Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
Are algorithms sexist? This is a question that has been frequently appearing in the mass media, and the debate has typically been far from a scientific analysis. This paper aims at answering the question using a hybrid social and technical perspective. First a technical-oriented definition of the al...
| Autores: | ; ; ; ; ; |
|---|---|
| Tipo de documento: | Artículo |
| Estado: | Published version |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/147827 |
| Acceso en línea: | http://hdl.handle.net/10609/147827 https://doi.org/10.3390/a15090303 |
| Nivel de acceso: | Acceso abierto |
| Palabra clave: | algorithmic bias gender bias data science artificial intelligence decision making biaix algorítmic biaix de gènere ciència de dades intel · ligència artificial presa de decision sesgo algorítmico los prejuicios de género ciencia de los datos inteligencia artificial toma de decisiones |
| _version_ | 1878729055508889600 |
|---|---|
| author | Castaneda, Juliana |
| author2 | Jover, Assumpta Calvet Liñán, Laura Yanes, Sergi Juan, Angel A. Sainz, Milagros |
| author2_role | author author author author author |
| author_browse | Calvet Liñán, Laura Castaneda, Juliana Jover, Assumpta Juan, Angel A. Sainz, Milagros Yanes, Sergi |
| author_facet | Castaneda, Juliana Jover, Assumpta Calvet Liñán, Laura Yanes, Sergi Juan, Angel A. Sainz, Milagros |
| author_role | author |
| collection | O2, repositorio institucional de la UOC |
| description | Are algorithms sexist? This is a question that has been frequently appearing in the mass media, and the debate has typically been far from a scientific analysis. This paper aims at answering the question using a hybrid social and technical perspective. First a technical-oriented definition of the algorithm concept is provided, together with a more social-oriented interpretation. Secondly, several related works have been reviewed in order to clarify the state of the art in this matter, as well as to highlight the different perspectives under which the topic has been analyzed. Thirdly, we describe an illustrative numerical example possible discrimination in the banking sector due to data bias, and propose a simple but effective methodology to address it. Finally, a series of recommendations are provided with the goal of minimizing gender bias while designing and using data-algorithmic processes to support decision making in different environments. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_00a38d70729d5abff7d359ef3b23e45a |
| institution | Universitat Oberta de Catalunya (UOC) |
| instname_str | Universitat Oberta de Catalunya (UOC) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:openaccess.uoc.edu:10609/147827 |
| publishDate | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| reponame_str | O2, repositorio institucional de la UOC |
| spelling | Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective Castaneda, Juliana Jover, Assumpta Calvet Liñán, Laura Yanes, Sergi Juan, Angel A. Sainz, Milagros algorithmic bias gender bias data science artificial intelligence decision making biaix algorítmic biaix de gènere ciència de dades intel · ligència artificial presa de decision sesgo algorítmico los prejuicios de género ciencia de los datos inteligencia artificial toma de decisiones Are algorithms sexist? This is a question that has been frequently appearing in the mass media, and the debate has typically been far from a scientific analysis. This paper aims at answering the question using a hybrid social and technical perspective. First a technical-oriented definition of the algorithm concept is provided, together with a more social-oriented interpretation. Secondly, several related works have been reviewed in order to clarify the state of the art in this matter, as well as to highlight the different perspectives under which the topic has been analyzed. Thirdly, we describe an illustrative numerical example possible discrimination in the banking sector due to data bias, and propose a simple but effective methodology to address it. Finally, a series of recommendations are provided with the goal of minimizing gender bias while designing and using data-algorithmic processes to support decision making in different environments. MDPI http://hdl.handle.net/10609/147827 https://doi.org/10.3390/a15090303 |
| spellingShingle | Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective Castaneda, Juliana algorithmic bias gender bias data science artificial intelligence decision making biaix algorítmic biaix de gènere ciència de dades intel · ligència artificial presa de decision sesgo algorítmico los prejuicios de género ciencia de los datos inteligencia artificial toma de decisiones |
| status_str | publishedVersion |
| title | Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective |
| title_full | Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective |
| title_fullStr | Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective |
| title_full_unstemmed | Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective |
| title_short | Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective |
| title_sort | Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective |
| topic | algorithmic bias gender bias data science artificial intelligence decision making biaix algorítmic biaix de gènere ciència de dades intel · ligència artificial presa de decision sesgo algorítmico los prejuicios de género ciencia de los datos inteligencia artificial toma de decisiones |
| topic_facet | algorithmic bias gender bias data science artificial intelligence decision making biaix algorítmic biaix de gènere ciència de dades intel · ligència artificial presa de decision sesgo algorítmico los prejuicios de género ciencia de los datos inteligencia artificial toma de decisiones |
| url | http://hdl.handle.net/10609/147827 https://doi.org/10.3390/a15090303 |