Dataset

This dataset was created by Alex Stevenson

Contents

Dataset

This dataset was created by KESHAV GOEL 123

Contents

The size and share of the market is categorized based on Type (Architectural Concrete, Exposed Aggregate Concrete, Pigmented Concrete) and Application (Commercial Buildings, Residential Buildings, Infrastructure Projects, Landscaping) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Historical price and volatility data for Fairface in Chinese Yuan Renminbi across different time periods.

Historical price and volatility data for Fairface in Polish Zloty across different time periods.

This replication package contains datasets and scripts related to the paper: "*How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study*"

Root directory

• statistics.r: R script used to compute the correlation between usage and downloads, and the RQ1/RQ2 inter-rater agreements
• modelsInfo.zip: zip file containing all the downloaded model cards (in JSON format)
• script: directory containing all the scripts used to collect and process data. For further details, see README file inside the script directory.

Dataset

• Dataset/Dataset_HF-models-list.csv: list of HF models analyzed
• Dataset/Dataset_github-prj-list.txt: list of GitHub projects using the transformers library
• Dataset/Dataset_github-Prj_model-Used.csv: contains usage pairs: project, model
• Dataset/Dataset_prj-num-models-reused.csv: number of models used by each GitHub project
• Dataset/Dataset_model-download_num-prj_correlation.csv contains, for each model used by GitHub projects: the name, the task, the number of reusing projects, and the number of downloads

RQ1

• RQ1/RQ1_dataset-list.txt: list of HF datasets
• RQ1/RQ1_datasetSample.csv: sample set of models used for the manual analysis of datasets
• RQ1/RQ1_analyzeDatasetTags.py: Python script to analyze model tags for the presence of datasets. it requires to unzip the modelsInfo.zip in a directory with the same name (modelsInfo) at the root of the replication package folder. Produces the output to stdout. To redirect in a file fo be analyzed by the RQ2/countDataset.py script
• RQ1/RQ1_countDataset.py: given the output of RQ2/analyzeDatasetTags.py (passed as argument) produces, for each model, a list of Booleans indicating whether (i) the model only declares HF datasets, (ii) the model only declares external datasets, (iii) the model declares both, and (iv) the model is part of the sample for the manual analysis
• RQ1/RQ1_datasetTags.csv: output of RQ2/analyzeDatasetTags.py
• RQ1/RQ1_dataset_usage_count.csv: output of RQ2/countDataset.py

RQ2

• RQ2/tableBias.pdf: table detailing the number of occurrences of different types of bias by model Task
• RQ2/RQ2_bias_classification_sheet.csv: results of the manual labeling
• RQ2/RQ2_isBiased.csv: file to compute the inter-rater agreement of whether or not a model documents Bias
• RQ2/RQ2_biasAgrLabels.csv: file to compute the inter-rater agreement related to bias categories
• RQ2/RQ2_final_bias_categories_with_levels.csv: for each model in the sample, this file lists (i) the bias leaf category, (ii) the first-level category, and (iii) the intermediate category

RQ3

• RQ3/RQ3_LicenseValidation.csv: manual validation of a sample of licenses
• RQ3/RQ3_{NETWORK-RESTRICTIVE|RESTRICTIVE|WEAK-RESTRICTIVE|PERMISSIVE}-license-list.txt: lists of licenses with different permissiveness
• RQ3/RQ3_prjs_license.csv: for each project linked to models, among other fields it indicates the license tag and name
• RQ3/RQ3_models_license.csv: for each model, indicates among other pieces of info, whether the model has a license, and if yes what kind of license
• RQ3/RQ3_model-prj-license_contingency_table.csv: usage contingency table between projects' licenses (columns) and models' licenses (rows)
• RQ3/RQ3_models_prjs_licenses_with_type.csv: pairs project-model, with their respective licenses and permissiveness level

scripts

Contains the scripts used to mine Hugging Face and GitHub. Details are in the enclosed README

Historical price and volatility data for Fairface in US Dollar across different time periods.

Historical price and volatility data for Fairface in Euro across different time periods.

FairJob: A Real-World Dataset for Fairness in Online Systems

  Summary

This dataset is released by Criteo to foster research and innovation on Fairness in Advertising and AI systems in general. See also Criteo pledge for Fairness in Advertising. The dataset is intended to learn click predictions models and evaluate by how much their predictions are biased between different gender groups. The associated paper is available at Vladimirova et al. 2024.… See the full description on the dataset page: https://huggingface.co/datasets/criteo/FairJob.

