Algorithmic Auditing

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 reputational 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.

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

This course is a part of the QuantUniversity Machine Learning and AI Risk Certificate Program.

 Take advantage of additional discounts by enrolling in the Certificate program.

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 Alogrithmic 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 Agorithmic 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

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

Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity, a data and quantitative analysis company. His experience includes analytics, quantitative analysis, statistical modeling and software development. He is a quantitative specialist with significant experience in designing data mining and analytic systems for some of the world’s largest asset management and financial companies.

Sri has worked at MathWorks as a Computational Finance Consultant where he worked with customers providing asset management, energy analytics, risk management and trading solutions. Prior to that, Sri was a consultant at Endeca (now Oracle) in their Analytics Group and at Citigroup in their Fixed-Income Group building large-scale analytical and trading systems.

Sri is a Charted Financial Analyst and a Certified Analytics Professional.  He is an active member of the Boston Security Analysts society and QWAFAFEW. He is the creator of the Fintech Certificate Program & Analytics Certificate Program and teaches graduate courses in Quantitative methods, Data science and Analytics and Big Data at Babson College, Northeastern University and Hult International Business School.