Algorithmic Auditing for Machine Learning & AI Models

Algorithmic Auditing for Machine Learning & AI Models
NEW COURSE! Learn how to audit ML and AI models in this 6-week program geared towards practitioners.
 

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(You will be redirected to our training partner's registration system.)
Agenda

Course Experts

CRL Credits

 Dates
Course Period:  April 1 - May 6, 2021
Lessons Launch: 
Each Thursday beginning April 1, 2021
Instructor Access:
April 1 - May 13, 2021

Time:
Self-paced

Presented by:
Sri Krishnamurthy, CFA
Chief Data Scientist, QuantUniversity


 Session Length:
90 minutes + Labs

 

About This Course
 
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.

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.
 
Agenda
Lesson   Topic
Lesson 1
  Introduction to Machine Learning and AI
In this week, you will get an orientation of the key Machine learning and AI techniques.
Lesson 2
  The Algorithm Audit
Defining the Algorithmic audit process
Lesson 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
Lesson 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
Lesson 5   Key aspects of an Algorithmic Audit
Learn about: Fairness, Bias, Interpretability, Explainability, Rating, Key metrics,
Model failures, Incident reporting, Model Risk, AI Insurance
Lesson 6   Case study
With a sample machine learning model, conduct a full Algorithm audit with the QuSandbox.
All Participants will get FREE access to QuSandbox


Who Should Attend
Model Risk professionals, Model validators, Regulators and Financial professionals new to data-driven methodologies
Quantitative analysts, investment professionals, Machine learning enthusiasts interested in understanding model risk and governance aspects in fintech, insurance and financial organizations.

About Our Expert

  
  Sri is the founder of QuantUniversity, a data and quantitative analysis company. He has more than 15 years experience in 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 more than 25 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.He is a Charted Financial Analyst and a Certified Analytics Professional.  He is an active member of the Boston Security Analysts society and QWAFAFEW.

Sri 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.

  About Our Training Partner: QuantUniversity is a quantitative analytics advisory focusing on the intersection of Data science, Machine learning and Quantitative Finance. We take a practitioner’s approach to working with pragmatic applications of frontier topics to real-world financial and energy problems. QuantUniversity advises various companies in Quant Finance application development, validation and in algorithmic auditing. We also run data science and machine learning workshops in the United States and online in its Explore-Experience-Excel series through QuAcademy. QuantUniversity is pioneering the next generation platform for Algorithmic auditing that supports anonymization, model escrow and tracking, synthetic data generation and experimentation through the QuSandbox.

Continued Risk Learning Credits:9

PRMIA Continued Risk Learning (CRL) programs provide you with the opportunity to formally recognize your professional development, documenting your evolution as a risk professional. Employers can see that you are not static, making you a highly valued, dynamic, and desirable employee. The CRL program is open to all Contributing, Sustaining, and Risk Leader members, providing a convenient and easily accessible way to submit, manage, track and document your activities online through the PRMIA CRL Center. To request CRL credits, please email learning@prmia.org.

Registration
 Membership Type Price
   
 PRMIA Network Member $699.00
  ** USE DISCOUNT CODE: PRMIA100  
 Non Member $799.00

QUANTUNIVERSITY Registration Policies

This course is being delivered by a PRMIA Training Partner, QuantUniversity. All registrations, payments, and course operations will be managed exclusively by QuantUniversity. When registering for the course, you will leave PRMIA.org and use QuantUniversity's registration system, proprietary learning management system, and QuSandbox for labs. Questions and requests should be directed to QuantUniversity at info@qusandbox.com.

Please review QuantUniversity's cancellation and refund policy at the time of purchase, as their policies supersede PRMIA's registration policies.  

Need Support? 

Contact our training partner for questions about the course, group registrations and technical support info@qusandbox.com

Analytics for a cause initiative - Thank you QuantUniversity for supporting future risk leaders!

QuantUniversity is a proud sponsor and contributor to educational scholarships, valued at $30,000, to students from eight countries and 12 chapters, participating in the PRMIA Risk Management Challenge for 2020 and 2021!  Learn more about this and other outreach supported by QuantUniversity.

PRMIA Network Fee: $699.00 using discount code: PRMIA100

 

Register Now

(You will be redirected to our training partner's registration system.)
When
4/1/2021 - 5/6/2021
Where
Virtual Course
 

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