Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction
Research into novel approaches like Machine Learning (ML) promotes a new set of opportunities for sustainable development of applications through automation. However, there are certain ML tasks which are prone to spurious classification, mainly due to the bias in legacy data. One well-known and high...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10759097/ |
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| author | Raik Orbay Evelina Wikner |
| author_facet | Raik Orbay Evelina Wikner |
| author_sort | Raik Orbay |
| collection | DOAJ |
| description | Research into novel approaches like Machine Learning (ML) promotes a new set of opportunities for sustainable development of applications through automation. However, there are certain ML tasks which are prone to spurious classification, mainly due to the bias in legacy data. One well-known and highly actual misclassification case concerns gender. As the vast dataset for engineering rules, standards and experiments are based on men, a bias towards women is the subject of research. Accordingly, any bias should be contained before the algorithms are deployed to the service of the sustainable society. There is a substantial amount of data on ML gender-bias in the literature. In these, the majority of the investigated cases are for ML branches like image or sound processing and text recognition. However, utilizing ML for driving style investigations is not an extensively researched area. In this work, a novel application for gender-based classification with bias-attenuation using anonymized driving data will be presented. Using data devoid of biometric and geographic information, the proposed pipeline distinguishes manifested binary genders with 80% accuracy for the drivers in the holdout data set. In addition, a method for sustainable algorithm selection and its extension to embedded applications, is proposed. An investigation into the environmental burden of seven different types of ML algorithms was conducted and the popular neural network algorithm had the highest environmental burden. |
| format | Article |
| id | doaj-art-e30186a919ce468d915d8d56021b0600 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-e30186a919ce468d915d8d56021b06002025-01-24T00:02:19ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-01634835810.1109/OJVT.2024.350292110759097Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated PredictionRaik Orbay0https://orcid.org/0000-0002-0339-1807Evelina Wikner1https://orcid.org/0000-0002-7203-6243Volvo Car Corporation - 97100 Propulsion and Energy - Strategy & Execution, Gothenburg, SwedenDepartment of Electrical Engineering, Chalmers Technology University, Gothenburg, SwedenResearch into novel approaches like Machine Learning (ML) promotes a new set of opportunities for sustainable development of applications through automation. However, there are certain ML tasks which are prone to spurious classification, mainly due to the bias in legacy data. One well-known and highly actual misclassification case concerns gender. As the vast dataset for engineering rules, standards and experiments are based on men, a bias towards women is the subject of research. Accordingly, any bias should be contained before the algorithms are deployed to the service of the sustainable society. There is a substantial amount of data on ML gender-bias in the literature. In these, the majority of the investigated cases are for ML branches like image or sound processing and text recognition. However, utilizing ML for driving style investigations is not an extensively researched area. In this work, a novel application for gender-based classification with bias-attenuation using anonymized driving data will be presented. Using data devoid of biometric and geographic information, the proposed pipeline distinguishes manifested binary genders with 80% accuracy for the drivers in the holdout data set. In addition, a method for sustainable algorithm selection and its extension to embedded applications, is proposed. An investigation into the environmental burden of seven different types of ML algorithms was conducted and the popular neural network algorithm had the highest environmental burden.https://ieeexplore.ieee.org/document/10759097/Battery electric vehiclesdriving style classificationenergy consumptionfeature engineeringmachine learningunsupervised/supervised learning |
| spellingShingle | Raik Orbay Evelina Wikner Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction IEEE Open Journal of Vehicular Technology Battery electric vehicles driving style classification energy consumption feature engineering machine learning unsupervised/supervised learning |
| title | Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction |
| title_full | Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction |
| title_fullStr | Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction |
| title_full_unstemmed | Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction |
| title_short | Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction |
| title_sort | sustainable selection of machine learning algorithm for gender bias attenuated prediction |
| topic | Battery electric vehicles driving style classification energy consumption feature engineering machine learning unsupervised/supervised learning |
| url | https://ieeexplore.ieee.org/document/10759097/ |
| work_keys_str_mv | AT raikorbay sustainableselectionofmachinelearningalgorithmforgenderbiasattenuatedprediction AT evelinawikner sustainableselectionofmachinelearningalgorithmforgenderbiasattenuatedprediction |