23,150 research outputs found
Balancing Utility and Fairness in Submodular Maximization (Technical Report)
Submodular function maximization is central in numerous data science
applications, including data summarization, influence maximization, and
recommendation. In many of these problems, our goal is to find a solution that
maximizes the \emph{average} of the utilities for all users, each measured by a
monotone submodular function. When the population of users is composed of
several demographic groups, another critical problem is whether the utility is
fairly distributed across groups. In the context of submodular optimization, we
seek to improve the welfare of the \emph{least well-off} group, i.e., to
maximize the minimum utility for any group, to ensure fairness. Although the
\emph{utility} and \emph{fairness} objectives are both desirable, they might
contradict each other, and, to our knowledge, little attention has been paid to
optimizing them jointly. In this paper, we propose a novel problem called
\emph{Bicriteria Submodular Maximization} (BSM) to strike a balance between
utility and fairness. Specifically, it requires finding a fixed-size solution
to maximize the utility function, subject to the value of the fairness function
not being below a threshold. Since BSM is inapproximable within any constant
factor in general, we propose efficient data-dependent approximation algorithms
for BSM by converting it into other submodular optimization problems and
utilizing existing algorithms for the converted problems to obtain solutions to
BSM. Using real-world and synthetic datasets, we showcase applications of our
framework in three submodular maximization problems, namely maximum coverage,
influence maximization, and facility location.Comment: 13 pages, 7 figures, under revie
CrossWalk: Fairness-enhanced Node Representation Learning
The potential for machine learning systems to amplify social inequities and
unfairness is receiving increasing popular and academic attention. Much recent
work has focused on developing algorithmic tools to assess and mitigate such
unfairness. However, there is little work on enhancing fairness in graph
algorithms. Here, we develop a simple, effective and general method, CrossWalk,
that enhances fairness of various graph algorithms, including influence
maximization, link prediction and node classification, applied to node
embeddings. CrossWalk is applicable to any random walk based node
representation learning algorithm, such as DeepWalk and Node2Vec. The key idea
is to bias random walks to cross group boundaries, by upweighting edges which
(1) are closer to the groups' peripheries or (2) connect different groups in
the network. CrossWalk pulls nodes that are near groups' peripheries towards
their neighbors from other groups in the embedding space, while preserving the
necessary structural properties of the graph. Extensive experiments show the
effectiveness of our algorithm to enhance fairness in various graph algorithms,
including influence maximization, link prediction and node classification in
synthetic and real networks, with only a very small decrease in performance.Comment: Association for the Advancement of Artificial Intelligence (AAAI)
202
Fairness in Influence Maximization through Randomization
The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In the present paper, we propose to use randomization as a mean for achieving fairness. While this general idea is not new, it has not been applied in the area of information spread in networks. Similar to previous works like Fish et al. (WWW '19) and Tsang et al. (IJCAI '19), we study the maximin criterion for (group) fairness. By allowing randomized solutions, we introduce two different variants of this problem. While the original deterministic maximin problem has been shown to be inapproximable, interestingly, we show that both probabilistic variants permit approximation algorithms with a constant multiplicative factor of 1 - 1/e plus an additive arbitrarily small error due to the simulation of the information spread. For an experimental study, we provide implementations of our methods and compare the achieved fairness values to existing methods. Non-surprisingly, the ex-ante values, i.e., minimum expected value of an individual (or group) to obtain the information, of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This confirms that studying fairness via randomization is a worthwhile direction. More surprisingly, we observe that even the ex-post fairness values, i.e., fairness values of sets sampled according to the probabilistic strategies, computed by our routines dominate over the fairness achieved by previous methods on most of the instances tested
Multiobjective Optimization-Based Collective Opinion Generation With Fairness Concern
The generation of collective opinion based on probability
distribution function (PDF) aggregation is gradually
becoming a critical approach for tackling immense and delicate
assessment and evaluation tasks in decision analysis. However, the
existing collective opinion generation approaches fail to model
the behavioral characteristics associated with individuals, and
thus, cannot reflect the fairness concerns among them when
they consciously or unconsciously incorporate their judgments
on the fairness level of distribution into the formulations of
individual opinions. In this study, we propose a multiobjective
optimization-driven collective opinion generation approach that
generalizes the bi-objective optimization-based PDF aggregation
paradigm. In doing so, we adapt the notion of fairness concern
utility function to characterize the influence of fairness inclusion
and take its maximization as an additional objective, together
with the criteria of consensus and confidence levels, to achieve in
generating collective opinion. The formulation of fairness concern
is then transformed into the congregation of individual
fairness concern utilities in the use of aggregation functions.
