Faktorer som påverkar ett rättvist beslutsfattande : En undersökning av begränsningar och möjligheter inom datainsamling för maskininlärning

Sammanfattning: Artificial intelligence, AI, is widely acknowledged to have atransformative impact on various industries. However, thistechnology is not without its limitations. One such limitationis the potential reinforcement of human biases withinmachine learning systems. After all, these systems rely ondata generated by humans. To address this issue, theEuropean Union, EU, are implementing regulationsgoverning the development of AI systems, not only topromote ethical decision-making but also to curb marketoligopolies. Achieving fair decision-making relies on highquality data. The performance of a model is thussynonymous with high-quality data, encompassing breadth,accurate annotation, and relevance. Previous researchhighlights the lack of processes and methods guiding theeffort to ensure high-quality training data. In response, thisstudy aims to investigate the limitations and opportunitiesassociated with claims of data quality within the domain ofdata collection research. To achieve this, a research questionis posed: What factors constrain and enable the creation of ahigh-quality dataset in the context of AI fairness? The studyemploys a method of semistructured interviews withindustry experts, allowing them to describe their personalexperiences and the challenges they have encountered. Thestudy reveals multiple factors that restrict the ability tocreate a high-quality dataset and, ultimately, a fair decisionmaking system. The study also reveals a few opportunities inrelation to high quality data, which methods associated withthe research landscape provides.