The Internet of tomorrow: Privacy, Accountability, Compliance and Trust

Counterfactual Explanations for Recommenders

A provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item.

Credibility Analysis in News Communities

A probabilistic graphical model to jointly identify credible news articles, trustworthy news sources, and expert users by leveraging joint interactions in a news community.

Credibility Analysis in Health Communities

Assessing trustworthiness of users, objectivity of language, and credibility of user statements in online health communities.

Probabilistic Graphical Models for Credibility Analysis

Probabilistic graphical models to extract "credible", "trustworthy" and "expert" information from large-scale, non-expert, user-generated content in online communities.

Deep Learning based Credibility Analysis

A deep learning based approach for credibility analysis of unstructured textual claims in an open-domain setting with interpretable explanations.

Web Credibility Analysis

A generic approach for credibility analysis of unstructured textual claims in an open-domain setting with interpretable explanations.

Fair Data Representations

This project introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models.

Mediator Accounts

This project proposes a framework which leverages solidarity in a large community to scramble user interaction histories.

Relationships between Users' Actions and Feeds

This project presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users’ actions and items in their social media feeds.

ExFAKT: Explainable Fact Checking

Moving forward towards deriving more human understandable evidence from Knowledge graphs and text based on background knowledge in the form of rules.