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GLocalX-From Local to Global Explanations of Black Box AI Models
Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and...-
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Fair Prediction with Disparate Impact A Study of Bias in Recidivism Predictio...
Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time.... -
Fair Transparent and Accountable Algorithmic Decision making Processes
The Premise, the Proposed Solutions, and the Open Challenges The combination of increased availability of large amounts of fine-grained human behavioral data and advances in... -
Fairer machine learning in the real world
Mitigating discrimination without collecting sensitive data Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used... -
Temporal social network reconstruction using wireless proximity sensors: mode...
The emerging technologies of wearable wireless devices open entirely new ways to record various aspects of human social interactions in a broad range of settings. Such...-
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Algorithmic Decision Making Based on ML from Big Data. Can Transparency Resto...
Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would... -
Algorithmic Decision Making Based on Machine Learning from Big Data
Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would... -
Explaining Image Classifiers Generating Exemplars and Counter-Exemplars from ...
We present an approach to explain the decisions of black-box image classifiers through synthetic exemplar and counter-exemplar learnt in the latent feature space. Our...-
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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... -
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... -
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... -
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...-
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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...-
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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 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... -
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...-
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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... -
Focus Metric
https://github.com/HPAI-BSC/Focus-Metric Implementation of the Focus metric. This metric is able to evaluate explainability methods and quantify their coherency to the task...-
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Visualizing the Results of Boolean Matrix Factorizations
We provide a method to visualize the results of Boolean Matrix Factorization algorithms. Our method can also be used to visualize overlapping clusters in bipartite graphs. The...
