### 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](mailto: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](/content/course-details/mlrisk.html). Avail additional discounts by enrolling to the Certification program. QuantUniversity has partnered with [(Professional Risk Managers' International Association)PRMIA](https://www.prmia.org/) 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](https://academy.qusandbox.com/) ##### Number of Modules 6 modules ##### Each module 1.5 hours/module **Registration options** ##### ON DEMAND: Access now through Qu.Academy [**Register**](https://buy.stripe.com/5kAg2p3xn9v02S49AL) Algorithmic Auditing Spring 2021 Course - YouTube Tap to unmute [Algorithmic Auditing Spring 2021 Course](https://www.youtube.com/watch?v=U8vmgrdX8Xk) [QuantUniversity Channel](https://www.youtube.com/channel/UCRlFeeFaSk7JiCSBYcFffGQ) QuantUniversity Channel1.44K subscribers ### 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.. ### PARTICIPANT FEEDBACK Sri was an awesome instructor. He took care of the group's needs and offered different channels to pose questions. Very competent and passionate about this very vast subject and caring about the learning of his students, even if the course was asynchronous - it has been real a pleasure to learn from Sri. I really loved the format of this course. The flexibility with respect to time was great as the modules were prerecorded and the possibility to pose questions to the instructor in scheduled Q/A meetings was extremely useful. ### KALIBA BILALA #### CFA I truly enjoyed your recent webinars with the CFA Institute and also learnt a lot. I look forward to more presentations from you. ### Frederic Siboulet #### Managing Director Ernst & Young Global Consulting Services I loved the course. Very well organized, clear and concise delivery, with a lot of practical examples. Abundance of materials, with links to live examples, tools, websites, articles and books. ### Michelle Allade #### Director, Model Risk Management MetaBank ..Very good info and wish all the modules were unlocked so I could binge watch. Comprehensive nature of the curriculum and the layout/sequence, which flowed very naturally. Along these lines, Sri did an effective job of introducing the big picture and alternating between focus on the building blocks and relating those back to the bigger picture.