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Found 16 papers, 2 papers with code
Flexible text generation for counterfactual fairness probing
A common approach for testing fairness issues in text-based classifiers is through the use of counterfactuals: does the classifier output change if a sensitive attribute in the input is changed?
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks.
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective
In this work we study the problem of measuring the fairness of a machine learning model under noisy information.
Measuring Recommender System Effects with Simulated Users
Using this simulation framework, we can (a) isolate the effect of the recommender system from the user preferences, and (b) examine how the system performs not just on average for an "average user" but also the extreme experiences under atypical user behavior.
Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding.
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.
Fairness without Demographics through Adversarially Reweighted Learning
Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns.
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems
In addition to the theoretical results, we find on multiple datasets -- including a large-scale real-world recommender system -- that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.
Toward a better trade-off between performance and fairness with kernel-based distribution matching
As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences.
Recommending what video to watch next: a multitask ranking system
In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.
Transfer of Machine Learning Fairness across Domains
A model trained for one setting may be picked up and used in many others, particularly as is common with pre-training and cloud APIs.
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues.
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data.
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group?
Why Are You More Engaged? Predicting Social Engagement from Word Use
We present a study to analyze how word use can predict social engagement behaviors such as replies and retweets in Twitter.
