Towards Healthy Engagement with Online Debates: An Investigation of Debate Summaries and Personalized Persuasive Suggestions

Online debates allow for large-scale participation by users with different opinions, values, and backgrounds. While this is beneficial for democratic discourse, such debates often tend to be cognitively demanding due to the high quantity and low quality of non-expert contributions. High cognitive demand, in turn, can make users vulnerable to...

Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices. One approach to mitigate such biases is through interface and interaction design. This survey reviews studies focused on cognitive bias mitigation of recommender system users during two processes: 1) item selection and...

This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias

During online information search, users tend to select search results that confirm previous beliefs and ignore competing possibilities. This systematic pattern in human behavior is known as confirmation bias. In this paper, we study the effect of obfuscation (i.e., hiding the result unless the user clicks on it) with warning labels and the...

Interactive Natural Language Technology for Explainable Artificial Intelligence

We have defined an interdisciplinary program for training a new generation of researchers who will be ready to leverage the use of Artificial Intelligence (AI)-based models and techniques even by non-expert users. The final goal is to make AI self-explaining and thus contribute to translating knowledge into products and services for economic...