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Writing about "Slavery"? This Might Help
A group led by senior slavery scholars of color community-sourced this short guide to share with and be used by editors, presses, museums, journalists, and curricular projects as well as with teachers, writers, curators, and public historians. Considering the particularities (legal, demographic, etc.) of the institution of slavery in various parts of the Americas, Europe, Africa, and Asia, and how slavery changed over time, this guide is a set of suggestions that raises questions and sensitivities rather than serving as a checklist that enforces any set of orthodoxies.
language  fairness  Research 
11 days ago by miaridge
[1507.06827] Egalitarianism of Random Assignment Mechanisms
We consider the egalitarian welfare aspects of random assignment mechanisms when agents have unrestricted cardinal utilities over the objects. We give bounds on how well different random assignment mechanisms approximate the optimal egalitarian value and investigate the effect that different well-known properties like ordinality, envy-freeness, and truthfulness have on the achievable egalitarian value. Finally, we conduct detailed experiments analyzing the tradeoffs between efficiency with envy-freeness or truthfulness using two prominent random assignment mechanisms --- random serial dictatorship and the probabilistic serial mechanism --- for different classes of utility functions and distributions.
game-theory  assignment-problems  fairness  collective-behavior  mechanism-design  to-understand 
14 days ago by Vaguery
[1705.09444] Equilibria in Sequential Allocation
Sequential allocation is a simple mechanism for sharing multiple indivisible items. We study strategic behavior in sequential allocation. In particular, we consider Nash dynamics, as well as the computation and Pareto optimality of pure equilibria, and Stackelberg strategies. We first demonstrate that, even for two agents, better responses can cycle. We then present a linear-time algorithm that returns a profile (which we call the "bluff profile") that is in pure Nash equilibrium. Interestingly, the outcome of the bluff profile is the same as that of the truthful profile and the profile is in pure Nash equilibrium for \emph{all} cardinal utilities consistent with the ordinal preferences. We show that the outcome of the bluff profile is Pareto optimal with respect to pairwise comparisons. In contrast, we show that an assignment may not be Pareto optimal with respect to pairwise comparisons even if it is a result of a preference profile that is in pure Nash equilibrium for all utilities consistent with ordinal preferences. Finally, we present a dynamic program to compute an optimal Stackelberg strategy for two agents, where the second agent has a constant number of distinct values for the items.
game-theory  allocation-problems  planning  algorithms  fairness  performance-measure  rather-interesting  to-simulate  consider:looking-to-see 
15 days ago by Vaguery
[1602.06940] Complexity of Manipulating Sequential Allocation
Sequential allocation is a simple allocation mechanism in which agents are given pre-specified turns and each agents gets the most preferred item that is still available. It has long been known that sequential allocation is not strategyproof.
Bouveret and Lang (2014) presented a polynomial-time algorithm to compute a best response of an agent with respect to additively separable utilities and claimed that (1) their algorithm correctly finds a best response, and (2) each best response results in the same allocation for the manipulator. We show that both claims are false via an example. We then show that in fact the problem of computing a best response is NP-complete. On the other hand, the insights and results of Bouveret and Lang (2014) for the case of two agents still hold.
game-theory  assignment-problems  rather-interesting  algorithms  planning  fairness  nudge-targets  consider:looking-to-see 
15 days ago by Vaguery
Twitter
If you're a student of and want to become a force for , learn:

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good  fairness  AI  privacy  from twitter_favs
20 days ago by danbri
Twitter
If you're a student of and want to become a force for , learn:

- AI
- AI
- AI…
AI  good  privacy  fairness  from twitter_favs
20 days ago by mattedgar
Fairness and machine learning
This online textbook is an incomplete work in progress. Essential chapters are still missing. In the spirit of open review, we solicit broad feedback that will influence existing chapters, as well as the development of later material.
book  ethics  fairness  AI  machine_learning 
22 days ago by macloo

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