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Found 7 papers, 2 papers with code
Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms
Our paper intends to serve as a guidance for discussions within the fair ML community to prevent or reduce the misuse of fairness metrics, and thus reduce overall harm from ML applications.
Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)
We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 0% "trivial" and 11. 5% "impossible" images (beyond label errors).
Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond)
We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 0% ``trivial'' and 11. 5% ``impossible'' images (beyond label errors).
ImageNet suffers from dichotomous data difficulty
We find that the ImageNet validation set suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 3% "trivial" and 11. 3% "impossible" images.
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Here we introduce trial-by-trial error consistency, a quantitative analysis for measuring whether two decision making systems systematically make errors on the same inputs.
Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research
For this purpose, we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR, and ICML of the last 5 years - almost 11, 000 papers in total.
Perceiving the arrow of time in autoregressive motion
We employ a so-called frozen noise paradigm enabling us to compare human performance with four different algorithms on a trial-by-trial basis: A causal inference algorithm exploiting the dependence structure of additive noise terms, a neurally inspired network, a Bayesian ideal observer model as well as a simple heuristic.
