There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many...

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in...

As peer-to-peer (P2P) lenders evaluate the potential risk of each loan application, they may rely on subjective judgement given qualitative information. Academics have found loan approval rates to be associated with the borrower's personality traits, social capital, and appearances. However, the...

We propose a method to integrate frequentist and subjective probabilities in order to obtain a coherent asset allocation in the presence of stress events. Our working assumption is that in normal market asset returns are sufficiently regular for frequentist statistical techniques to identify...

We propose a method to integrate frequentist and subjective probabilities in order to obtain a coherent asset allocation in the presence of stress events. Our working assumption is that in normal market asset returns are sufficiently regular for frequentist statistical techniques to identify...

We show how to execute a macro-hedge on a portfolio that depends on several risk factors in a one period static context. We show, by applying a orthogonalization procedure, that adding a hedging instrument with just one underlying reduces the risk of the portfolio along several dimensions but up...