Observing and recommending from a social web with biases

The research question this report addresses is: how, and to what extent, those directly involved with the design, development and employment of a specific black box algorithm can be certain that it is not unlawfully discriminating (directly and/or indirectly) against particular persons with protected characteristics (e.g. gender, race and ethnicity)?Comment: Technical Report, University of Southampton, March 201

Biases in scholarly recommender systems: impact, prevalence, and mitigation

We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market

An analysis of popularity biases in recommender system evaluation and algorithms

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 03-10-2019Las tecnologías de recomendación han ido progresivamente extendiendo su presencia en las aplicaciones y servicios de uso diario. Los sistemas de recomendación buscan realizar sugerencias individualizadas de productos u opciones que los usuarios puedan encontrar interesantes o útiles. Implícita en el concepto de recomendación está la idea de que las sugerencias más satisfactorias para cada usuario son aquellas que tienen en cuenta sus gustos particulares, por lo que cabría esperar que los algoritmos de recomendación más eficaces sean los más personalizados. Sin embargo, se ha observado recientemente que recomendar simplemente los productos más populares no resulta una estrategia mucho peor que los mejores y más sofisticados algoritmos personalizados, y más aún, que estos tienden a sesgar sus recomendaciones hacia opciones mayoritarias. Por todo ello, es rele-vante entender en qué medida y bajo qué circunstancias es la popularidad una señal real-mente efectiva a la hora de recomendar, y si su aparente efectividad se debe a la existencia de ciertos sesgos en las metodologías de evaluación offline actuales, como todo parece indicar, o no. En esta tesis abordamos esta cuestión desde un punto de vista plenamente formal, identificando los factores que pueden determinar la respuesta y modelizándolos en térmi-nos de dependencias probabilísticas entre variables aleatorias, tales como la votación, el descubrimiento y la relevancia. De esta forma, caracterizamos situaciones concretas que garantizan que la popularidad sea efectiva o que no lo sea, y establecemos las condiciones bajo las cuales pueden existir contradicciones entre el acierto observado y el real. Las principales conclusiones hacen referencia a escenarios simplificados prototípicos, más allá de los cuales el análisis formal concluye que cualquier resultado es posible. Para profun-dizar en el escenario general sin suposiciones tan simplificadas, estudiamos un caso parti-cular donde el descubrimiento de ítems es consecuencia de la interacción entre usuarios en una red social. Además, en esta tesis proporcionamos una explicación formal del sesgo de populari-dad que presentan los algoritmos de filtrado colaborativo. Para ello, desarrollamos una versión probabilística del algoritmo de vecinos próximos kNN. Dicha versión evidencia además la condición fundamental que hace que kNN produzca recomendaciones perso-nalizadas y se diferencie de la popularidad pura

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement

The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.Comment: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, 2013, Pages 793-80

Towards Responsible Media Recommendation

Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recommendations may lead to undesired effects on consumers or even society, for example, when an algorithm leads to the creation of filter bubbles or amplifies the spread of misinformation. These well-documented phenomena create a need for improved mechanisms for responsible media recommendation, which avoid such negative effects of recommender systems. In this research note, we review the threats and challenges that may result from the use of automated media recommendation technology, and we outline possible steps to mitigate such undesired societal effects in the future.publishedVersio

Bias characterization, assessment, and mitigation in location-based recommender systems

Location-Based Social Networks stimulated the rise of services such as Location-based Recommender Systems. These systems suggest to users points of interest (or venues) to visit when they arrive in a specific city or region. These recommendations impact various stakeholders in society, like the users who receive the recommendations and venue owners. Hence, if a recommender generates biased or polarized results, this affects in tangible ways both the experience of the users and the providers’ activities. In this paper, we focus on four forms of polarization, namely venue popularity, category popularity, venue exposure, and geographical distance. We characterize them on different families of recommendation algorithms when using a realistic (temporal-aware) offline evaluation methodology while assessing their existence. Besides, we propose two automatic approaches to mitigate those biases. Experimental results on real-world data show that these approaches are able to jointly improve the recommendation effectiveness, while alleviating these multiple polarizationsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been funded by the Ministerio de Ciencia e Innovación (reference PID2019-108965GB-I00) and by the European Social Fund (ESF), within the 2017 call for predoctoral contract