A User-Centric Approach to Explainable AI in Corporate Performance Management

Machine learning (ML) applications have surged in popularity in the industry, however, the lack of transparency of ML-models often impedes the usability of ML in practice. Especially in the corporate performance management (CPM) domain, transparency is crucial to support corporate decision-making processes. To address this challenge, approaches of explainable artificial intelligence (XAI) provide solutions to reduce the opacity of ML-based systems. This design science study further builds on prior user experience (UX) and user interface (UI) focused XAI-research, to develop a user-centric approach to XAI for the CPM field. As key results, we identify design principles in three decomposition layers, including ten explainability UI-elements that we developed and evaluated through seven interviews. These results complement prior research by focusing it on the CPM domain and provide practitioners with concrete guidelines to foster ML adoption in the CPM field

How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing

Machine Learning (ML) technologies have become the foundation of a plethora of products and services. While the economic potential of such ML-infused solutions has become irrefutable, there is still uncertainty on pricing. Currently, software testing is one area to benefit from ML services assisting in the creation of test cases; a task both complex and demanding human-like outputs. Yet, little is known on the willingness to pay of users, inhibiting the suppliers\u27 incentive to develop suitable tools. To provide insights into desired features and willingness to pay for such ML-based tools, we perform a choice-based conjoint analysis with 119 participants in Germany. Our results show that a high level of accuracy is particularly important for users, followed by ease of use and integration into existing environments. Thus, we not only guide future developers on which attributes to prioritize but also which characteristics of ML-based services are relevant for future research

An Ambidextrous Perspective on Machine Learning Development and Operation: The Nexus of Organizational Structure, Tensions, and Tactics

Organizations from all industries have recently begun to develop and operate machine learning (ML) systems. While ML promises to improve an organization\u27s effectiveness and efficiency, developing and operating ML systems remains challenging as these systems differ significantly from traditional software and require novel work practices that run counter to existing business processes. These conflicting demands create tension in the organization as resources to develop and operate ML systems are limited. Organizations thus seek to leverage scarce resources by employing a range of organizational structures and tailored tactics. To explore the interplay of organizational structures, tensions, and tactics, we conducted an explorative expert interview study informed by computational grounded theory methodology. We took an ambidextrous perspective to identify four central tensions and associated tactics employed within given organizational structures. Further, we found that organizations are moving from centralized and decentralized structures to hybrid ones to enable effective ML development and operation

Machine Learning Developments as Stimuli for Organizational Learning

Organizational learning is a fundamental process that defines organizational behavior and thereby strongly influences organizational performance. As organizations increasingly adopt machine learning (ML) systems in their routines, the need to illuminate the impact of learning machines on organizational learning processes becomes increasingly urgent. In particular, due to their highly interdisciplinary and collaborative nature, ML developments—as organizations’ activities aimed at creating productively usable ML systems—may hereby represent an important but not yet well understood mechanism for fostering organizational learning. To explore how ML developments affect organizational learning processes, we interviewed 42 experts who are frequently involved in ML developments. Our findings suggest that ML developments can enhance organizational learning by stimulating a variety of organizational learning processes that generate a wealth of explicit and tacit knowledge in organizations

What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core

Artificial intelligence, specifically machine learning (ML), technologies are powerfully driving business model innovation in organizations against the backdrop of increasing digitalization. The resulting novel business models are profoundly shaped by ML, a technology that brings about unique opportunities and challenges. However, to date, little research examines what exactly constitutes these business models that use ML at their core and how they can be distinguished. Therefore, this study aims to contribute to an increased understanding of the anatomy of ML-driven business models in the business-to-business segment. To this end, we develop a taxonomy that allows researchers and practitioners to differentiate these ML-driven business models according to their characteristics along ten dimensions. Additionally, we derive archetypes of ML-driven business models through a cluster analysis based on the characteristics of 102 start-ups from the database Crunchbase. Our results are cross-industry, providing fertile soil for expansion through future investigations

