Can Economic Justice Be Colorblind?
By: Jason Capehart
Tags: Machine Learning, Fintech, BlackLivesMatter, Data Science, Towards Data Science

Can Economic Justice Be Colorblind?

Fair Machine Learning Can Shed Light on Institutional Racism in Lending

George Floyd’s death forces each of us to reckon with difficult questions. For me, that question is my conscience about my work as a data scientist in lending and the institutional racism I see in the industry. I’ve never written about it publicly because it feels like too much of a risk for my livelihood and my family. But one MLK quote keeps me up …

“I have almost reached the regrettable conclusion that the Negro’s great stumbling block in his stride toward freedom is not the White Citizen’s Counciler or the Ku Klux Klanner, but the white moderate, who is more devoted to “order” than to justice; who prefers a negative peace which is the absence of tension to a positive peace which is the presence of justice”

I first investigated discrimination in lending over a decade ago. I’ve thought about it a lot since then and talked about it a lot sense then — but always behind closed doors. I’ve always felt that working constructively inside industry was the right path. For all the ink I’ve spilled over the topic of discrimination in lending I don’t have any real progress to show for it. Now I wonder if I am the white moderate. Now I wonder if my public silence was not in the service of building trust in the ecosystem of lending, but to spare myself and those I work with from the tension and discomfort of acknowledging that it is not justice to address racism on our own timeline in ways that make us feel safe. Now I think I need to answer some uncomfortable questions … if only for myself.

Why don’t we talk about discrimination in lending?

Despite all the Fair Lending laws and regulators out there it seems like almost nothing constructive is ever said about discrimination in lending. Though I should remember that my experience in lending is short in comparison to the history of America. Within that history, there have been two important types of discrimination in lending:

Intentional discrimination — lenders call this disparate treatment

Unintentional discrimination — lenders call this disparate impact

In the late 1800s the first iteration of credit bureaus were deeply discriminatory, back when loans were seen as a moral obligation. Nevermind being Black — you might not get a loan for being Catholic. In the early 1900s loans and home ownership were impossible for most black Americans even in the North and the repercussions of that still echo today. Redlining was not an uncommon practice all the way up to the subprime mortgage crisis and myths about the causes of that crisis and the effects of the Community Reinvestment Act persist today. Intentional discrimination, whether it is overt discrimination or disparate treatment, is the history of lending in the United States.

After reforms from the subprime mortgage crisis, like the creation of the Consumer Financial Protection Bureau (CFPB), I think things improved. Out of the many different algorithmic definitions of fairness that have been proposed, I think the one that best describes what occurs in lending today is Fairness Through Unawareness: the idea that a model or policy is fair if it does not explicitly use protected class data. There are constraints beyond that, for example geographic data is rarely used due to its correlation with race, but calling it “fairness through unawareness” is a fairly accurate simplification. Credit bureaus do not accept information on race, ethnicity, gender, or other protected class information and lenders do not collect it, except for the requirements under the Home Mortgage Disclosure Act (HMDA). This, in combination with the transition toward statistical / machine learning model-based underwriting and online lending was a big step in the right direction. The precautions that good lenders and good bureaus take to assiduously avoid protected class data make it easy to argue that there is probably less intentional discrimination in lending now than at any previous point in US history. The problem is that all this effort to turn a blind eye toward race is not leading to fair outcomes.

The Center for Investigative Reporting performed a study that showed black mortgage applicants are much less likely to be approved than white applicants. If there is no intentional discrimination, how did we arrive at this status quo? The main culprit is unintentional discrimination, or disparate impact. Starting in 2018, the Dodd-Frank Act amended data collection rules for the HMDA to include credit score for a very good reason. Credit data is strongly correlated with approval and pricing outcomes in lending. Today, discrimination primarily enters lending through structural inequality. By definition, disadvantaged groups have lower socioeconomic status. They have worse credit scores, make less money, have fewer assets, less social network wealth, and are more likely to have been driven into predatory credit products. All these factors contribute to a higher probability of future financial hardship and probability of default drives lenders’ decisions on approvals and pricing. This is the cycle that creates institutional racism in lending. Safe credit creates leverage and economic advantage. No credit, or worse yet predatory credit, creates a cycle of debt and economic disadvantage. Once you’re poor — it’s harder and harder to catch back up.

The logical response is that information about race should be incorporated into lending decisions to make reparations for historical discrimination, but this is not possible under current laws. Even if I had no sympathy for lenders, the reality is they face an unwinnable Catch-22. Disparate treatment is illegal: no lending decision can be made on the basis of race. Disparate impact is illegal: protected classes cannot have adverse outcomes.

