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Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction

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Název: Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction
Autoři: Orbay, Raik, 1974, Wikner, Evelina, 1987
Zdroj: IEEE Open Journal of Vehicular Technology. 6:348-358
Témata: Feature engineering, Energy consumption, Battery electric vehicles, Unsupervised/supervised learning, Driving style classification, Machine learning
Popis: 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.
Popis souboru: electronic
Přístupová URL adresa: https://research.chalmers.se/publication/543996
https://research.chalmers.se/publication/543996/file/543996_Fulltext.pdf
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  Data: Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Orbay%2C+Raik%22">Orbay, Raik</searchLink>, 1974<br /><searchLink fieldCode="AR" term="%22Wikner%2C+Evelina%22">Wikner, Evelina</searchLink>, 1987
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  Data: <i>IEEE Open Journal of Vehicular Technology</i>. 6:348-358
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  Data: <searchLink fieldCode="DE" term="%22Feature+engineering%22">Feature engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Battery+electric+vehicles%22">Battery electric vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Unsupervised%2Fsupervised+learning%22">Unsupervised/supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Driving+style+classification%22">Driving style classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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.
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        Value: 10.1109/OJVT.2024.3502921
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      – Text: English
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        PageCount: 11
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      – SubjectFull: Feature engineering
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Battery electric vehicles
        Type: general
      – SubjectFull: Unsupervised/supervised learning
        Type: general
      – SubjectFull: Driving style classification
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      – SubjectFull: Machine learning
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      – TitleFull: Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction
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              M: 01
              Type: published
              Y: 2025
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