Designing the Organizational Metaverse for Effective Socialization

The metaverse is a virtual world that merges physical, virtual, and augmented reality, enabling collaboration between online users and offering limitless opportunities for connectivity and integration. While the metaverse has gained significant attention in organizations, it presents social challenges as organizations have unprecedented insight and influence over individuals\u27 thoughts and beliefs. Our review is based on a theoretical framework and examines the impact of the environment, collaboration, avatars, and individual behavior on organizational socialization. We develop a conceptual model for the socialization process in the metaverse, contributing to a deep understanding of this emerging field and providing a research agenda for future work

METABOLIC AND HORMONAL RESPONSE TO ACUTE MYOCARDIAL INFARCTION

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/22335/1/0000780.pd

Recent advances in classical electromagnetic theory

The early Sections of the present Thesis utilise a metric-free and connection-free approach so as derive the foundations of classical electrodynamics. More specifically, following a tradition established by Kottler [65], Cartan [14] and van Dantzig [137], Maxwell's theory is introduced without making reference to a notion of distance or parallel transport. With very few exceptions, the relevant concepts are derived from first principles. Indeed, Maxwell's theory is constructed starting from three experimentally justified axioms: (i) electric charge is conserved, (ii) the force acting on a test charge due to the electromagnetic field is the standard Lorentz one, (iii) magnetic flux is conserved. To be precise, a strictly deductive approach requires that three further postulates are introduced, as explained in the manual [41] by Hehl and Obukhov. Nevertheless, a shortened formalism is observed to be adequate for the purpose of this work. In nearly all cases, the electromagnetic medium is demanded to be local and linear. Moreover, the propagation of light is studied in the approximate geometrical optics regime. Lindell's astute derivation of the dispersion equation [80] is reformulated in the widespread mathematical language of tensor indices. The method devised in Ref. [80] is integrated with the analysis due to Dahl [16] of the space encompassing the physically viable polarisations. As a result, the geometry associated with the dispersion equation is investigated with considerable rigour. From the literature it is known that, to a great extent, the notion of distance can be viewed as a by-product of Maxwell's theory. In fact, imposing that the constitutive law is electric-magnetic reciprocal and skewon-free determines, albeit non-uniquely, a Lorentzian metric. A novel proof of this statement is examined. In addition, the unimodular forerunner of electric-magnetic reciprocity, defined in earlier works by Lindell [79] and Perlick [112], is shown to preserve the energy-momentum tensor.Open Acces

Describing complex interactions of social-ecological systems for tipping point assessments: an analytical framework

Humans play an interconnecting role in social-ecological systems (SES), they are part of these systems and act as agents of their destruction and regulation. This study aims to provide an analytical framework, which combines the concept of SES with the concept of tipping dynamics. As a result, we propose an analytical framework describing relevant dynamics and feedbacks within SES based on two matrixes: the “tipping matrix” and the “cross-impact matrix.” We take the Southwestern Amazon as an example for tropical regions at large and apply the proposed analytical framework to identify key underlying sub-systems within the study region: the soil ecosystem, the household livelihood system, the regional social system, and the regional climate system, which are interconnected through a network of feedbacks. We consider these sub-systems as tipping elements (TE), which when put under stress, can cross a tipping point (TP), resulting in a qualitative and potentially irreversible change of the respective TE. By systematically assessing linkages and feedbacks within and between TEs, our proposed analytical framework can provide an entry point for empirically assessing tipping point dynamics such as “tipping cascades,” which means that the crossing of a TP in one TE may force the tipping of another TE. Policy implications: The proposed joint description of the structure and dynamics within and across SES in respect to characteristics of tipping point dynamics promotes a better understanding of human-nature interactions and critical linkages within regional SES that may be used for effectively informing and directing empirical tipping point assessments, monitoring or intervention purposes. Thereby, the framework can inform policy-making for enhancing the resilience of regional SES

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie