This paper presents an intuitive explanation about why and how Rawlsian Theory of Justice (Rawls in A theory of justice, Harvard University Press, Harvard, 1971) provides the foundations to a solution for algorithmic bias. The contribution of the paper is to discuss and show why Rawlsian ideas in their original form (e.g. the veil of ignorance, original position, and allowing inequalities that serve the worst-off) are relevant to operationalize fairness for algorithmic decision making. The paper also explains how this leads to a specific MinMaxfairness solution, which addresses the basic challenges of algorithmic justice. We combine substantive elements of Rawlsian perspective with an intuitive explanation in order to provide accessible and practical insights. The goal is to propose and motivate why and how the MinMaxfairness solution derived from Rawlsian principles overcomes some of the current challenges for algorithmic bias and highlight the benefits provided when compared to other approaches. The paper presents and discusses the solution by building a bridge between the qualitative theoretical aspects and the quantitative technical approach.

The importance of fairness in machine learning models is widely acknowledged, and ongoing academic debate revolves around how to determine the appropriate fairness definition, and how to tackle the trade-off between fairness and model performance. In this paper we argue that besides these concerns, there can be ethical implications behind seemingly purely technical choices in fairness interventions in a typical model development pipeline. As an example we show that the technical choice between in-processing and post-processing is not necessarily value-free and may have serious implications in terms of who will be affected by the specific fairness intervention. The paper reveals how assessing the technical choices in terms of their ethical consequences can contribute to the design of fair models and to the related societal discussions.

Default probability is a fundamental variable determining the credit worthiness of a firm and equity volatility estimation plays a key role in its evaluation. Assuming a structural credit risk modeling approach, we study the impact of choosing different non parametric equity volatility estimators on default probability evaluation, when market microstructure noise is considered. A general stochastic volatility framework with jumps for the underlying asset dynamics is defined inside a Merton-like structural model. To estimate the volatility risk component of a firm we use high-frequency equity data: market microstructure noise is introduced as a direct effect of observing noisy high-frequency equity prices. A Monte Carlo simulation analysis is conducted to (i) test the performance of alternative non-parametric equity volatility estimators in their capability of filtering out the microstructure noise and backing out the true unobservable asset volatility; (ii) study the effects of different non-parametric estimation techniques on default probability evaluation. The impact of the non-parametric volatility estimators on risk evaluation is not negligible: a sensitivity analysis defined for alternative values of the leverage parameter and average jumps size reveals that the characteristics of the dataset are crucial to determine which is the proper estimator to consider from a credit risk perspective.

This paper introduces a methodology to disentangle the hedging error associated with the hedging of exotic derivatives, whose payment time is unknown at inception. We derive the mathematical representation for a one-dimensional setting: we identify and characterize the hedging error and discuss the economic intuition of hedging error as a generalized timing risk. We then provide its mathematical integral representation to: (i) disentangle the hedging error into a specific set of positions in barrier options, (ii) re-iterate the procedure to the second order to reduce the hedging error cost. We provide an illustrative example via a dedicated numerical study. From a theoretical point of view, this paper states the foundations for future extensions in the directions of: (i) building a general multidimensional framework, (ii) re-iterating the procedure to higher orders, (iii) investigate the bridge with advanced analytics methodologies and techniques.

Classification algorithms are widely used in critical domains such as healthcare, bank loans, credit and fraud detection. These systems should be transparent, yet it remains unclear how individuals will use explanations to adjust their own features. Individuals often access multiple sources of information, from insights provided by institutions to experiences shared among peers. Based on the information received, individuals may decide to strategically adapt to obtain a favourable outcome, honestly improving or attempting to game the system. This paper studies the impact of transparency and social information on strategic classification. We assume that agents adapt based on best response and behavioural imitation along the edges of social networks. We observe that increasingly opaque decision rules can negatively impact the utility of institutions, especially in dense social networks. The number of False Positives is reduced in networks with a lower average degree, when users imitate the average behaviour, as opposed to the most extreme behaviours. When imitating the most extreme behaviour among their connections, users change their features to a large extent in networks with a higher average degree (i.e., higher density). This applies to both honest improving and gaming, with more pronounced impacts in the case of the latter, creating an additional source of risk for institutions. Our model and results reveal that behavioural imitation patterns and social network effects influence the downstream effects of algorithmic transparency.