5 research outputs found
FNNC: Achieving Fairness through Neural Networks
In classification models fairness can be ensured by solving a constrained
optimization problem. We focus on fairness constraints like Disparate Impact,
Demographic Parity, and Equalized Odds, which are non-decomposable and
non-convex. Researchers define convex surrogates of the constraints and then
apply convex optimization frameworks to obtain fair classifiers. Surrogates
serve only as an upper bound to the actual constraints, and convexifying
fairness constraints might be challenging.
We propose a neural network-based framework, \emph{FNNC}, to achieve fairness
while maintaining high accuracy in classification. The above fairness
constraints are included in the loss using Lagrangian multipliers. We prove
bounds on generalization errors for the constrained losses which asymptotically
go to zero. The network is optimized using two-step mini-batch stochastic
gradient descent. Our experiments show that FNNC performs as good as the state
of the art, if not better. The experimental evidence supplements our
theoretical guarantees. In summary, we have an automated solution to achieve
fairness in classification, which is easily extendable to many fairness
constraints
Fair Allocation of goods and chores -- Tutorial and Survey of Recent Results
Fair resource allocation is an important problem in many real-world
scenarios, where resources such as goods and chores must be allocated among
agents. In this survey, we delve into the intricacies of fair allocation,
focusing specifically on the challenges associated with indivisible resources.
We define fairness and efficiency within this context and thoroughly survey
existential results, algorithms, and approximations that satisfy various
fairness criteria, including envyfreeness, proportionality, MMS, and their
relaxations. Additionally, we discuss algorithms that achieve fairness and
efficiency, such as Pareto Optimality and Utilitarian Welfare. We also study
the computational complexity of these algorithms, the likelihood of finding
fair allocations, and the price of fairness for each fairness notion. We also
cover mixed instances of indivisible and divisible items and investigate
different valuation and allocation settings. By summarizing the
state-of-the-art research, this survey provides valuable insights into fair
resource allocation of indivisible goods and chores, highlighting computational
complexities, fairness guarantees, and trade-offs between fairness and
efficiency. It serves as a foundation for future advancements in this vital
field
Combinatorial Civic Crowdfunding with Budgeted Agents: Welfare Optimality at Equilibrium and Optimal Deviation
Civic Crowdfunding (CC) uses the ``power of the crowd'' to garner
contributions towards public projects. As these projects are non-excludable,
agents may prefer to ``free-ride,'' resulting in the project not being funded.
For single project CC, researchers propose to provide refunds to incentivize
agents to contribute, thereby guaranteeing the project's funding. These funding
guarantees are applicable only when agents have an unlimited budget. This work
focuses on a combinatorial setting, where multiple projects are available for
CC and agents have a limited budget. We study certain specific conditions where
funding can be guaranteed. Further, funding the optimal social welfare subset
of projects is desirable when every available project cannot be funded due to
budget restrictions. We prove the impossibility of achieving optimal welfare at
equilibrium for any monotone refund scheme. We then study different heuristics
that the agents can use to contribute to the projects in practice. Through
simulations, we demonstrate the heuristics' performance as the average-case
trade-off between welfare obtained and agent utility.Comment: To appear in the Proceedings of the Thirty-Seventh AAAI Conference on
Artificial Intelligence (AAAI '23). A preliminary version of this paper
titled "Welfare Optimal Combinatorial Civic Crowdfunding with Budgeted
Agents" also appeared at GAIW@AAMAS '2
Coordinating Monetary Contributions in Participatory Budgeting
We formalize a framework for coordinating the funding of projects and sharing
the costs among agents with quasi-linear utility functions and individual
budgets. Our model contains the classical discrete participatory budgeting
model as a special case, while capturing other well-motivated problems. We
propose several important axioms and objectives and study how well they can be
simultaneously satisfied. One of our main results is that whereas welfare
maximization admits an FPTAS, welfare maximization subject to a well-motivated
and very weak participation requirement leads to a strong inapproximability
result. We show that this result is bypassed if we consider some natural
restricted valuations or when we take an average-case heuristic approach