An interdisciplinary competence profile for AI in engineering

The use of Artificial Intelligence (AI) in engineering is on the rise and comes with the promise of cost reductions and efficiency gains. However, classical engineers often lack the necessary skills to implement data-driven solutions. At the same time, computer scientists lack the required understanding of engineering systems. Thus, we need to extend the current set of competencies of engineers across the boundaries of disciplines to include competencies of Artificial Intelligence as well as skills necessary for interdisciplinary work. In this paper, we propose a competence profile of a so-called AI Engineer that combines the expertise of AI systems in the context of engineering. Based on perspectives from literature and interviews with experts from industry and research, we highlight the most important set of competencies across the professional, methodological, social, and selfcompetencies. The contributions of our paper can act as a reference point for developing and advancing future engineering curricula. Furthermore, it serves as a guide for professional self-development

Toward an Objective Measurement of AI Literacy

Humans multitudinously interact with Artificial Intelligence (AI) as it permeates every aspect of contemporary professional and private life. The socio-technical competencies of humans, i.e., their AI literacy, shape human-AI interactions. While academia does explore AI literacy measurement, current literature exclusively approaches the topic from a subjective perspective. This study draws on a well-established scale development procedure employing ten expert interviews, two card-sorting rounds, and a between-subject comparison study with 88 participants in two groups to define, conceptualize, and empirically validate an objective measurement instrument for AI literacy. With 16 items, our developed instrument discriminates between an AI-literate test and a control group. Furthermore, the structure of our instrument allows us to distinctly assess AI literacy aspects. We contribute to IS education research by providing a new instrument and conceptualizing AI literacy, incorporating critical themes from the literature. Practitioners may employ our instrument to assess AI literacy in their organizations

Key AI Competences by 2035: A Taxonomy for Firms

Our research examines the transformative changes that AI systems already bring about and are projected to cause in the future. These transformations are often referred to as ‘a fourth industrial revolution’ (Schwab, 2016; cf. Brynjolfsson & McAfee, 2014; Raisch & Krakowski, 2021). For the purposes of this foresight exercise, we assume that AI is likely to be a ‘general-purpose technology’ (Brynjolfsson et al., 2019; cf. Lipsey et al., 2005), similar to technologies such as the steam engine, electrification, and computing. The overall research questions that this project aims to address are: What are the effects of AI on companies by 2035? Does the advent of AI necessitate changes in the organisational design of companies? What are the corresponding key competences that companies need? In this paper, we propose a taxonomy that addresses the last question: what are the key competences for firms on an organisational level to be prepared for AI systems

Characteristics of Contemporary Artificial Intelligence Technologies and Implications for IS Research

Artificial Intelligence (AI) is often presented as a new phenomenon that is primarily driven by advances in contemporary machine learning technologies. Despite the steep rise, conceptualizations of contemporary AI technologies tend to be vague in many studies. This is problematic not only for positioning and focusing such research, but also for theorizing on the pervasive AI phenomenon. This paper presents a systematic literature review to understand and synthesize distinctive characteristics of contemporary AI technologies. In the course of our ongoing research, the preliminary findings encompass the changing role of data, feature extraction, adaptivity, transparency, and biases. With our future research, we seek to provide guidance on the conceptualizations of AI in IS research and to facilitate a more nuanced and focused theorization of AI in future IS studies

Accountability in human and artificial decision-making as the basis for diversity and educational inclusion

Accountability is an important dimension of decision-making in Human and Artificial Intelligence (AI). We argue that it is of fundamental importance to inclusion, diversity and fairness of both the AI-based and human-controlled interactions and any human-facing interventions aiming to change human development, behaviour and learning. Less well debated, however, is the nature and the role of biases that emerge from theoretical or empirical models that underpin AI algorithms and the interventions driven by such algorithms. While, the biases emerging from the theoretical and empirical models also affect human-controlled educational systems and interventions (e.g. hindsight and unconscious biases), the key mitigating difference between AI and human decision-making is that human decisions involve individual flexibility, context-relevant judgements, empathy, as well as complex moral judgements, missing from AI. In this chapter, we argue that our fascination with AI, which pre-dates the current craze by centuries, resides in its ability to act as a ‘mirror’ reflecting our current understandings of human intelligence. Such understandings also inevitably encapsulate the biases emerging from our intellectual and empirical limitations. We make a case for the need for diversity to mitigate against biases becoming inbuilt into systems (in both Education and AI) and, with reference to specific examples of AI approaches and applications, we outline one compelling future for inclusive and accountable AI and Educational research and practice

