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Found 30 papers, 14 papers with code
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces
Finally, we assessed the effectiveness of data augmentation approaches on synthetic and authentic data, with the same goal in mind.
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation
When devising recommendation services, it is important to account for the interests of all content providers, encompassing not only newcomers but also minority demographic groups.
Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
Faithful Path Language Modelling for Explainable Recommendation over Knowledge Graph
Path reasoning methods over knowledge graphs have gained popularity for their potential to improve transparency in recommender systems.
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider.
(Un)fair Exposure in Deep Face Rankings at a Distance
Law enforcement regularly faces the challenge of ranking suspects from their facial images.
GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning
Moreover, an empirical evaluation of the perturbed network uncovered relevant patterns that justify the nature of the unfairness discovered by the generated explanations.
Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation
Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG).
Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design
Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency.
Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning
This weakness is one of the main factors undermining users' trust, since model predictions could for instance lead an instructor to not intervene on a student in need.
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
Time series is the most prevalent form of input data for educational prediction tasks.
The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds
Commonly, the recognition threshold of a face recognition system is adjusted based on the degree of security for the considered use case.
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality
However, the existing explainable recommendation approaches based on KG merely optimize the selected reasoning paths for product relevance, without considering any user-level property of the paths for explanation.
Explaining Bias in Deep Face Recognition via Image Characteristics
In this paper, we propose a novel explanatory framework aimed to provide a better understanding of how face recognition models perform as the underlying data characteristics (protected attributes: gender, ethnicity, age; non-protected attributes: facial hair, makeup, accessories, face orientation and occlusion, image distortion, emotions) on which they are tested change.
Generalisable Methods for Early Prediction in Interactive Simulations for Education
In this paper, we investigate the quality and generalisability of models for an early prediction of conceptual understanding based on clickstream data of students across interactive simulations.
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs
Neural networks are ubiquitous in applied machine learning for education.
Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education
In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can support the science and engineering of intelligent educational applications.
Meta Transfer Learning for Early Success Prediction in MOOCs
Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates.
Dictionary Attacks on Speaker Verification
In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance.
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e. g., movie "x" starred by actress "y" recommended to a user because that user watched other movies with "y" as an actress).
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Regulating Group Exposure for Item Providers in Recommendation
Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working.
Robust Reputation Independence in Ranking Systems for Multiple Sensitive Attributes
A notable example is represented by reputation-based ranking systems, a class of systems that rely on users' reputation to generate a non-personalized item-ranking, proved to be biased against certain demographic classes.
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
In this paper, we conduct a systematic analysis of mitigation procedures against consumer unfairness in rating prediction and top-n recommendation tasks.
Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation
We characterize the recommendations of representative algorithms by means of the proposed metrics, and we show that the item probability of being recommended and the item true positive rate are biased against the item popularity.
ECIR 2020 Workshops: Assessing the Impact of Going Online
In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organizers and the workshop participants.
