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Found 10 papers, 3 papers with code
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness
Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests.
Fair Inference for Discrete Latent Variable Models
It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations.
Framework for Behavioral Disorder Detection Using Machine Learning and Application of Virtual Cognitive Behavioral Therapy in COVID-19 Pandemic
On the other hand, people are stressed, becoming more anxious during COVID-19 pandemic situation and exhibits symptoms of behavioral disorder.
User Acceptance of Gender Stereotypes in Automated Career Recommendations
In other words, our results demonstrate we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans.
Fair Representation Learning for Heterogeneous Information Networks
Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness.
Equitable Allocation of Healthcare Resources with Fair Cox Models
Healthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most languish on waiting lists.
Neural Fair Collaborative Filtering
A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms.
Scalable Collapsed Inference for High-Dimensional Topic Models
In this paper, we develop an online inference algorithm for topic models which leverages stochasticity to scale well in the number of documents, sparsity to scale well in the number of topics, and which operates in the collapsed representation of the topic model for improved accuracy and run-time performance.
Bayesian Modeling of Intersectional Fairness: The Variance of Bias
Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability.
An Intersectional Definition of Fairness
We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens arising from the Humanities literature which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including gender, race, sexual orientation, class, and disability.
