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Found 5 papers, 2 papers with code
Reproducibility in Machine Learning-Driven Research
Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce.
A conceptual model for leaving the data-centric approach in machine learning
For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings.
Modelling the long-term fairness dynamics of data-driven targeted help on job seekers
We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers
In this work, we first provide a novel framework to measure the fairness in the retrieved text contents of ranking models.
Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics
Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation.
