Thesis supervisor: Orsolya Vásárhelyi
Location of studies (in Hungarian): CUB Abbreviation of location of studies: BCE
Description of the research topic:
In an era where data-driven decision-making shapes critical aspects of our society, the field of data science stands at the forefront of innovation and transformation. Data-driven insights have the power to revolutionize industries, inform policies, and enhance our daily lives. However, this promising domain is not without its challenges, foremost among them being the potential for algorithmic discrimination. Algorithmic discrimination, wherein data-driven systems unfairly treat individuals or groups based on factors such as race, gender, or socioeconomic background, poses a profound ethical and social concern. While data science holds the potential to promote objectivity and fairness, it can also inadvertently perpetuate biases, reinforcing systemic inequalities. Addressing this challenge requires an interdisciplinary approach that acknowledges the pivotal role of diversity in data science teams. This research project seeks to explore how diversity within data science teams impacts objectivity, confirmation bias, and ethical practices, ultimately leading to the mitigation of algorithmic discrimination.
Research Goals
The primary objective of this proposed research is to deepen our understanding of the following critical aspects, while also encouraging candidates to develop new ideas based on their own interests in the topic:
Objectivity Quantifying how diverse teams in data science contribute to enhanced objectivity in decision-making processes and data analysis. Research has shown that diverse teams are better equipped to challenge preconceived notions and biases, thus potentially fostering more objective outcomes.
Ethical Practices Ethical considerations are at the heart of addressing algorithmic discrimination. This part of the project aims to understand how diverse data science teams are more attuned to ethical concerns and how they contribute to the development and implementation of ethical practices in data collection, analysis, and algorithm deployment.
Number of students who can be accepted: 1
Deadline for application: 2024-12-31
2024. VII. 26. ODT ülés Az ODT következő ülésére 2024. augusztus 1-én, csütörtökön 14.00 órakor kerül sor online formában a Webex felületén.
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