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master's thesis
Detekcija i uklanjanje pristranosti iz podataka za uvježbavanje
2019. urn:nbn:hr:166:470361
Šušnjara, Sara
University of Split
Faculty of Science
Department of Informatics

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Šušnjara, S. (2019). Detekcija i uklanjanje pristranosti iz podataka za uvježbavanje (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:470361

Šušnjara, Sara. "Detekcija i uklanjanje pristranosti iz podataka za uvježbavanje." Master's thesis, University of Split, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:166:470361

Šušnjara, Sara. "Detekcija i uklanjanje pristranosti iz podataka za uvježbavanje." Master's thesis, University of Split, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:166:470361

Šušnjara, S. (2019). 'Detekcija i uklanjanje pristranosti iz podataka za uvježbavanje', Master's thesis, University of Split, Faculty of Science, accessed 19 April 2024, https://urn.nsk.hr/urn:nbn:hr:166:470361

Šušnjara S. Detekcija i uklanjanje pristranosti iz podataka za uvježbavanje [Master's thesis]. Split: University of Split, Faculty of Science; 2019 [cited 2024 April 19] Available at: https://urn.nsk.hr/urn:nbn:hr:166:470361

S. Šušnjara, "Detekcija i uklanjanje pristranosti iz podataka za uvježbavanje", Master's thesis, University of Split, Faculty of Science, Split, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:166:470361

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Metadata
TitleDetekcija i uklanjanje pristranosti iz podataka za uvježbavanje
Title (english)Detecting and mitigating bias in training datasets
AuthorSara Šušnjara
MentorSaša Mladenović (mentor)
Committee memberSaša Mladenović (predsjednik povjerenstva)
Committee memberDivna Krpan (član povjerenstva)
Committee memberGoran Zaharija (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Informatics)
Split
Defense date and country2019-09-25, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Veliki porast mogućnosti i sve veća prisutnost sustava utemeljenih na AI-u u životima ljudi postavljaju pitanja o utjecaju, upravljanju, etici i odgovornosti ovih tehnologija. Sustavi temeljeni na strojnom učenju donose odluke koje nekada direktno utječu na ljudske sudbine. Iako bi neživi sustavi trebali biti nepristrani, praksa je pokazala da algoritmi često diskriminirajuće tretiraju određene skupine. Računala donose odluke na temelju naučenog, a uče iz podataka koje im priloži čovjek. Pojava diskriminacije u podacima naziva se pristranost (engl. Bias). Ukloniti u potpunosti pristranost iz podataka nije moguće, stoga je potrebno u fazi razvoja i produkciji modela strojnog učenja obratiti posebnu pažnju na detekciju, vizualizaciju i umanjivanje pristranosti. U sklopu ovog rada testirana je XAI biblioteka za programski jezik Python kroz izradu i korištenje web aplikacije testirane na podacima vezanim za odobrenje kredita s ciljem provjere upotrebljivosti.
Abstract (english)
The vast increase in capabilities and the increasing presence of AI-based systems in people's lives raise questions about the impact, governance, ethics and responsibilities of these technologies. Machine-based learning systems make decisions that may directly affect human destinies. Although non-living systems should be impartial, practice has shown that algorithms often discriminate against certain groups. Computers make decisions based on what they have learned, and they learn from the information served by humans. The occurrence of discrimination in data is called bias. It is not possible to completely eliminate bias from the data, so special attention should be paid to detecting, visualizing and reducing bias in the development and production of machine learning models with the goal of making experts aware of the potential consequences. As part of this work, the XAI library for the Python programming language was tested through the creation and use of a web application tested on credit approval data for usability testing.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:470361
Study programmeTitle: Computer Science; specializations in: major in teaching
Course: major in teaching
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra edukacije informatike (magistar/magistra edukacije informatike)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2019-11-25 09:27:42