359 research outputs found

    "It's not a career": Platform work among young people aged 16-19

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    In the online gig economy, or platform work as it is sometimes known, work can be organised through websites and smartphone apps. People can drive for Uber or Deliveroo, sell items on eBay or Etsy, or rent their properties on Airbnb. This research examines the views of young people between the ages of 16 and 19 in the United Kingdom to see whether they knew about the online gig economy, whether they were using it already to earn money, and whether they expected to use it for their careers. It discovers careers professionals’ levels of knowledge, and their ability (and desire) to include the gig economy in their professional practice. This research contributes to discussions about what constitutes decent work, and whether it can be found within the online gig economy. The results point to ways in which careers practice could include platform work as a means of extending young people’s knowledge about alternative forms of work. This study also makes a theoretical contribution to literature, bringing together elements of careership, cognitive schema theory, and motivational theory and psychology of working theory, in a novel combination, to explain how young people were thinking about platform work in the context of their careers

    Racism Pays: How Racial Exploitation Gets Innovation Off the Ground

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    Recent work on the history of capitalism documents the key role that racial exploitation played in the launch of the global cotton economy and the construction of the transcontinental railroad. But racial exploitation is not a thing of the past. Drawing on three case studies, this Paper argues that some of our most celebrated innovations in the digital economy have gotten off the ground by racially exploiting workers of color, paying them less than the marginal revenue product of their labor for their essential contributions. Innovators like Apple and Uber have been able to racially exploit workers of color because they have monopsony power to do so. Workers of color have far fewer outside options than white workers, owing to intentional and structural discrimination against workers on the basis of their race. In the emerging digital economy, racial exploitation has paid off by giving innovators a workforce that is cheap, easy to scale, flexible, and productive—the kind of workforce that is especially useful in digital markets, where a first-mover advantage often translates to winner-take-all. This Paper argues that these workers should be paid the marginal revenue product of their labor, and it proposes a number of potential ways to do so: by increasing worker compensation or worker power. More generally, I argue that we should value the essential contributions of workers of color and immigrant workers who make innovation possible

    An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

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    Origin-destination~(OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow-related research, fostering interdisciplinary perspectives and modes of thinking.Comment: 49 pages, 6 figure

    Managing Consumers’ Adoption of Artificial Intelligence-Based Financial Robo-Advisory Services: A Moderated Mediation Model

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    Introduction/Main Objectives: This study investigates the determinants of willingness to use financial robo-advisory services. The study aims to identify the intertwined roles of perceived value, perceived risk, and perceived financial knowledge in consumers’ acceptance of financial robo-advisory services. Background Problem: Fintech and AI-based applications have opened up new prospects for financial management, but studies into the adoption and implementation of robo-advisors are limited and scant. Novelty: The study offers novel insights by exploring the direct and indirect effects of perceived value and risk on consumer deci­sions around adopting robo-advisory services. The study also identifies other major drivers of robo-advisory service adoption and formulates a comprehensive model. Research Methods: A quantitative method using a deductive approach was applied, with PLS-SEM performed on a sample of 285 respondents from Bangladesh. The sample was gathered using a purposive sampling method. Findings/Results: Findings revealed that while relative advantage and perceived innovativeness positively affected perceived value and adoption intention, complexity negatively impacted perceived value and adoption intention. The findings also highlighted that attitude had a negative effect on perceived risk and intention to adopt robo-advisory services. The mediating impact of perceived value and risk in predicting the relationship between relative advantage, attitude and behavioral intention to adopt robo-advisory services was also identified. Moreover, the study revealed that perceived financial knowledge moderated the relationship between perceived value and behavioral intention. Conclusion: This study contributes to the existing body of literature by showing the intertwined roles of perceived value, perceived risk, and perceived financial knowledge in consumer acceptance of robo-advisory services. The study provides meaningful insights for financial institutions, and policymakers seeking to make robo-advisory services more reliable and acceptable to consumers through innovative service design and positioning

    Quantum Secure Threshold Private Set Intersection Protocol for IoT-Enabled Privacy Preserving Ride-Sharing Application

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    The Internet of Things (IoT)-enabled ride sharing is one of the most transforming and innovative technologies in the transportation industry. It has myriads of advantages, but with increasing demands there are security concerns as well. Traditionally, cryptographic methods are used to address the security and privacy concerns in a ride sharing system. Unfortunately, due to the emergence of quantum algorithms, these cryptographic protocols may not remain secure. Hence, there is a necessity for privacy-preserving ride sharing protocols which can resist various attacks against quantum computers. In the domain of privacy preserving ride sharing, a threshold private set intersection (TPSI) can be adopted as a viable solution because it enables the users to determine the intersection of private data sets if the set intersection cardinality is greater than or equal to a threshold value. Although TPSI can help to alleviate privacy concerns, none of the existing TPSI is quantum secure. Furthermore, the existing TPSI faces the issue of long-term security. In contrast to classical and post quantum cryptography, quantum cryptography (QC) provides a more robust solution, where QC is based on the postulates of quantum physics (e.g., Heisenberg uncertainty principle, no cloning theorem, etc.) and it can handle the prevailing issues of quantum threat and long-term security. Herein, we propose the first QC based TPSI protocol which has a direct application in privacy preserving ride sharing. Due to the use of QC, our IoT-enabled ride sharing scheme remains quantum secure and achieves long-term security as well

    Learning Sparse Graphon Mean Field Games

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    Although the field of multi-agent reinforcement learning (MARL) has made considerable progress in the last years, solving systems with a large number of agents remains a hard challenge. Graphon mean field games (GMFGs) enable the scalable analysis of MARL problems that are otherwise intractable. By the mathematical structure of graphons, this approach is limited to dense graphs which are insufficient to describe many real-world networks such as power law graphs. Our paper introduces a novel formulation of GMFGs, called LPGMFGs, which leverages the graph theoretical concept of LpL^p graphons and provides a machine learning tool to efficiently and accurately approximate solutions for sparse network problems. This especially includes power law networks which are empirically observed in various application areas and cannot be captured by standard graphons. We derive theoretical existence and convergence guarantees and give empirical examples that demonstrate the accuracy of our learning approach for systems with many agents. Furthermore, we extend the Online Mirror Descent (OMD) learning algorithm to our setup to accelerate learning speed, empirically show its capabilities, and conduct a theoretical analysis using the novel concept of smoothed step graphons. In general, we provide a scalable, mathematically well-founded machine learning approach to a large class of otherwise intractable problems of great relevance in numerous research fields.Comment: accepted for publication at the International Conference on Artificial Intelligence and Statistics (AISTATS) 2023; code available at: https://github.com/ChrFabian/Learning_sparse_GMFG

    The Road to Autonomy

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    Workers\u27 Perceived Algorithmic Exploitation on Online Labor Platforms

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    Online labor platforms (OLPs) like Uber have become increasingly prevalent, attracting numerous workers with the appeal of flexible work arrangements. OLPs present themselves as an innovative alternative to traditional employment structures, but there remains a sense of exploitation among their workers. This perception is impelled by the platforms’ heavy reliance on algorithmic management (AM), which often exerts a tighter form of management than traditional human-led oversight. This study examines how AM induces workers’ exploitation perceptions (i.e., perceived algorithmic exploitation) by conducting a grounded theory methodology on 22 interviews with Uber drivers. We identified several forms of perceived algorithmic exploitation (i.e., manipulation, falsification, disempowerment, and dependency), which include AM practices that workers perceive as disadvantaging them to the potential benefit of the OLP. Overall, this study contributes to the “dark side” of AM and offers platform providers and policymakers crucial insights to create more sustainable working environments for platform workers
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