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1.
Adaptive boosting method for mitigating ethnicity and age group unfairness
Ivona Colakovic, Sašo Karakatič, 2024, original scientific article

Abstract: Machine learning algorithms make decisions in various fields, thus influencing people’s lives. However, despite their good quality, they can be unfair to certain demographic groups, perpetuating socially induced biases. Therefore, this paper deals with a common unfairness problem, unequal quality of service, that appears in classification when age and ethnicity groups are used. To tackle this issue, we propose an adaptive boosting algorithm that aims to mitigate the existing unfairness in data. The proposed method is based on the AdaBoost algorithm but incorporates fairness in the calculation of the instance’s weight with the goal of making the prediction as good as possible for all ages and ethnicities. The results show that the proposed method increases the fairness of age and ethnicity groups while maintaining good overall quality compared to traditional classification algorithms. The proposed method achieves the best accuracy in almost every sensitive feature group. Based on the extensive analysis of the results, we found that when it comes to ethnicity, interestingly, White people are likely to be incorrectly classified as not being heroin users, whereas other groups are likely to be incorrectly classified as heroin users.
Keywords: fairness, boosting, machine learning, classification
Published in DKUM: 24.05.2024; Views: 103; Downloads: 5
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Improved Boosted Classification to Mitigate the Ethnicity and Age Group Unfairness
Ivona Colakovic, Sašo Karakatič, 2022, published scientific conference contribution

Abstract: This paper deals with the group fairness issue that arises when classifying data, which contains socially induced biases for age and ethnicity. To tackle the unfair focus on certain age and ethnicity groups, we propose an adaptive boosting method that balances the fair treatment of all groups. The proposed approach builds upon the AdaBoost method but supplements it with the factor of fairness between the sensitive groups. The results show that the proposed method focuses more on the age and ethnicity groups, given less focus with traditional classification techniques. Thus the resulting classification model is more balanced, treating all of the sensitive groups more equally without sacrificing the overall quality of the classification.
Keywords: fairness, classification, boosting, machine learning
Published in DKUM: 02.08.2023; Views: 402; Downloads: 29
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4.
Economic effects of renewable energy technologies
Dario Maradin, Ljerka Cerović, Trina Mjeda, 2017, original scientific article

Abstract: Rapid economic development has resulted in the more frequent use of renewable energy technologies. On the other hand, the production and use of renewables fosters the development of new technologies, creating many new opportunities for entrepreneurial-minded individuals and, consequently, the economy in general. Renewable energy technologies have a multiplier effect in spurring the economy and the development of not only the energy sector but also all the supporting activities related to such industry. The purpose of this research is to analyse both the positive and the negative economic effects of investing in various renewable technologies, as well as to confirm, by means of the energy-economy model, the benefit of such technologies in boosting the economy.
Keywords: renewable energy sources, new technologies, boosting the economy
Published in DKUM: 13.11.2017; Views: 1611; Downloads: 452
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