Amin, Rifat Mehreen2023-08-242023-08-242023https://dl.gi.de/handle/20.500.12116/42146This position paper emphasizes the role of user-centered artificial intelligence in critical decision-making domains in machine learning models. In this paper, I introduce MOCCF (Multi-Objective Counterfactuals for Counterfactual Fairness) as an extended method that generates realistic counterfactuals by leveraging multiple objectives. Furthermore, to increase transparency, I propose two fairness metrics, Absolute Mean Prediction Difference (AMPD), and Model Biasness Estimation (MBE). I argue that these metrics enable the detection and quantification of unfairness in binary classification models both at the individual and holistic levels consecutively and contribute to user-centered artificial intelligence.Text/Workshop Paper10.18420/muc2023-mci-ws16-397