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1 – 4 of 4While a growing body of literature reveals the prevalence of men's harassment and abuse of women online, scant research has been conducted into women's attacks on each other in…
Abstract
While a growing body of literature reveals the prevalence of men's harassment and abuse of women online, scant research has been conducted into women's attacks on each other in digital networked environments. This chapter responds to this research gap by analyzing data obtained from qualitative interviews with Australian women who have received at times extremely savage cyberhate they know or strongly suspect was sent by other women. Drawing on scholarly literature on historical intra-feminism schisms – specifically what have been dubbed the “mommy wars” and the “sex wars” – this chapter argues that the conceptual lenses of internalized misogyny and lateral violence are useful in their framing of internecine conflict within marginalized groups as diagnostic of broader, systemic oppression rather than being solely the fault of individual actors. These lenses, however, require multiple caveats and have many limitations. In conclusion, I canvas the possibility that the pressure women may feel to present a united front in the interests of feminist politics could itself be considered an outcome of patriarchal oppression (even if performing solidarity is politically expedient and/or essential). As such, there might come a time when openly renouncing discourses of sisterhood and feeling free to disagree with, and even dislike, other women might be considered markers of liberation.
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Andrea Ceschi, Marco Perini, Andrea Scalco, Monica Pentassuglia, Elisa Righetti and Beniamino Caputo
This study aims to provide an overview of the past two decades of lifelong learning (LLL) policies for enhancing employability and reduce social exclusion in young people of…
Abstract
Purpose
This study aims to provide an overview of the past two decades of lifelong learning (LLL) policies for enhancing employability and reduce social exclusion in young people of European countries through the development of the so-called LLL key-competences.
Design/methodology/approach
Built on a quasi-systematic review, this contribution explores traditional and new methods for promoting the LLL transition, and then employability, in young adults (e.g. apprenticeship, vocational training, e-learning, etc.).
Findings
It argues the need to identify all the possible approaches able to support policymakers, as they can differently impact key-competence development.
Originality/value
Finally, based on the consolidated EU policy experience, we propose a strategy of implementation of the LLL programmes that facilitates the institutions’ decision processes for policy-making through the use of decisional support system.
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Othmar Manfred Lehner, Kim Ittonen, Hanna Silvola, Eva Ström and Alena Wührleitner
This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's…
Abstract
Purpose
This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.
Design/methodology/approach
This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.
Findings
In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.
Originality/value
This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.
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Heimo Losbichler and Othmar M. Lehner
Looking at the limits of artificial intelligence (AI) and controlling based on complexity and system-theoretical deliberations, the authors aimed to derive a future outlook of the…
Abstract
Purpose
Looking at the limits of artificial intelligence (AI) and controlling based on complexity and system-theoretical deliberations, the authors aimed to derive a future outlook of the possible applications and provide insights into a future complementary of human–machine information processing. Derived from these examples, the authors propose a research agenda in five areas to further the field.
Design/methodology/approach
This article is conceptual in its nature, yet a theoretically informed semi-systematic literature review from various disciplines together with empirically validated future research questions provides the background of the overall narration.
Findings
AI is found to be severely limited in its application to controlling and is discussed from the perspectives of complexity and cybernetics. A total of three such limits, namely the Bremermann limit, the problems with a partial detectability and controllability of complex systems and the inherent biases in the complementarity of human and machine information processing, are presented as salient and representative examples. The authors then go on and carefully illustrate how a human–machine collaboration could look like depending on the specifics of the task and the environment. With this, the authors propose different angles on future research that could revolutionise the application of AI in accounting leadership.
Research limitations/implications
Future research on the value promises of AI in controlling needs to take into account physical and computational effects and may embrace a complexity lens.
Practical implications
AI may have severe limits in its application for accounting and controlling because of the vast amount of information in complex systems.
Originality/value
The research agenda consists of five areas that are derived from the previous discussion. These areas are as follows: organisational transformation, human–machine collaboration, regulation, technological innovation and ethical considerations. For each of these areas, the research questions, potential theoretical underpinnings as well as methodological considerations are provided.
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