Diskriminerande utfall från maskininlärningsmodeller : En kvalitativ studie av identifierade faktorer och lösningar fördiskriminerande utfall

Sammanfattning: In a world where artificial intelligence and machine learning aregrowing and spreading in society, its impact and consequence forpeople is increasing. The technology is used in services that peopleuse every day. Both privately but also in a commercial context, forexample social media and to identify fraud in the banking sector.Previous studies show that machine learning models can givediscriminatory outcomes when it comes to, among other things,gender and ethnicity. This study aims to investigate how, in systemdevelopment projects where machine learning is used, one works tocounteract discriminatory outcomes. The study examines both thefactors that contribute to the emergence of discriminatoryoutcomes, as well as the solutions that exist to counteract theproblem. The study is conducted at a global IT consultingcompany.To investigate the area, a study, with qualitative researchmethodology, has been conducted. The empirical material has beencollected through six semi-structured interviews. All respondentswho participated in the study work within the same organization, indifferent projects and with varying experiences in the area. Therespondents have been selected through a subjective selectionbased on their experience in the field in relation to the purpose ofthe study.The results of the study show that the decisive factor for theemergence of discrimination is the training data which the modelsare trained with. The majority of solutions to counteractdiscriminatory outcomes have also been identified. The results ofthe study differ to some extent from the previous research done inthe field. Regarding factors, previous research and the results of thestudy agree that data is the decisive factor that contributes todiscriminatory outcomes arising from machine learning models.The main difference among the solutions is that previous researchshows more specific techniques, which are used to identify ormitigate discriminatory outcomes, while the results of the studyshow softer values and almost no specific techniques at all. In theresults of the study, for example, the individual is seen as a centralpart of the process instead of automatic techniques and tools.The study concludes that data is the most decisive factor indiscriminatory outcomes in machine learning models. The modelsare not discriminatory in themselves, they only reflect the trainingdata. If the data contains discrimination, the model will learn thisand ultimately give discriminatory outcomes. The very basicproblem for this is the human being, who creates the prejudices thatexist in society and from which the data is collected. At the sametime, man is a central part of the process of reducing discriminatoryoutcomes and is needed to counteract this problem.