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GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning [article]

Govardana Sachithanandam Ramachandran, Ivan Brugere, Lav R. Varshney, Caiming Xiong
2021 arXiv   pre-print
Here we introduce a new class of problems, Graph Augmentation for Equitable Access (GAEA), to enhance equity in networked systems by editing graph edges under budget constraints.  ...  We develop a principled, sample- and time- efficient Markov Reward Process (MRP)-based mechanism design framework for GAEA. Our algorithm outperforms baselines on a diverse set of synthetic graphs.  ...  Graph Diffusion, Augmentation, and Reinforcement Learning Our formulation of equitable access lies within a larger body of work on diffusion in graphs.  ... 
arXiv:2012.03900v2 fatcat:xnotbuzozbedrf4ahuba4bubra

Algorithmic Fairness Datasets: the Story so Far [article]

Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto
2022 arXiv   pre-print
Progress in fair Machine Learning hinges on data, which can be appropriately used only if adequately documented.  ...  Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS and German Credit, for which we compile in-depth documentation.  ...  Acknowledgements The authors would like to thank the following researchers and dataset creators for the useful feedback on the data briefs: Alain Barrat, Luc Behaghel, Asia Biega, Marko Bohanec, Chris  ... 
arXiv:2202.01711v3 fatcat:kd546yklwjhvtkrbhtzgbzb2xm

Beyond Territory and Scarcity: Exploring Conflicts over Natural Resource Management

Helga Malmin Binningsbø
2006 Journal of Peace Research  
Vulnerable people who had hitherto had access to land via customary land claims find that such claims are no longer being honoured by land-holders.  ...  Then people experience difference, and they will, of course, learn from this.  ... 
doi:10.1177/002234330604300613 fatcat:a6y7jltktrbbnakh7jy36odehi