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Algorithmic decision making and the cost of fairness
Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are... -
Automatic pass annotation from soccer VideoStreams based on object detection ...
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of data that describe all the spatio-temporal events that occur in each... -
A qualitative exploration of perceptions of algorithmic fairness
Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases,... -
A Learned Approach to Quicken and Compress Rank Select Dictionaries
We introduce the first “learned” scheme for implementing a compressed rank/select dictionary. We prove theoretical bounds on its time and space performance both in the worst... -
A comparative study of fairness enhancing interventions in machine learning
Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups... -
Estimating countries’ peace index through the lens of the world news as monit...
Peacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital... -
STS-EPR: Modelling individual mobility considering the spatial, temporal, and...
Modelling human mobility is crucial in several scientific areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing... -
Heterogeneous Document Embeddings for Cross-Lingual Text Classification
Funnelling (Fun) is a method for cross-lingual text classification (CLC) based on a two-tier ensemble for heterogeneous transfer learning. In Fun, 1st-tier classifiers, each... -
Boilerplate Removal using a Neural Sequence Labeling Model
The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web... -
Private traits and attributes are predictable from digital records of human b...
We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes... -
Beyond Distributive Fairness in Algorithmic Decision Making
Beyond Distributive Fairness in Algorithmic Decision Making Feature Selection for Procedurally Fair Learning With widespread use of machine learning methods in numerous... -
Private Interpretable Next Basket Prediction Boosted with Representative Recipes
Food is an essential element of our lives, cultures, and a crucial part of human experience. The study of food purchases can drive the design of practical services such as... -
Private Explaining Any Time Series Classifier
We present a method to explain the decisions of black box models for time series classification. The explanation consists of factual and counterfactual shapelet-based rules... -
Why Are Learned Indexes So Effective
A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends...-
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Private Structural Invariants in Individuals Language Use The Ego Network of Words
The cognitive constraints that humans exhibit in their social interactions have been extensively studied by anthropologists, who have highlighted their regularities across... -
Private Predicting and Explaining Privacy Risk Exposure in Mobility Data
Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the... -
Explanation of Deep Models with Limited Interaction for Trade Secret and Priv...
An ever-increasing number of decisions affecting our lives are made by algorithms. For this reason, algorithmic transparency is becoming a pressing need: automated decisions... -
Multi layered Explanations from Algorithmic Impact Assessments in the GDPR
Impact assessments have received particular attention on both sides of the Atlantic as a tool for implementing algorithmic accountability. The aim of this paper is to address... -
Measuring the Impact of Readability Features in Fake News Detection
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language... -
Measuring What Counts The case of Rumour Stance Classification
Stance classification can be a powerful tool for understanding whether and which users believe in online rumours. The task aims to automatically predict the stance of replies...-
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