WORKSHOP DETAILS

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.

Delivery:

  • LIVE: Email info@qusandbox.com for upcoming LIVE training dates
  • ON DEMAND: Pre-recorded sessions with interactive videos, slides, demos and fully functional code through Qu.Academy.

Who should attend

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

This course is a part of the QuantUniversity Machine Learning and AI Risk Certificate Program. Avail additional discounts by enrolling to the Certification program.

QuantUniversity has partnered with (Professional Risk Managers' International Association)PRMIA to offer this course and is eligible for Continued Risk Learning Credits

Note: All courses come with a 90-day access to course materials and recordings Qu.Academy from the activation/class-start date. You can extend access to Qu.Academy. Contact us for subscription options. All sales are final. No request for cancellations, exchanges, changes or refunds shall be honored.

Delivery

LIVE/ON DEMAND

Where

QuAcademy

Number of Modules

6 modules

Each module

1.5 hours/module

Registration options
ON DEMAND:
Access now through Qu.Academy

COURSE SUMMARY

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.

MODULES 1: Introduction to Machine Learning and AI

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

MODULES 2: The Algorithm Audit

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

MODULES 3: The Algorithmic Audit Process

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

MODULES 4: 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

MODULES 5: Key aspects of an Algorithmic Audit

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

MODULES 6: Case study

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

PAST ATTENDEES

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..