Executive AI Literacy: A Text-Mining Approach to Understand Existing and Demanded AI Skills of Leaders in Unicorn Firms

Despite the growing relevance of artificial intelligence (AI) for busi-nesses, there is a lack of research on how top-level executives must be skilled in AI. Drawing on upper echelons theory, this paper explores executive AI literacy, defined as the combined AI skills of top-level executives, and its relevance for different executive roles. We conducted a text-mining analysis of 1,625 execu-tives’ online profiles and 1,033 executive job postings from unicorn firms re-trieved via web-scraping from an online professional social network. We find that AI skills are mostly required in product-related executive roles (vs. adminis-trative roles). Thus, we provide an AI-specific perspective complementing prior information systems research on executives, which asserts that (non-AI) IT is driven by administrative executive roles. Our paper contributes to AI literacy lit-erature by shedding light on the substance of executive AI literacy within firms. Lastly, we provide implications for AI-related information systems strategy

Ethical Marketing AI?A Structured Literature Review of the Ethical Challenges Posed by Artificial Intelligence in the Domains of Marketing and Consumer Behavior

With the increasing use of AI in marketing, ethical repercussions are beginning to emerge. From privacy issues, through discrimination of marginalized groups, to emergent systemic social distortions, AI is changing the marketing ethical landscape. In this paper we conduct a structured literature review of the emerging ethical issues posed by AI in the domains of marketing and consumer behavior. We identify three clusters of ethical issues (algorithm, society and existential) and map these to the marketing domains of systems, brands, and consumers. We conclude that the field of ethical marketing AI is still very much in its infancy, but such is the rate of development ethical marketing AI is likely to become important field for academics and practitioners alike

Dissection of AI Job Advertisements: A Text Mining-based Analysis of Employee Skills in the Disciplines Computer Vision and Natural Language Processing

Human capital is a well discussed topic in information system research. In order for companies to develop and use IT artifacts, they need specialized employees. This is especially the case when complex technologies, such as artificial intelligence, are used. Two major fields of artificial intelligence are computer vision (CV) and natural language processing (NLP). In this paper skills and know-how required for CV and NLP specialists are analyzed and compared from a job market perspective. For this purpose, we utilize a text mining-based analysis pipeline to dissect job advertisements for artificial intelligence. In concrete, job advertisements of both sub-disciplines were crawled from a large international online job platform and analyzed using named entity recognition and term vectors. It could be shown that know-how and skills required differ between the two job profiles. There is no general requirement profile of an artificial intelligence specialist, and it requires a differentiated consideration

Organizational, Sociological and Procedural Uncertainties in Statistical and Machine Learning: A Systematic Literature Review

Driven by the potential of digitalization, statistical learning and machine learning methods are commonly used for scheduling complex processes or forecasting in supply chain domains. However, trust in such methods is hampered by uncertainties in data quality, data exchange platforms, and data processing, affecting its traceability and reliability. Decision-relevant output provided by such methods is prone to trust issues in the data used for training, in the resulting model, and in the infrastructure in which the model is embedded. Considering the vulnerability of supply chains, wrong decisions have far-reaching consequences, raising the question of to what extent systems alone should be trusted for strategic, operational, and tactical decision-making. In this paper, we take a multidisciplinary perspective with the intention to analyze trust in statistical learning and machine learning methods from an organizational, sociological, and procedural perspective. The information base for this article is gathered through a systematic literature review. The central results of our research are a concept matrix comparing papers based on relevant criteria derived from literature and subsequent findings derived from this matrix. We encourage researchers in the fields of supply chain management, sociology, and statistics or machine learning to open up for interdisciplinary research and to build upon our findings