Pretty face is a book. It was written by Mary Hogan and published by Simon&Schuster in 2008.

There is increasing interest in clarifying how different face emotion expressions are perceived by people from different cultures, of different ages and sex. However, scant availability of well-controlled emotional face stimuli from non-Western populations limit the evaluation of cultural differences in face emotion perception and how this might be modulated by age and sex differences. We present a database of East Asian face expression stimuli, enacted by young and older, male and female, Taiwanese using the Facial Action Coding System (FACS). Combined with a prior database, this present database consists of 90 identities with happy, sad, angry, fearful, disgusted, surprised and neutral expressions amounting to 628 photographs. Twenty young and 24 older East Asian raters scored the photographs for intensities of multiple-dimensions of emotions and induced affect. Multivariate analyses characterized the dimensionality of perceived emotions and quantified effects of age and sex. We also applied commercial software to extract computer-based metrics of emotions in photographs. Taiwanese raters perceived happy faces as one category, sad, angry, and disgusted expressions as one category, and fearful and surprised expressions as one category. Younger females were more sensitive to face emotions than younger males. Whereas, older males showed reduced face emotion sensitivity, older female sensitivity was similar or accentuated relative to young females. Commercial software dissociated six emotions according to the FACS demonstrating that defining visual features were present. Our findings show that East Asians perceive a different dimensionality of emotions than Western-based definitions in face recognition software, regardless of age and sex. Critically, stimuli with detailed cultural norms are indispensable in interpreting neural and behavioral responses involving human facial expression processing. To this end, we add to the tools, which are available upon request, for conducting such research.

CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including - 10,177 number of identities, - 202,599 number of face images, and - 5 landmark locations, 40 binary attributes annotations per image.

The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face detection, and landmark (or facial part) localization.

Note: CelebA dataset may contain potential bias. The fairness indicators example goes into detail about several considerations to keep in mind while using the CelebA dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('celeb_a', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/celeb_a-2.1.0.png" alt="Visualization" width="500px">

Historical price and volatility data for Fairface in Vietnamese Dong across different time periods.

Historical price and volatility data for Euro in Fairface across different time periods.

Historical price and volatility data for Fairface in Chinese Yuan Renminbi across different time periods.

Preference for beauty is human nature, as previous behavior studies have supported the notion of “beauty premium” in which attractive people were more easily to get promoted and receive higher salaries. In the present study, 29 males were recruited to participate in a three-person ultimatum game (UG) including a proposer, a responder and a powerless third player. Each subject, playing as the responder, had to decide whether to accept an offer from the allocator both for himself and a female third person. We aimed to elucidate how the facial attractiveness of the female subject affected the male subjects’ fairness and decision-making in social exchanges. Frontal feedback-related negativity (FRN) in response to four offers in an attractive-face condition revealed no significant differences between offers; however, when the companion was an unattractive female, an “unfair/fair” offer, which assigned a lower share to the responder and a fair share to the third player, elicited the largest FRN. Furthermore, when the third player was offered the smallest amount (“fair/unfair” offer), a larger FRN was generated in an attractive-face condition than unattractive-face condition. In the “unfair/fair” offer condition in which subjects received a smaller allocation than the third person, the beauty of their female counterparts attenuated subjects’ aversion to inequality, resulting in a less negative FRN in the frontal region and an increased acceptance ratio. However, the influence of the third player’s facial attractiveness only affected the early evaluation stage: late P300 was found to be immune to the “beauty premium”. Under the two face conditions, P300 was smallest following an “unfair/fair” offer, whereas the amplitudes in the other three offer conditions exhibited no significant differences. In addition, the differentiated neural features of processing facial attractiveness were also determined and indexed by four event-related potentials (ERP) components: N170, frontal N1, N2 and late positive potentials (LPPs).

Historical price and volatility data for Vietnamese Dong in Fairface across different time periods.

Historical price and volatility data for Chinese Yuan Renminbi in Fairface across different time periods.

Historical price and volatility data for US Dollar in Fairface across different time periods.

Historical price and volatility data for Fairface in UKrainian Hryvnia across different time periods.