118,296 research outputs found
Problem Formulation and Fairness
Formulating data science problems is an uncertain and difficult process. It
requires various forms of discretionary work to translate high-level objectives
or strategic goals into tractable problems, necessitating, among other things,
the identification of appropriate target variables and proxies. While these
choices are rarely self-evident, normative assessments of data science projects
often take them for granted, even though different translations can raise
profoundly different ethical concerns. Whether we consider a data science
project fair often has as much to do with the formulation of the problem as any
property of the resulting model. Building on six months of ethnographic
fieldwork with a corporate data science team---and channeling ideas from
sociology and history of science, critical data studies, and early writing on
knowledge discovery in databases---we describe the complex set of actors and
activities involved in problem formulation. Our research demonstrates that the
specification and operationalization of the problem are always negotiated and
elastic, and rarely worked out with explicit normative considerations in mind.
In so doing, we show that careful accounts of everyday data science work can
help us better understand how and why data science problems are posed in
certain ways---and why specific formulations prevail in practice, even in the
face of what might seem like normatively preferable alternatives. We conclude
by discussing the implications of our findings, arguing that effective
normative interventions will require attending to the practical work of problem
formulation.Comment: Conference on Fairness, Accountability, and Transparency (FAT* '19),
January 29-31, 2019, Atlanta, GA, US
Is Equality always desirable?
In this paper, we analyze the trade-off between perceived fairness and perceived attractiveness in crew rostering.
First, we introduce the Fairness-oriented Crew Rostering Problem. In this problem, attractive cyclic rosters have to be constructed, while respecting a pre-specified fairness level. Then, we propose a flexible mathematical formulation, able to exploit problem specific knowledge, and develop an exact Branch-Price-and-Cut solution method.
The solution method combines Branch-and-Bound with column generation, where profitable columns are separated by solving resource constrained shortest path problems with surplus variables. We also derive a set of valid inequalities to tighten the formulation. Finally, we demonstrate the benefit of our approach on practical instances from Netherlands Railways, the largest passenger railway operator in the Netherlands.
We are able to construct the explicit trade-off curve between fairness and attractiveness and show that a sequential approach can lead to suboptimal results. In particular, we show that focusing solely on fairness leads to rosters that are disproportionally less attractive. Furthermore, this decrease in attractiveness is heavily skewed towards the most exible employees. Thus, in order to generate truly fair rosters, the explicit trade-off between fairness and attractiveness should be considered
Max-min Fair Beamforming for SWIPT Systems with Non-linear EH Model
We study the beamforming design for multiuser systems with simultaneous
wireless information and power transfer (SWIPT). Employing a practical
non-linear energy harvesting (EH) model, the design is formulated as a
non-convex optimization problem for the maximization of the minimum harvested
power across several energy harvesting receivers. The proposed problem
formulation takes into account imperfect channel state information (CSI) and a
minimum required signal-to-interference-plus-noise ratio (SINR). The globally
optimal solution of the design problem is obtained via the semidefinite
programming (SDP) relaxation approach. Interestingly, we can show that at most
one dedicated energy beam is needed to achieve optimality. Numerical results
demonstrate that with the proposed design a significant performance gain and
improved fairness can be provided to the users compared to two baseline
schemes.Comment: Invited paper, IEEE VTC 2017, Fall, Toronto, Canad
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