Credit rating agencies (CRAs) play a central role in the debt (bond) markets of many
countries. CRAs have also attracted a considerable amount of public and policy attention …

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers
trained on such datasets can inherit these biases. In this paper, we provide a mathematical …

We propose an approach to fair classification that enforces independence between the
classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

Machine learning bias and fairness have recently emerged as key issues due to the
pervasive deployment of data-driven decision making in a variety of sectors and services. It …

We test whether Standard and Poor's (S&P) assigns higher bond ratings after it switches
from investor-pay to issuer-pay fees in 1974. Using Moody's rating for the same bond as a …

This paper examines how the information quality of ratings from an issuer-paid rating
agency (Standard and Poor's) responds to the entry of an investor-paid rating agency, the …

We present a model in which issuers of asset-backed securities choose to release coarse
information to enhance the liquidity of their primary market, at the cost of reducing secondary …

In a market where sellers solicit certification to overcome asymmetric information, we show
that the profit of a monopolistic certifier can be hump-shaped in its reputation for accuracy: a …

A review of the main fairness definitions and fair learning methodologies proposed in the
literature over the last years is presented from a mathematical point of view. Following our …