Unfair Inequality in Education: A Benchmark for AI-Fairness Research

Unfair Inequality in Education: A Benchmark for AI-Fairness Research This is the repository for the code and dataset of the paper intitled "Unfair Inequality in Education: A Benchmark for AI-Fairness Research" submitted to the DEMO track of the 27TH European Conference on Artificial Intell...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Giovanelli, Joseph, Magnini, Matteo, James, Liam, Ciatto, Giovanni, Marrero, Angel S., Borghesi, Andrea, Marrero, Gustavo A., Calegari, Roberta
Format: Dataset
Sprache:eng
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Giovanelli, Joseph
Magnini, Matteo
James, Liam
Ciatto, Giovanni
Marrero, Angel S.
Borghesi, Andrea
Marrero, Gustavo A.
Calegari, Roberta
description Unfair Inequality in Education: A Benchmark for AI-Fairness Research This is the repository for the code and dataset of the paper intitled "Unfair Inequality in Education: A Benchmark for AI-Fairness Research" submitted to the DEMO track of the 27TH European Conference on Artificial Intelligence (ECAI). Abstract This paper proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students. Structure benchmark contains: the proposed dataset (dataset.csv), and the mask for dealing with missing values (missing_mask.csv). raw_data includes: the original dataset (original.csv), and the intermediate stages of the pre-processing pipeline (split and pre_processed). res contains the documentation, including: the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (meta_data_mapping.csv), and the value type and domains of each column of the original, intermediate-stage, and proposed datasets (rispectively, meta_data_original.json and meta_data_merged.json and meta_data_final.json). src contains the source code for running the pre-processing and corresponding analysis: pre_processing and statscontain the code for the two corresponding tasks, and pre_processing.py and split.py are two entry points. Finally, Dockerfile and requirements.txt set up the environment for running the applications across multiple platforms and with Python, respectively.
doi_str_mv 10.5281/zenodo.11171862
format Dataset
fullrecord <record><control><sourceid>datacite_PQ8</sourceid><recordid>TN_cdi_datacite_primary_10_5281_zenodo_11171862</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_5281_zenodo_11171862</sourcerecordid><originalsourceid>FETCH-datacite_primary_10_5281_zenodo_111718623</originalsourceid><addsrcrecordid>eNqVzr0OgjAUhuEuDkadXc8N8FMMStxQIbIanZuTcgiN2GpbBrx6MeoFOH3T--VhbMnjME0yHj1Jm9qEnPMNz9bJlB0uukFlodL06LFTfgCloah7iV4ZvYUcdqRle0N7hcZYyKugHANNzsGJHKGV7ZxNGuwcLb47Y1FZnPfHoEaPUnkSd6vGh0HwWLwd4uMQP8fq_-IFKI9B5w</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>dataset</recordtype></control><display><type>dataset</type><title>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</title><source>DataCite</source><creator>Giovanelli, Joseph ; Magnini, Matteo ; James, Liam ; Ciatto, Giovanni ; Marrero, Angel S. ; Borghesi, Andrea ; Marrero, Gustavo A. ; Calegari, Roberta</creator><creatorcontrib>Giovanelli, Joseph ; Magnini, Matteo ; James, Liam ; Ciatto, Giovanni ; Marrero, Angel S. ; Borghesi, Andrea ; Marrero, Gustavo A. ; Calegari, Roberta</creatorcontrib><description>Unfair Inequality in Education: A Benchmark for AI-Fairness Research This is the repository for the code and dataset of the paper intitled "Unfair Inequality in Education: A Benchmark for AI-Fairness Research" submitted to the DEMO track of the 27TH European Conference on Artificial Intelligence (ECAI). Abstract This paper proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students. Structure benchmark contains: the proposed dataset (dataset.csv), and the mask for dealing with missing values (missing_mask.csv). raw_data includes: the original dataset (original.csv), and the intermediate stages of the pre-processing pipeline (split and pre_processed). res contains the documentation, including: the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (meta_data_mapping.csv), and the value type and domains of each column of the original, intermediate-stage, and proposed datasets (rispectively, meta_data_original.json and meta_data_merged.json and meta_data_final.json). src contains the source code for running the pre-processing and corresponding analysis: pre_processing and statscontain the code for the two corresponding tasks, and pre_processing.py and split.py are two entry points. Finally, Dockerfile and requirements.txt set up the environment for running the applications across multiple platforms and with Python, respectively.</description><identifier>DOI: 10.5281/zenodo.11171862</identifier><language>eng</language><publisher>Zenodo</publisher><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0003-0093-9571 ; 0009-0001-7809-7514 ; 0000-0002-2298-2944 ; 0000-0001-9990-420X ; 0000-0003-3794-2942 ; 0000-0002-1841-8996 ; 0000-0003-4030-0078 ; 0000-0002-0990-3893</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>776,1888</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.5281/zenodo.11171862$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Giovanelli, Joseph</creatorcontrib><creatorcontrib>Magnini, Matteo</creatorcontrib><creatorcontrib>James, Liam</creatorcontrib><creatorcontrib>Ciatto, Giovanni</creatorcontrib><creatorcontrib>Marrero, Angel S.</creatorcontrib><creatorcontrib>Borghesi, Andrea</creatorcontrib><creatorcontrib>Marrero, Gustavo A.