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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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2.
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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3.
Prenos stila slike s pomočjo prenosnega učenja in nevronskih mrež : magistrsko delo
Ivona Colakovic, 2021, master's thesis

Abstract: Hitro razvijajoče področje umetne inteligence se v zadnjih letih integrira v različna področja in tako postaja neizogiben del številnih človeških dejavnosti. Umetna inteligenca je pokazala, da se lahko integrira tudi v področje umetnosti in ustvarja nova umetniška dela a podlagi kopiranja stilov grafičnih del priznanih avtorjev. Nevronske mreže, ki posnamejo delovanje človeških možganov, dodatno pomagajo pri tem postopku, saj omogočajo razpoznavo vzorcev v stilih grafičnih del. V magistrskem delu se osredotočimo na raziskovanje tehnike prenosa stila grafičnih del iz enega na drugo grafično delo s pomočjo nevornskih mrež. V ta namen opišemo sestavne dele nevronskih mrež, podrobneje razložimo konvolucijske nevronske mreže in predstavimo pojem prenosnega učenja. Z namenom boljšeg razumevanja področja prenosa stila ilustracij pregledamo obstoječe raziskave ter opišemo delovanje algoritma za prenos stila. V okviru magistrskega dela prikažemo implementacijo in rezultate eksperimenta skozi katerega smo ugotovili, da pristop prenosa stila lahko uspešno prenaša stil iz ilustracij na fotografije kakor tudi iz ilustracij na druge ilustracije.
Keywords: prenos stila, konvolucijske nevronske mreže, prenosno učenje
Published in DKUM: 18.10.2021; Views: 1033; Downloads: 104
.pdf Full text (3,40 MB)

4.
Mobilna aplikacija za ocenjevanje truda pri agilnih razvojnih metodah : diplomsko delo
Ivona Colakovic, 2019, undergraduate thesis

Abstract: Ocenjevanje truda pri razvoju programske opreme je proces, ki lahko zelo pomaga pri planiranju projekta. V diplomskem delu predstavimo proces ocenjevanja truda, elemente procesa ter aktivnosti, katere se izvajajo. Osredotočamo se na ocenjevanje truda pri agilnem razvoju informacijskih sistemov ter klasificiramo obstoječe metode in pregledamo njihove prednosti in slabosti. Odločimo se za predstavitev pogosto uporabljene metode za ocenjevanje truda pri agilnem razvoju, in sicer Planning poker. Po analizi trga ugotovimo pomanjkljivosti obstoječih rešitev in določimo funkcionalnosti lastne rešitve. Prikažemo arhitekturo lastne rešitve in končni izdelek. Na koncu prikažemo izvedeni eksperiment z mobilno aplikacijo ter rezultate eksperimenta.
Keywords: ocenjevanje truda, agilni razvoj, mobilna aplikacija, Planning poker
Published in DKUM: 25.11.2019; Views: 1484; Downloads: 111
.pdf Full text (916,52 KB)

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