The use of AI and machine learning in finance has grown significantly in the last few years. As more and more AI and ML applications are being deployed in enterprises, concerns are growing about the increased complexity of models, the growing ecosystem of untested frameworks and products, potential for AI accidents, model and reputation risk. As the debate about explainability, fairness, bias, and privacy grows, there is increased attention to understanding how the models work and whether the models are designed and thoroughly tested to address potential issues.

The area "Algorithmic auditing" is fast emerging and becoming an important aspect in the adoption of machine learning and AI products in the enterprise. Companies are now incorporating formal ethics reviews, model validation exercises, internal and external algorithmic auditing to ensure that the adoption of AI is transparent and has gone through thorough vetting and formal validation processes. However, the area is new and organizations are realizing, there is an implementation gap on how Algorithmic auditing best practices can be adopted within an organization.

Who should attend

  • Risk professionals
  • Model Validators
  • Auditors
  • Data Scientists
  • ML engineers and Software engineers involved in ML and AI deployment




Number of Sessions


Each weekly session

1.5 hours


Online through QuAcademy with Video, Demos, Case studies + Labs


Sri Krishnamurthy


In this QuantUniversity course, the first formal course offered in the industry, we will introduce Algorithmic auditing and discuss the various aspects of Algorithmic auditing when operationalizing Algorithmic auditing within the enterprise. We will discuss the emerging risks in the adoption of AI and discuss how to address the emerging needs of formal Algorithmic auditing practices.

Hands-on examples and case studies through QuSandbox will be provided to reinforce concepts.


Introduction to Machine Learning and AI

  • Key Data And Machine Learning And AI Techniques.
  • RPA, Machine Learning And Analytical Models

The Algorithm Audit

  • The Algorithmic Audit Framework
  • Internal And External Audit Considerations
  • Industry Case Studies

The Algorithmic Audit Process

  • 5 Things To Note When Auditing An Algorithm: Use Case, Data, Model, Environment, Process
  • Scorecards, Synthetic Data,Verification Vs Validation

Scoping the Algorithmic Audit

  • How Do You Scope An Algorithmic Audit?
  • Methods For RPA Processes, Data Handling, Algorithms (Blackbox, Grey Box, White Box), Roles, Responsibility, Governance And Stakeholders

Key aspects of an Algorithmic Audit

  • Issues Of Fairness, Bias, Interpretability, Explainability, Rating, Key Metrics, Model Failures, Incident Reporting, Model Risk, AI Insurance.

Case study

  • With A Sample Machine Learning Model, conduct A Full Algorithm Audit With The QuSandbox.


Past Attendees of QuantUniversity workshops include Assette, Baruch College, Bentley College, Bloomberg, BNY Mellon, Boston University, Datacamp, Fidelity, Ford, Goldman Sachs, IBM, J.P. Morgan Chase, MathWorks, Matrix IFS, MIT Lincoln Labs, Morgan Stanley, Nataxis Global, Northeastern University, NYU, Pan Agora, Philips Health, Stevens Institute, T.D. Securities and many more..