This is why it’s hard to have meaningful conversations about discrimination in lending:

The stakes are high for consumers, lenders, and regulators. We are all afraid of what we might lose.

There is no publicly available data. Other than HMDA data, we have no shared touchstones to talk about the extent, cause, or cost of discrimination.

Regulators are primed to stop intentional discrimination. They lack the data, tools, and resources to identify disparate impact efficiently.

Lenders are put into a paradoxical situation by regulators. Disparate treatment and disparate impact are at odds with each other. How to demonstrate business necessity for disparate impact is vague at best, and at worst might be completely unprovable.

Consumers have limited visibility and feedback. It is hard for a consumer to know if they’ve been a victim or to make an informed decision to work with an anti-racist lender.

On top of all this are the competition dynamics between lenders. As Frank Rotman illustrates, there can be massive rewards for being able to offer a slightly better lending product to consumers. This is a huge incentive to seek every possible edge in risk prediction — even at the expense of discrimination. Lenders optimize for the wrong thing. There is an equally healthy and profitable business to be had with a slight change of rules. Recent advances in Fair Machine Learning show clear and direct ways to start the virtuous cycle toward economic justice for black Americans.

How Could Economic Justice Be Achieved?

I don’t know how to create a positive peace in the face of police brutality. I don’t know what it feels like to be black in America today. But I know a data science problem when I see one and I think this one is solvable.

We need a solution that does three things: promotes economic justice for disadvantaged groups, is realistic about lenders’ risk aversion and requirement for profit, and aligns with regulators’ mandate to protect consumers and serve the law. That dictates 4 hard, but straightforward goals:

Define Fairness Collect Protected Class Data Test Fair Machine Learning in A Sandbox Fund the Fight Against Structural Inequality

Define Fairness

There are good surveys that compare different definitions of fairness and Martin Wattenberg et. al. visualize how a subset of these metrics could work in practice.

Martin Wattenberg on Fairness in Lending via Twitter

The definition I find cleanest is Counterfactual Fairness, which is the concept that the outcome should be the same if a consumer belonged to the advantaged group. Represented as a graphical causal model the problem is this:

Causal Graph of Discrimination in Lending (Jason Capehart, 2020)

Race directly affects attributes of an individual like: income, debt, and credit history. Unresolved discrimination is any affect of race directly on the outcome, and is what we want to avoid. When it gets down to it though, the choice of definition is unimportant. What matters is that we choose an objective definition for fairness and measure it.

Collect Protected Class Data

Although Bayesian Improved Surname Geocoding (BISG) is a great idea and a great tool, it has been the target of cheap shots and suspicion, however unfair that is. Direct data collection provides transparency and can build trust. HMDA’s data collection requirements provide a simple solution non-mortgage lenders can follow and credit bureaus can aggregate. Centralized storage of protected class data can, in turn, make it simpler for regulators to evaluate the activity of lenders.

Test Fair Machine Learning in A Sandbox

Efforts like this are the reason CFPB created sandboxes to partner with financial services. Lenders committed to fairness with the technical ability can lead the way to a better future and figure out how to make these principles work in practice. For example, several researchers have pointed out there are inherent trade-offs in learning fair representations. It would be necessary for any lender to start by pairing a fair model with a traditional one to understand trade-offs in risk and approval. There would undoubtedly be unexpected challenges along the way.

This sort of investment might seem like a hard sell for lenders — but it shouldn’t be because it aligns with how they want to make money. With few exceptions lenders are risk averse and don’t want to make money by pushing compliance, legal, or public relation boundaries. Third party legal consulting firms with the ability to do disparate impact analysis don’t come cheap. Lobbyists to defend industry interests aren’t cheap, neither are fines from regulators, or the PR efforts to make a brand look socially conscious. The investigation into Apple Card’s potential gender discrimination in credit line assignment drives this point home. These are headaches most lenders would love to have a straightforward way to resolve.

Fund the Fight Against Structural Inequality

There’s no machine learning trick that undoes 250 years of inequality. But, awareness of discrimination opens new opportunities to address discrimination, including interventions. Given the latitude to acknowledge race, lenders should naturally do a good job uncovering discrimination due to historical biases in the data because the process would be similar to developing a new market segment.

Some loans will be unprofitable and beyond what lenders will commit to, but for marginal declines due to structural inequality there’s the possibility for creative partnerships. For example, one could imagine an organization like the National Community Reinvestment Coalition or a government program partnering with a lender’s capital to fund only the “excess risk” on a loan that’s made to a member of a community they support.

Admitting to the presence of inequality can free us to do things that haven’t been possible before.