What you’ll learn
This course teaches you a systematic approach to algorithmic risk and fundamental rights impact assessments, which is a necessary skill for practitioners in the space of emerging technology. After finishing this course students will be able to:
Identify the socio-technical components of an algorithmic system that are relevant for risk analysis
Produce a narrative of these components (a “CIDA” narrative) as a form of algorithmic transparency
Identify important stakeholders
List engagement strategies for relevant stakeholders to determine their salient interests, fundamental rights, and identify potential harms due to the algorithmic system
Decide which components of the algorithmic system can serve as metrics for risk analysis
Develop initial assessment strategies for these metrics
Who is this course for?
This course is developed for AI governance, risk, and compliance professionals needing to perform AI risk or impact assessments within companies using or developing high-risk AI systems. This kind of training is important for companies trying to conform with:
- The EU AI Act
- Digital Services Act
- ISO 42001
- Auditors for ISO 42001 (see ISO 42006)
- NIST AI RMF
Other groups that would find this course and certification useful:
- Consultants in AI Ethics and Governance
- Procurement specialists who are concerned about risks due to AI vendors
- Employees at VC firms that want to incorporate AI risks into their due diligence process
This course is part of a 5-course certification program for AI and Algorithm Auditors.
About the Instructor
Shea Brown is the founder and CEO of BABL AI, a research consultancy that focuses on the ethical use and development of artificial intelligence. His research addresses algorithm auditing and bias in machine learning, and he serves as a ForHumanity Fellow that sets standards for the organizational governance of artificial intelligence.
He has a PhD in Astrophysics from the University of Minnesota and is currently an Associate Professor of Instruction in the Department of Physics & Astronomy at the University of Iowa, where he has been recognized for his teaching excellence from the College of Liberal Arts & Sciences.
Curriculum
Introduction
What you'll learn
Course Resources
Week 1 - Foundation
The main modes of working (14:43)
Spotting risks (11:34)
Researching solutions (9:23)
Effective communications (13:02)
Exercise 1: Putting your knowledge to work (5:26)
Specialized tasks
Overview of non-technical tasks (27:22)
Algorithms, AI and learning machines (21:01
Bias testing (21:41)
Exercise 2: Finding your niche
What now? (3:26)
Don’t just take our word for it
Choose a Pricing Option
Algorithmic Risk & Impact Assessments
Essential Tools for AI and Algorithm Auditing
Additional qualifying discounts are available
Contact us today to learn more