An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans, and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop ML algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision making may be inherently prone to unfairness, even when there is no intention for it. This article presents an overview of the main concepts of identifying, measuring, and improving algorithmic fairness when using ML algorithms, focusing primarily on classification tasks. The article begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process, and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, toward a better understanding of which mechanisms should be used in different scenarios. The article ends by reviewing several emerging research sub-fields of algorithmic fairness, beyond classification.

In this paper, we exploit different facets of the Friends and Family study to deal with two personality-related tasks of paramount importance for the user modeling and ubiquitous computing fields. First, we propose and validate an approach for automatic classification of personality traits based on the ego-networks’ structural characteristics. Our classification results show that (i) mobile phones-based behavioral data can be superior to survey ones for the purposes of personality classification from structural network properties and (ii) particular feature set/network type combinations promise to perform better with given personality traits. Then, we investigate the mediating role played by personality in the context of inducing behavioral change, specifically increasing daily physical activity using social strategies (social comparison and peer pressure). Our results confirm the role played by Extraversion and Neuroticism. Extroverts exposed to a social comparison strategy are positively associated with an increase in physical activity level, while they tend to decrease physical activity level if they are exposed to a peer pressure intervention strategy. Regarding Neuroticism dimension, neurotic people tend to increase their physical daily activity level if they are exposed to a social comparison strategy. Our findings may have implications in designing personality-based behavioral change strategies and suggest to incorporate users’ personality models in the implementation of persuasive systems.

The concept of “Structural Diversity” of a network refers to the level of dissimilarity between the various agents acting in the system, and it is typically interpreted as the number of connected components in the network. This key property of networks has been studied in multiple settings, including diffusion of ideas in social networks and functional diversity of regions in brain networks. Here, we propose a new measure, “Structural Entropy”, as a revised interpretation to “Structural Diversity”. The proposed measure relies on the finer-grained network communities (in contrast to the network’s connected components), and takes into consideration both the number of communities and their sizes, generating a single representative value. We then propose an approach for monitoring the structure of correlation-based networks over time, which relies on the newly suggested measure. Finally, we illustrate the usefulness of the new approach, by applying it to the particular case of emergent organization of financial markets. This provides us a way to explore their underlying structural changes, revealing a remarkably high linear correlation between the new measure and the volatility of the assets’ prices over time.