We regard the generalized extended Bonferroni mean (BM) as
an elaborated framework for aggregating individual fairness
concern utilities. In such way, we establish the concept of BMtype
collective fairness concern utility to empower multiobjective
optimization-driven collective opinion generation approach with
the capacity of modeling different structures associated with
the expert group with fairness concern. The application of the
proposed fairness-aware framework in the maturity assessment
of building information modeling demonstrates the effectiveness
and efficiency of multiobjective optimization-driven approach for
generating collective opinion when accomplishing complicated
assessment and evaluation tasks with data scarcity
Multi-objective optimization-based collective opinion generation with fairness concern
open access articleThe generation of collective opinion based on probability distribution function (PDF) aggregation is gradually becoming a critical approach for tackling immense and delicate assessment and evaluation tasks in decision analysis. However, the existing collective opinion generation approaches fail to model the behavioral characteristics associated with individuals, and thus, cannot reflect the fairness concerns among them when they consciously or unconsciously incorporate their judgments on the fairness level of distribution into the formulations of individual opinions. In this study, we propose a multiobjective optimization-driven collective opinion generation approach that generalizes the bi-objective optimization-based PDF aggregation paradigm. In doing so, we adapt the notion of fairness concern utility function to characterize the influence of fairness inclusion and take its maximization as an additional objective, together with the criteria of consensus and confidence levels, to achieve in generating collective opinion. The formulation of fairness concern is then transformed into the congregation of individual fairness concern utilities in the use of aggregation functions. We regard the generalized extended Bonferroni mean (BM) as an elaborated framework for aggregating individual fairness concern utilities. In such way, we establish the concept of BM-type collective fairness concern utility to empower multiobjective optimization-driven collective opinion generation approach with the capacity of modeling different structures associated with the expert group with fairness concern. The application of the proposed fairness-aware framework in the maturity assessment of building information modeling demonstrates the effectiveness and efficiency of multiobjective optimization-driven approach for generating collective opinion when accomplishing complicated assessment and evaluation tasks with data scarcity
Sub-Stream Fairness and Numerical Correctness in MIMO Interference Channels
Signal-to-interference plus noise ratio (SINR) and rate fairness in a system
are substantial quality-of-service (QoS) metrics. The acclaimed SINR
maximization (max-SINR) algorithm does not achieve fairness between user's
streams, i.e., sub-stream fairness is not achieved. To this end, we propose a
distributed power control algorithm to render sub-stream fairness in the
system. Sub-stream fairness is a less restrictive design metric than stream
fairness (i.e., fairness between all streams) thus sum-rate degradation is
milder. Algorithmic parameters can significantly differentiate the results of
numerical algorithms. A complete picture for comparison of algorithms can only
be depicted by varying these parameters. For example, a predetermined iteration
number or a negligible increment in the sum-rate can be the stopping criteria
of an algorithm. While the distributed interference alignment (DIA) can
reasonably achieve sub-stream fairness for the later, the imbalance between
sub-streams increases as the preset iteration number decreases. Thus comparison
of max-SINR and DIA with a low preset iteration number can only depict a part
of the picture. We analyze such important parameters and their effects on SINR
and rate metrics to exhibit numerical correctness in executing the benchmarks.
Finally, we propose group filtering schemes that jointly design the streams of
a user in contrast to max-SINR scheme that designs each stream of a user
separately.Comment: To be presented at IEEE ISWTA'1
Fairness, Adverse Selection, and Employment Contracts
This paper considers a firm whose potential employees have private information on both their productivity and the extent of their fairness concerns. Fairness is modelled as inequity aversion, where fair-minded workers suffer if their colleagues get more income net of production costs. Screening workers with equal productivity but different fairness concerns is shown to be impossible if both types are to be employed, thereby rendering the optimal employment contracts discontinuous in the fraction of fair-minded workers. As a result, fairness might infuence the employment contracts of all workers although only some are fair-minded, and identical firms facing very similar pools of workers might employ very different remuneration schemes
Fuzzy Logic and Corporate Governance Theories
[Excerpt] “Fuzzy logic is a theory that categorizes concepts or things belonging to more than one group. A methodology that explains how things function in multiple groups (not fully in one group or another) offers advantages when no one definition or membership in a group accounts for belonging to multiple groups. The principal/agent model of corporate governance has some characteristics of fuzzy logic theory.
Under traditional agency theory of corporate governance, shareholders, directors, and senior corporate officers each belong to groups having multiple attributes. In the principal/agent model of corporate governance, shareholders are owners or principals; directors are shareholders and agents of the corporation; and senior corporate officers are directors’ agents, shareholders’ agents, and agents of the corporation. Each one functions within multiple groups serving multiple agency roles, and each owes fiduciary duties that vary depending on whose agent they are functioning as.
Such a multi-dimensional role for corporate actors is a consequence of multi-definitional corporate purpose within agency theory of governance. This multi-dimensional group membership is not easily reconciled within agency theory and is therefore not always explained. However, traditional corporate governance theory can borrow another basic tenet of fuzzy logic theory. Fuzzy theory not only accounts for membership in multiple groups, but also explains how things work because they are multidimensional or ambiguous. This article seeks to explain the ambiguities of corporate governance theory and suggests a framework that accounts for the multi-agent role of senior corporate officers of public companies. It offers a kind of fuzzy logic theory for understanding the fiduciary duties of senior officers.
The purpose of this article is to evaluate other models of corporate governance that account for the multi-agent role of senior officers of public companies and assess the ability of various models to hold senior officers accountable to the corporation.
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