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1. So you think you can track?

   https://doi.org/10.1109/WACV57701.2024.00447  

   Gloudemans, Derek; Zachár, Gergely; Wang, Yanbing; Ji, Junyi; Nice, Matt; Bunting, Matt; Barbour, William W; Sprinkle, Jonathan; Piccoli, Benedetto; Delle_Monache, Maria Laura; et al (January 2024, IEEE)
2. Can you trust what you see online?

   https://doi.org/10.33424/FUTURUM381  

   Wang, Gang; Mink, Jaron (April 2023, Futurum Careers)

   https://futurumcareers.com/can-you-trust-what-you-see-online 

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3. GitLab: work where you want, when you want

   https://doi.org/10.1186/s41469-020-00087-8  

   Choudhury, Prithwiraj; Crowston, Kevin; Dahlander, Linus; Minervini, Marco S.; Raghuram, Sumita (December 2020, Journal of Organization Design)

   Abstract GitLab is a software company that works “all remote” at the scale of more than 1000 employees located in more than 60 countries. GitLab has no physical office and its employees can work from anywhere they choose. Any step of the organizational life of a GitLab employee (e.g., hiring, onboarding and firing) is performed remotely, except for a yearly companywide gathering. GitLab strongly relies on asynchronous coordination, allowing employees to work anytime they want. After highlighting some of the main practices implemented by GitLab to effectively work all remotely and asynchronously, I asked renowned organizational scientists their thoughts on this interesting case and to question the generalizability of the all remote asynchronous model. Understanding whether and under what conditions this model can succeed can be of guidance for organizational designers that are now considering different remote models in response of the COVID-19 shock and its aftermath. 

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4. You Get What You Give: Reciprocally Fair Federated Learning

   Murhekar, A; Song, J; Shahkar, P; Ray_Chaudhury, B; Mehta, R (May 2025, Proceedings of Machine Learning Research (PMLR))

   Federated learning (FL) is a popular collaborative learning paradigm, whereby agents with individual datasets can jointly train an ML model. While higher data sharing improves model accuracy and leads to higher payoffs, it also raises costs associated with data acquisition or loss of privacy, causing agents to be strategic about their data contribution. This leads to undesirable behavior at a Nash equilibrium (NE) such as free-riding, resulting in sub-optimal fairness, data sharing, and welfare. To address this, we design MSHAP, a budget-balanced payment mechanism for FL, that admits Nash equilibria under mild conditions, and achieves reciprocal fairness: where each agent's payoff equals her contribution to the collaboration, as measured by the Shapley share. In addition to fairness, we show that the NE under MSHAP has desirable guarantees in terms of accuracy, welfare, and total data collected. We validate our theoretical results through experiments, demonstrating that MSHAP outperforms baselines in terms of fairness and efficiency. 

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5. You Get What You Give: Reciprocally Fair Federated Learning

   Murhekar, Aniket; Song, Jiaxin; Shahkar, Parnian; Chaudhury, Bhaskar Ray; Mehta, Ruta (July 2025, Proceedings of the 42nd International Conference on Machine Learning)