Recommendations with Responsibility Constraints

Dear Colleagues,

AI promises to bring significant improvements in people’s lives, accelerating knowledge discovery and innovation. However, lately, there is an increasing concern regarding the lack of diversity (leading to exclusion), fairness (leading to discrimination), and transparency (leading to opacity) of decision-making algorithms, such as recommender systems (RS), raising a call for responsible design systems. This Special Issue on “Recommendations with Responsibility Constraints” focuses on advancing methods and algorithms that promote fairness and transparency in recommender systems. In general, fairness is a broad term, and typically means the fair (non-discriminating, equal, proportional, etc.) allocation of some resources (recommendation utility, exposure, etc.), while explanations make AI systems more transparent and trustworthy.

Topics of interest include, but are not limited to, the following topics related to algorithms: 

• Fairness-aware recommendations;
• Fairness-aware explanations for recommendations;
• Unfairness discovery in recommender systems;
• Fairness assessment in recommender systems;
• Fairness correction in recommender systems;
• Intent-aware recommendations and explanations;
• Explanations of recommendations;
• Counterfactual explanations for recommendations;
• Explanation-based fairness audit for recommendations;
• Explanation-driven fairness by design;
• Interactive explanations for recommendations;
• Guidelines for trustworthy, explainable recommender systems;
• Auditing for fairness based on explanations in recommender systems;
• Explanations via visualization in recommender systems;
• Fairness for different stakeholders in recommender systems;
• Interactive explanations for providing feedback on recommendations. 

Dr. Kostas Stefanidis
Guest Editor

Manuscript Submission Information

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• fairness-aware recommendations
• fairness-aware explanations for recommendations
• unfairness discovery in recommender systems
• fairness assessment in recommender systems
• fairness correction in recommender systems
• intent-aware recommendations and explanations
• explanations of recommendations
• counterfactual explanations for recommendations
• explanation-based fairness audit for recommendations
• explanation-driven fairness by design
• interactive explanations for recommendations
• guidelines for trustworthy, explainable recommender systems
• auditing for fairness based on explanations in recommender systems
• explanations via visualization in recommender systems
• fairness for different stakeholders in recommender systems
• interactive explanations for providing feedback on recommendations