</creatorcontrib><creatorcontrib>Calegari, Roberta</creatorcontrib><title>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</title><description>Unfair Inequality in Education: A Benchmark for AI-Fairness Research This is the repository for the code and dataset of the paper intitled "Unfair Inequality in Education: A Benchmark for AI-Fairness Research" submitted to the DEMO track of the 27TH European Conference on Artificial Intelligence (ECAI). Abstract This paper proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students. Structure benchmark contains: the proposed dataset (dataset.csv), and the mask for dealing with missing values (missing_mask.csv). raw_data includes: the original dataset (original.csv), and the intermediate stages of the pre-processing pipeline (split and pre_processed). res contains the documentation, including: the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (meta_data_mapping.csv), and the value type and domains of each column of the original, intermediate-stage, and proposed datasets (rispectively, meta_data_original.json and meta_data_merged.json and meta_data_final.json). src contains the source code for running the pre-processing and corresponding analysis: pre_processing and statscontain the code for the two corresponding tasks, and pre_processing.py and split.py are two entry points. Finally, Dockerfile and requirements.txt set up the environment for running the applications across multiple platforms and with Python, respectively.</description><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2024</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNqVzr0OgjAUhuEuDkadXc8N8FMMStxQIbIanZuTcgiN2GpbBrx6MeoFOH3T--VhbMnjME0yHj1Jm9qEnPMNz9bJlB0uukFlodL06LFTfgCloah7iV4ZvYUcdqRle0N7hcZYyKugHANNzsGJHKGV7ZxNGuwcLb47Y1FZnPfHoEaPUnkSd6vGh0HwWLwd4uMQP8fq_-IFKI9B5w</recordid><startdate>20240509</startdate><enddate>20240509</enddate><creator>Giovanelli, Joseph</creator><creator>Magnini, Matteo</creator><creator>James, Liam</creator><creator>Ciatto, Giovanni</creator><creator>Marrero, Angel S.</creator><creator>Borghesi, Andrea</creator><creator>Marrero, Gustavo A.</creator><creator>Calegari, Roberta</creator><general>Zenodo</general><scope>DYCCY</scope><scope>PQ8</scope><orcidid>https://orcid.org/0000-0003-0093-9571</orcidid><orcidid>https://orcid.org/0009-0001-7809-7514</orcidid><orcidid>https://orcid.org/0000-0002-2298-2944</orcidid><orcidid>https://orcid.org/0000-0001-9990-420X</orcidid><orcidid>https://orcid.org/0000-0003-3794-2942</orcidid><orcidid>https://orcid.org/0000-0002-1841-8996</orcidid><orcidid>https://orcid.org/0000-0003-4030-0078</orcidid><orcidid>https://orcid.org/0000-0002-0990-3893</orcidid></search><sort><creationdate>20240509</creationdate><title>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</title><author>Giovanelli, Joseph ; Magnini, Matteo ; James, Liam ; Ciatto, Giovanni ; Marrero, Angel S. ; Borghesi, Andrea ; Marrero, Gustavo A. ; Calegari, Roberta</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_5281_zenodo_111718623</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Giovanelli, Joseph</creatorcontrib><creatorcontrib>Magnini, Matteo</creatorcontrib><creatorcontrib>James, Liam</creatorcontrib><creatorcontrib>Ciatto, Giovanni</creatorcontrib><creatorcontrib>Marrero, Angel S.</creatorcontrib><creatorcontrib>Borghesi, Andrea</creatorcontrib><creatorcontrib>Marrero, Gustavo A.</creatorcontrib><creatorcontrib>Calegari, Roberta</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Giovanelli, Joseph</au><au>Magnini, Matteo</au><au>James, Liam</au><au>Ciatto, Giovanni</au><au>Marrero, Angel S.</au><au>Borghesi, Andrea</au><au>Marrero, Gustavo A.</au><au>Calegari, Roberta</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</title><date>2024-05-09</date><risdate>2024</risdate><abstract>Unfair Inequality in Education: A Benchmark for AI-Fairness Research This is the repository for the code and dataset of the paper intitled "Unfair Inequality in Education: A Benchmark for AI-Fairness Research" submitted to the DEMO track of the 27TH European Conference on Artificial Intelligence (ECAI). Abstract This paper proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students. Structure benchmark contains: the proposed dataset (dataset.csv), and the mask for dealing with missing values (missing_mask.csv). raw_data includes: the original dataset (original.csv), and the intermediate stages of the pre-processing pipeline (split and pre_processed). res contains the documentation, including: the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (meta_data_mapping.csv), and the value type and domains of each column of the original, intermediate-stage, and proposed datasets (rispectively, meta_data_original.json and meta_data_merged.json and meta_data_final.json). src contains the source code for running the pre-processing and corresponding analysis: pre_processing and statscontain the code for the two corresponding tasks, and pre_processing.py and split.py are two entry points. Finally, Dockerfile and requirements.txt set up the environment for running the applications across multiple platforms and with Python, respectively.</abstract><pub>Zenodo</pub><doi>10.5281/zenodo.11171862</doi><orcidid>https://orcid.org/0000-0003-0093-9571</orcidid><orcidid>https://orcid.org/0009-0001-7809-7514</orcidid><orcidid>https://orcid.org/0000-0002-2298-2944</orcidid><orcidid>https://orcid.org/0000-0001-9990-420X</orcidid><orcidid>https://orcid.org/0000-0003-3794-2942</orcidid><orcidid>https://orcid.org/0000-0002-1841-8996</orcidid><orcidid>https://orcid.org/0000-0003-4030-0078</orcidid><orcidid>https://orcid.org/0000-0002-0990-3893</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.5281/zenodo.11171862
ispartof
issn
language eng
recordid cdi_datacite_primary_10_5281_zenodo_11171862
source DataCite
title Unfair Inequality in Education: A Benchmark for AI-Fairness Research
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-19T10%3A47%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-datacite_PQ8&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=unknown&rft.au=Giovanelli,%20Joseph&rft.date=2024-05-09&rft_id=info:doi/10.5281/zenodo.11171862&rft_dat=%3Cdatacite_PQ8%3E10_5281_zenodo_11171862%3C/datacite_PQ8%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true