We identify direct and indirect discrimination behaviors in the public and private sector that lead to economic unfairness. We investigate discrimination bias arising from the use of algorithms, artificial intelligence and pricing policies that cause a disparate impact on protected minorities.

Our original research spans across all major economic sectors and is grounded in solid statistical methodologies. We focus on practices that have a disproportionate impact on subsets of the population that present one of the many protected characteristics such as: gender, age, ethnicity, disability and religion.

Our discrimination practice focuses on the many aspects of the enforcement cycle: from identifying discriminatory conduct, to estimating damages and producing expert reports assessing disparate impact.

Our anti-discrimination practice focuses on:

  • Producing research that outlines discriminatory practices
  • Assessing disparate impact on relevant protected minority groups
  • Address class action commonality issues, where applicable
  • Estimation of damages
  • Digital algorithms
  • Artificial intelligence
  • Statistical and data analysis
  • Merits reports

Discrimination Caused by Digital Algorithms


  • Algorithmic decision making has become increasingly prevalent, but it also creates the potential for subtle new forms of discrimination
  • Algorithms are also usually a black box, with almost no transparency on their inputs
  • It is difficult to show commonality in US discrimination class actions because the Supreme Court has ruled that discriminatory actions by a subset of employees are insufficient to show commonality


  • We used regressions to analyze US mortgages and examine if borrower ethnicity had a statistical relationship with their interest rate
  • We controlled for a range of credit worthiness variables, while looking exclusively at loans issued by “Fintech” lenders, who use algorithms to originate loans


  • We identified algorithmic lenders who discriminate against ethnic minorities
  • For these lenders, we showed a strong positive relationship between borrower race and the interest rate they pay, with black/latinx borrowers receiving loans with 5-7 bps higher interest rates than comparable white borrowers
  • This methodology provided a new path to show commonality in US class action discrimination cases, where there is standard centralized decision making through algorithms

We have provided expert economic advice in most of the prominent international discrimination cases of the last decade:

  • Pre-paid cards
  • Energy payments affected minorities
  • Landline overcharging

Alberto Thomas

Head of Discrimination

Founding Partner