453 research outputs found
Reputation Agent: Prompting Fair Reviews in Gig Markets
Our study presents a new tool, Reputation Agent, to promote fairer reviews
from requesters (employers or customers) on gig markets. Unfair reviews,
created when requesters consider factors outside of a worker's control, are
known to plague gig workers and can result in lost job opportunities and even
termination from the marketplace. Our tool leverages machine learning to
implement an intelligent interface that: (1) uses deep learning to
automatically detect when an individual has included unfair factors into her
review (factors outside the worker's control per the policies of the market);
and (2) prompts the individual to reconsider her review if she has incorporated
unfair factors. To study the effectiveness of Reputation Agent, we conducted a
controlled experiment over different gig markets. Our experiment illustrates
that across markets, Reputation Agent, in contrast with traditional approaches,
motivates requesters to review gig workers' performance more fairly. We discuss
how tools that bring more transparency to employers about the policies of a gig
market can help build empathy thus resulting in reasoned discussions around
potential injustices towards workers generated by these interfaces. Our vision
is that with tools that promote truth and transparency we can bring fairer
treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202
Stationary solutions of driven fourth- and sixth-order Cahn-Hilliard type equations
New types of stationary solutions of a one-dimensional driven sixth-order
Cahn-Hilliard type equation that arises as a model for epitaxially growing
nano-structures such as quantum dots, are derived by an extension of the method
of matched asymptotic expansions that retains exponentially small terms. This
method yields analytical expressions for far-field behavior as well as the
widths of the humps of these spatially non-monotone solutions in the limit of
small driving force strength which is the deposition rate in case of epitaxial
growth. These solutions extend the family of the monotone kink and antikink
solutions. The hump spacing is related to solutions of the Lambert
function. Using phase space analysis for the corresponding fifth-order
dynamical system, we use a numerical technique that enables the efficient and
accurate tracking of the solution branches, where the asymptotic solutions are
used as initial input. Additionally, our approach is first demonstrated for the
related but simpler driven fourth-order Cahn-Hilliard equation, also known as
the convective Cahn-Hilliard equation
World citation and collaboration networks: uncovering the role of geography in science
Modern information and communication technologies, especially the Internet,
have diminished the role of spatial distances and territorial boundaries on the
access and transmissibility of information. This has enabled scientists for
closer collaboration and internationalization. Nevertheless, geography remains
an important factor affecting the dynamics of science. Here we present a
systematic analysis of citation and collaboration networks between cities and
countries, by assigning papers to the geographic locations of their authors'
affiliations. The citation flows as well as the collaboration strengths between
cities decrease with the distance between them and follow gravity laws. In
addition, the total research impact of a country grows linearly with the amount
of national funding for research & development. However, the average impact
reveals a peculiar threshold effect: the scientific output of a country may
reach an impact larger than the world average only if the country invests more
than about 100,000 USD per researcher annually.Comment: Published version. 9 pages, 5 figures + Appendix, The world citation
and collaboration networks at both city and country level are available at
http://becs.aalto.fi/~rajkp/datasets.htm
Exploring Author Gender in Book Rating and Recommendation
Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as gender or ethnic discrimination in publishing. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution
Measurement of shower development and its Moli\`ere radius with a four-plane LumiCal test set-up
A prototype of a luminometer, designed for a future e+e- collider detector,
and consisting at present of a four-plane module, was tested in the CERN PS
accelerator T9 beam. The objective of this beam test was to demonstrate a
multi-plane tungsten/silicon operation, to study the development of the
electromagnetic shower and to compare it with MC simulations. The Moli\`ere
radius has been determined to be 24.0 +/- 0.6 (stat.) +/- 1.5 (syst.) mm using
a parametrization of the shower shape. Very good agreement was found between
data and a detailed Geant4 simulation.Comment: Paper published in Eur. Phys. J., includes 25 figures and 3 Table
Performance of fully instrumented detector planes of the forward calorimeter of a Linear Collider detector
Detector-plane prototypes of the very forward calorimetry of a future
detector at an e+e- collider have been built and their performance was measured
in an electron beam. The detector plane comprises silicon or GaAs pad sensors,
dedicated front-end and ADC ASICs, and an FPGA for data concentration.
Measurements of the signal-to-noise ratio and the response as a function of the
position of the sensor are presented. A deconvolution method is successfully
applied, and a comparison of the measured shower shape as a function of the
absorber depth with a Monte-Carlo simulation is given.Comment: 25 pages, 32 figures, revised version following comments from
referee
POTs: Protective Optimization Technologies
Algorithmic fairness aims to address the economic, moral, social, and
political impact that digital systems have on populations through solutions
that can be applied by service providers. Fairness frameworks do so, in part,
by mapping these problems to a narrow definition and assuming the service
providers can be trusted to deploy countermeasures. Not surprisingly, these
decisions limit fairness frameworks' ability to capture a variety of harms
caused by systems.
We characterize fairness limitations using concepts from requirements
engineering and from social sciences. We show that the focus on algorithms'
inputs and outputs misses harms that arise from systems interacting with the
world; that the focus on bias and discrimination omits broader harms on
populations and their environments; and that relying on service providers
excludes scenarios where they are not cooperative or intentionally adversarial.
We propose Protective Optimization Technologies (POTs). POTs provide means
for affected parties to address the negative impacts of systems in the
environment, expanding avenues for political contestation. POTs intervene from
outside the system, do not require service providers to cooperate, and can
serve to correct, shift, or expose harms that systems impose on populations and
their environments. We illustrate the potential and limitations of POTs in two
case studies: countering road congestion caused by traffic-beating
applications, and recalibrating credit scoring for loan applicants.Comment: Appears in Conference on Fairness, Accountability, and Transparency
(FAT* 2020). Bogdan Kulynych and Rebekah Overdorf contributed equally to this
work. Version v1/v2 by Seda G\"urses, Rebekah Overdorf, and Ero Balsa was
presented at HotPETS 2018 and at PiMLAI 201
Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy
This article evaluates the job quality of work in the remote gig economy. Such work consists of the remote provision of a wide variety of digital services mediated by online labour platforms. Focusing on workers in Southeast Asia and Sub-Saharan Africa, the article draws on semi-structured interviews in six countries (N = 107) and a cross-regional survey (N = 679) to detail the manner in which remote gig work is shaped by platform-based algorithmic control. Despite varying country contexts and types of work, we show that algorithmic control is central to the operation of online labour platforms. Algorithmic management techniques tend to offer workers high levels of flexibility, autonomy, task variety and complexity. However, these mechanisms of control can also result in low pay, social isolation, working unsocial and irregular hours, overwork, sleep deprivation and exhaustion
Effect of quasi-periodic gravitational modulation on the convective instability in Hele-Shaw cell
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