Information retrieval and recommender systems based on machine learning can be used to make decisions about people. Government agencies can use such systems to detect welfare fraud, insurers can use them to predict risks and to set insurance premiums, and companies can use them to select the best people from a list job applicants. Such systems can lead to more efficiency, and could improve our society in many ways. However, such AI-driven decision-making also brings risks. This project focuses on the risk that such AI systems lead to illegal discrimination, for instance harming people of a certain ethnicity, or other types of unfairness. A different type of unfairness could concern, for instance, a system that reinforces financial inequality in society. Recent machine learning work on measures of fairness has resulted in several competing approaches for measuring fairness. There is no consensus on what is the best way to measure fairness and the measures often depend on the type of machine learning that is applied. Based on the application of existing measures on real-world data, we suspect that many proposed measures are not that helpful in practice. In this project, you will study measures of fairness, answering questions such as the following. To what extent can legal non-discrimination norms be translated into fairness measures for machine learning? Can we measure fairness independently of the machine learning approach? Can we show which machine learning methods are the most appropriate to achieve non-discrimination and fairness? The project concerns primarily machine learning for information retrieval and recommendation, but is interdisciplinary, as it is also informed by legal norms. The project will be supervised by Professor Hiemstra, professor of data science and federated search, and Professor Zuiderveen Borgesius, professor of ICT and law.
Profile
- You hold a completed Master’s Degree or Research Master’s degree in computer science, data science, machine learning, artificial intelligence, or a related discipline.
- You have good programming skills.
- You have good command of spoken and written English.
- We encourage you to apply even if you think you do not meet all the requirements.
More information at: https://www.ru.nl/english/working-at/vacature/details-vacature/?recid=1171943