The widespread use of artificial intelligence algorithms and their role in decision-making with consequential decisions for human subjects has resulted in a growing interest in designing AI algorithms accounting for fairness considerations. There have been attempts to account for fairness of AI algorithms without compromising their accuracy to improve poorly designed algorithms that disregard sensitive attributes (e.g., age, race, and gender) at the peril of introducing or increasing bias against specific groups. Although many studies have examined the optimal trade-off between fairness and accuracy, it remains a challenge to understand the sources of unfairness in decision-making and mitigate it effectively. To tackle this problem, researchers have proposed fair causal learning approaches which assist us in modeling cause and effect knowledge structures, discovering bias sources, and refining AI algorithms to make them more transparent and explainable. In this study, we formalize probabilistic interpretations of both contrastive and counterfactual causality as essential features in order to encourage users' trust and to expand the applicability of such automated systems. We use this formalism to define a novel fairness criterion that we call contrastive counterfactual fairness. This paper introduces, to the best of our knowledge, the first probabilistic fairness-aware data augmentation approach that is based on contrastive counterfactual causality. We tested our approach on two well-known fairness-related datasets, UCI Adult and German Credit, and concluded that our proposed method has a promising ability to capture and mitigate unfairness in AI deployment. This model-agnostic approach can be used with any AI model because it is applied in pre-processing.

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

Understanding moral foundations can yield powerful results in terms of perceiving the intended meaning of the text data, as the concept of morality provides additional information on the unobservable characteristics of information processing and non-conscious cognitive processes. Considering that moral values vary significantly across cultures and yet many recurrent themes are observed and that each culture builds its societal and ideological narratives on top of its moral virtues, an enhanced understanding of morality can prove to be a valuable tool in deterring disinformation narratives by adversaries. Therefore, we investigate the evolution of latent moral loadings over time and across different sub-narratives on human and bot-generated tweets. For this purpose, we analyze the Syrian White Helmets-related tweets from April 1st, 2018 to April 30th, 2019. For the operationalization and quantification of moral rhetoric in tweets, we use Moral Foundations Dictionary in which five psychological dimensions (Harm/Care, Subversion/Authority, Cheating/Fairness, Betrayal/Loyalty and Degradation/Purity) are considered. Our results present the significant differences between the strength and patterns of moral rhetoric for human and bot-generated content on Twitter.