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 $W$ 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