AI Explainability & Fairness (Consumer Credit) — Insights from Stanford & FinRegLab

  • For lenders: model explainability is a key instrument to help them evaluate whether a model can be responsibly used in an intended application, to enable the day in, day out work of managing relevant prudential and consumer protection risks, and to document efforts to comply with law and regulation.
  • For consumers: model explainability helps ensure that they receive basic information about how certain kinds of adverse credit decisions are made and enable effective recourse.
  • For regulators and policy makers: model explainability is an instrument to enable oversight and detect shortcomings in adherence to laws and regulations
  1. Adverse Action Notice (AAN) — Provide loan applicants who are denied credit or charged higher prices with the principal reasons for those decisions. In order to produce adverse action notices, lenders must be able to identify drivers of the model’s prediction for individual applicants who are subject to adverse decisions and map those drivers to descriptions or reason codes that will be given to the consumer
  2. Fair Lending, Disparate Impact and Least Discriminatory Alternative (FL, DI & LDA) — Investigate whether the underwriting models have disproportionately adverse effects on the basis of protected characteristics, and if so to search for alternative models.

3.1 Fidelity

  • For AAN, it is the ability to reliably identify features that can help describe how models take adverse credit decisions;
  • * For DI it is the ability to reliably identify features that are in fact related to a model’s adverse impact

3.2 Consistency

  • Whether drivers identified by the same tool across different models or by different tools across the same model vary i.e., consistency across diagnostic tools and consistency across models

3.3 Usability

  • For AAN it is the ability of a model diagnostic tool to provide actionable information that helps an applicant subject to an adverse credit decision satisfy the criteria for approval within one year..
  • For DI it is the ability to identify information that enables lenders to comply with the goals and purposes of consumer protection regulation

6.1. Overview :: Cautious Optimism !

6.2. AAN (Adverse Action Notice) :: Use wisely !

  • The two tasks were to generate four drivers of adverse credit decisions for the set of 3000 rejected applicants and then to identify a feasible path towards acceptance within 12 months for each of the 3000 rejected applicants.
  • The path to acceptance is interesting because it is a computational challenge as well as finding the right set of feasible features that are practical for a consumer to change. [Note : Counterfactuals is an important area for this — I have marked counterfactuals as future additions below]

6.3. FL, DI & LDA (Fair Lending, Disparate Impact & Least Discriminatory Alternative) :: Practical and improving !

  • Two fair lending doctrines reflect these requirements: Disparate Treatment and Disparate Impact.
  • Disparate treatment focuses on whether lenders have treated applicants differently based on protected characteristics like race, gender et al
  • Disparate impact prohibits lenders’ use of facially neutral practices that have a disproportionately negative effect on protected classes, unless those practices meet a legitimate business need that cannot reasonably be achieved through alternative means with a smaller discriminatory effect. This is where the LDA comes in. As you will see down below, tools are able to find alternates using automated search.
  • Financial institutions rely on statistical analyses to help them comply with both legal fair lending doctrines.


  • Except a brief mention, this is a topic not covered in the white paper. A deep dive into the counterfactual methodologies, the vendor offerings and the usefulness would definitely be a great track for the next version of the paper.
  • The counterfactuals not only aid the AAN part of the story but also as an educational tool for the consumers when used in a human-in-the-loop fashion.

Non-traditional, extended datasets and an inclusive eco system

  • The question is not whether we can be more inclusive and serve under represented population, but how …
  • How can we be more inclusive, advancing the causes of the under represented viz. the under-banked and the non-banked. This is socially beneficial and good for business. But requires non-traditional datasets and even business policies
  • Where future versions of this report can help us to understand how we can use the tooling to build and evaluate models adding the extra dimension of non-traditional, extended datasets.

More Model Types and white-box (less opaque) models

  • I suspect most of the work assumes black-box models. It would be interesting to extend to more transparent modeling where one cam peer-into the different stages inside al algorithm — things like integrated gradients for deep learning models.
  • This will give us a glimpse into the effective use of DeepLearning (with all it’s “deepness”) for underwriting, especially for the non-traditional, extended datasets. They have used complex-neural network models, but there are no insights specific to that class of models — i.e., how do they perform vis-à-vis non-DL models.
  • We know we can develop models, but we also need to understand how to explain opaque models as well as apply fairness assessment effectively. If we can do that, I think we will make more progress in the inclusivity dimension viz. larger varied data and models that can extract correct valuable insights — the “responsible risk-taking”.


  • Another related line of inquiry. The paper explores only one class — which might be relatively easier for the tools. Methodology to evaluate in the intersectional dimension is very important for a practitioner.

Fairness mitigation

  • This is another topic that is peripherally covered. A separate section covering mitigation strategies in the light of the tooling available would be very interesting.

Editorial nitpick

  • The white paper could use some more editing ! I am saying this as humbly as possible, realizing the amount of material it covers. Interestingly that is when we need more editing. There is some redundancy and repetition — many times I felt that I read the same thing somewhere else, probably due to occasional loss of context that is not apparent .
  • I fear that many who would have benefitted from this work might not stay with it till the end !
  • Probably, organize the paper leaning more towards industry practitioners, rather than the academic, might help.

Having said that …

  • The main goal for this blog was for me to extract the insights and best practices for a couple of documents (internal and external) I am working on. This, I was able to do very well — most of this blog is directly from the paper ! So the depth and the details are there.

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