is to highlight the necessity of the ALTAI and to provide a basis for its wider use