5,466 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Ethical Questions Raised by AI-Supported Mentoring in Higher Education

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    Mentoring is a highly personal and individual process, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. The emerging use of AI in mentoring processes in higher education not only necessitates the adherence to applicable laws and regulations (e.g., relating to data protection and nondiscrimination) but further requires a thorough understanding of ethical norms, guidelines, and unresolved issues (e.g., integrity of data, safety, and security of systems, and confidentiality, avoiding bias, insuring trust in and transparency of algorithms). Mentoring in Higher Education requires one of the highest degrees of trust, openness, and social–emotional support, as much is at the stake for mentees, especially their academic attainment, career options, and future life choices. However, ethical compromises seem to be common when digital systems are introduced, and the underlying ethical questions in AI-supported mentoring are still insufficiently addressed in research, development, and application. One of the challenges is to strive for privacy and data economy on the one hand, while Big Data is the prerequisite of AI-supported environments on the other hand. How can ethical norms and general guidelines of AIED be respected in complex digital mentoring processes? This article strives to start a discourse on the relevant ethical questions and in this way raise awareness for the ethical development and use of future data-driven, AI-supported mentoring environments in higher education

    The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures

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    Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation. Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents. In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI

    Decentralised finance's timocratic governance: The distribution and exercise of tokenised voting rights

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    Ethereum's public distributed ledger can issue tokenised voting rights that are tradable on crypto-asset exchanges by potentially anyone. Ethereum thus enables global, unincorporated associations to conduct governance experiments. Such experiments are crucial to Decentralised Finance (DeFi). DeFi is a nascent field of unlicensed, unregulated, and non-custodial financial services that utilise public distributed ledgers and crypto-assets rather than corporate structures and sovereign currencies. The inaugural Bloomberg Galaxy DeFi Index, launched in August 2021, included nine Ethereum-based projects – non-custodial exchanges as well as lending and derivatives platforms. Each project is governed, at least in part, by unregistered holders of tokenised voting rights (also known as governance tokens). Token-holders typically vote for or against coders' improvement proposals that pertain to anything from the allocation of treasury funds to a collateral's risk parameters. DeFi's governance thus depends on the distribution and exercise of tokenised voting rights. Since archetypal DeFi projects are not managed by companies or public institutions, not much is known about DeFi's governance. Regulators and law-makers from the United States recently asked if DeFi's governance entails a new class of “shadowy” elites. In response, we conducted an exploratory, multiple-case study that focused on the tokenised voting rights issued by the nine projects from Bloomberg's inaugural Galaxy DeFi index. Our mixed methods approach drew on Ethereum-based data about the distribution, trading, staking, and delegation of voting rights tokens, as well as project documentation and archival records. We discovered that DeFi projects' voting rights are highly concentrated, and the exercise of these rights is very low. Our theoretical contribution is a philosophical intervention: minority rule, not “democracy”, is the probable outcome of token-tradable voting rights and a lack of applicable anti-concentration laws. We interpret DeFi's minority rule as timocratic

    Financial and Economic Review 22.

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    Grenzen auflösen – Grenzen ziehen. Grenzbearbeitungen zwischen Erziehungswissenschaft, Politik und Gesellschaft

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    Die Grenzen zwischen Erziehungswissenschaft, Politik und Gesellschaft verlaufen fließend und werden immer wieder neu hergestellt. Diesem Thema widmet sich der vorliegende Band mit Beiträgen der Jahrestagung der Sektion Interkulturelle und International Vergleichende Erziehungswissenschaft (SIIVE) 2021 in der Deutschen Gesellschaft für Erziehungswissenschaft (DGfE). Es werden theoretische und empirische Perspektiven auf Grenzbearbeitungen eröffnet, Bestandsaufnahmen von Grenzbearbeitungen vorgenommen, methodisch-methodologische Herausforderungen in den Mittelpunkt gerückt und Grenzen bearbeitet. (DIPF/Verlag

    Toward a Minor Tech: A Peer-reviewed Newspaper, Volume 12, Issue 1, 2023

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    Following a process of open exchanges and a three-day research workshop in London, at London South Bank University and King’s College, London, the publication brings together researchers who address the problems of technological scale, thinking through the potentials of ‘the minor’; or what we are referring to as minor (or minority) tech. As such, the publication sets out to question the universal ideals of technology and its problems of scale, extending it to follow the three main characteristics identified in Deleuze and Guattari’s essay (Toward a Minor Literature), namely deterritorialization, political immediacy, and collective value.  Contributions by Christian Ulrik Andersen, Geoff Cox, Camille Crichlow, Mateus Domingos, Feminist Servers (mara karagianni & nate wessalowski), Teodora Sinziana Fartan, Susanne Förster, Inte Gloerich, Daniel Chávez Heras, Macon Holt, Jung-Ah Kim, Edoardo Lomi, Inga Luchs, Gabriel Menotti, Alasdair Milne, Anna Mladentseva, Shusha Niederberger, Søren Bro Pold, Roel Roscam Abbing, Winnie Soon, Magdalena Tyżlik-Carver, Varia, Jack Wilson, xenodata co-operative (Yasemin Keskintepe & Alexandra Anikina), Sandy Di Yu, Freja Kir. Design & Production: Manetta Berends and Simon Browne (Varia)

    Bio-inspired optimization in integrated river basin management

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    Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms
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