Manh Khoi Duong

Jannik Dunkelau, Manh Khoi Duong

The literature for fairness-aware machine learning knows a plethora of different fairness notions. It is however wellknown, that it is impossible to satisfy all of them, as certain notions contradict each other. In this paper, we take a closer look at academic performance prediction (APP) systems and try to distil which fairness notions suit this task most. For this, we scan recent literature proposing guidelines as to which fairness notion to use and apply these guidelines onto APP. Our findings suggest equalised odds as most suitable notion for APP, based on APP's WYSIWYG worldview as well as potential long-term improvements for the population.

Manh Khoi Duong, Stefan Conrad

In this paper, we deal with bias mitigation techniques that remove specific data points from the training set to aim for a fair representation of the population in that set. Machine learning models are trained on these pre-processed datasets, and their predictions are expected to be fair. However, such approaches may exclude relevant data, making the attained subsets less trustworthy for further usage. To enhance the trustworthiness of prior methods, we propose additional requirements and objectives that the subsets must fulfill in addition to fairness: (1) group coverage, and (2) minimal data loss. While removing entire groups may improve the measured fairness, this practice is very problematic as failing to represent every group cannot be considered fair. In our second concern, we advocate for the retention of data while minimizing discrimination. By introducing a multi-objective optimization problem that considers fairness and data loss, we propose a methodology to find Pareto-optimal solutions that balance these objectives. By identifying such solutions, users can make informed decisions about the trade-off between fairness and data quality and select the most suitable subset for their application. Our method is distributed as a Python package via PyPI under the name FairDo (https://github.com/mkduong-ai/fairdo).

Manh Khoi Duong, Stefan Conrad

Fairness metrics are used to assess discrimination and bias in decision-making processes across various domains, including machine learning models and human decision-makers in real-world applications. This involves calculating the disparities between probabilistic outcomes among social groups, such as acceptance rates between male and female applicants. However, traditional fairness metrics do not account for the uncertainty in these processes and lack of comparability when two decision-makers exhibit the same disparity. Using Bayesian statistics, we quantify the uncertainty of the disparity to enhance discrimination assessments. We represent each decision-maker, whether a machine learning model or a human, by its disparity and the corresponding uncertainty in that disparity. We define preferences over decision-makers and utilize brute-force to choose the optimal decision-maker according to a utility function that ranks decision-makers based on these preferences. The decision-maker with the highest utility score can be interpreted as the one for whom we are most certain that it is fair.