Algorithmic Risk & Impact Assessments

Essential Tools for AI and Algorithm Auditing

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 (11:09)

Course Resources

Week 1 - Foundations

The goals of an algorithmic risk and impact assessment (10:22)

A simplified framework (21:23)

Algorithms in context: the CIDA narrative (11:56)

Stakeholders: Interests, rights, and harms (14:08)

Metrics of a socio-technical system (13:34)

Exercise 1: Putting your knowledge to work (66:48)

Week 2 - Practical Assessments

Case study introduction (6:20)

System analysis and stakeholder mapping (14:34)

Interviews (21:41)

Testing and evaluation (21:12)

Magnitude, likelihood, and risk prioritization (16:02)

Risk mitigation and residual risk (5:40)

Exercise 2: Documentation (113:26)

Don’t just take our word for it

Here’s what our graduates have to say…

What I particularly appreciated about this program was its ability to strengthen my understanding in areas where my knowledge was previously relatively limited. The content was not only thoroughly well-balanced, covering both foundational and advanced topics, but also very engaging and interesting. The unique nature of the program really stood out; despite my extensive search for similar courses, none matched the depth and relevance in risk management offered here.

-Tony Hibbert – AI Governance Expert, ING Bank

Throughout the course, I gleaned a framework for the science, laws and critical thinking behind AI auditing. Dr. Shea Brown and his team provided an excellent balance of theoretical knowledge and practical applications so that I could feel confident in working with a team to identify, analyze and test risks of the systems all around us… BABL AI created a welcoming environment for people regardless of professional background. I felt confident in scaling my knowledge and skills without a science background and also in my lived experience being respected as part of the auditing process.

– Luna

While learning, I was impressed by its well-structured design, practical content, and accessible resources. Whether you’re navigating your AI career or pursuing self-education for AI literacy, I highly recommend you enjoy the learning journey. You’ll gain not only AI knowledge and insights but also invaluable advice, confidence, and professional support from the community. The program’s comprehensive approach and engaging materials make it a standout choice for anyone looking to deepen their understanding of AI.

Kelvin Lou – Tech lawyer, Executive Director of OneCompliance Consulting

The BABL AI & Algorithm Auditor Certification program stands out in the responsible AI field for its practical, community-driven approach. Dr. Brown is an exceptional mentor whose emphasis on first-principles thinking helped me break down complex theoretical and technical concepts into accessible, actionable insights. As someone new to the field, the bi-weekly Q&A sessions were invaluable—providing me the chance to interact with peers and learn from their diverse experiences in a supportive environment. Most importantly, the program transformed me into a confident responsible AI practitioner. I now have the skills to identify AI system risks, audit algorithms, conduct assurance engagements, and guide organizations through AI governance challenges.

Edward Feldman – Manager at Target

Following through with BABL’s “AI and Algorithm Auditor Certificate Program” has been a great decision and learning experience. I am yet to find another program that puts so much emphasis on a holistic approach for AI Auditing, aiming to connect the dots between areas like bias testing or AI governance. At the same time, the hands-on assignments incentivize one to thoroughly grasp the theoretical content and to apply it to one’s own professional context. Paired with Dr. Shea Brown’s gift for conveying knowledge in an engaging way, the kind support of the BABL team and the welcoming learning community, I recommend this program to technical and non-technical professionals alike who want to dive deeper into AI Auditing.

Nadine Dammaschk – AI Governance Advisor at GIZ

In today’s AI-driven world, where algorithms are quietly influencing everything from credit decisions to healthcare, being a passive observer is no longer enough. That’s why I chose to embark on the AI and Algorithm Auditor Certification with BABL AI—and it has been a game-changer. It was an eye-opening, mind-expanding experience that equipped me with practical tools to assess, audit, and advocate for responsible AI. From uncovering hidden biases to designing risk mitigation strategies, I now have the confidence to engage with organizations and help them embed fairness, transparency, and accountability deep into their AI systems.

– Asim Butt – AI Ethics/Risk Auditor

Thanks to the rigorous program at BABL AI, I am now equipped with the expertise to consult companies and governments on responsible AI. I can guide organizations in building AI products that prioritize fairness, transparency, and explainability.

Additionally, I can assist in sourcing AI products that adhere to responsible AI principles, educate customers about the risks associated with generative and discriminative AI, develop responsible AI governance capabilities, and collaborate with governments to implement responsible AI initiatives. My journey with BABL AI has been a remarkable one.

– Abhinav

I have done many training programs in the field of AI Ethics and AI Assessments and/or Audits and I can say that the AI and Algorithm Auditor Certificate Program from BABL AI is definitely one of the best on the market. Prof. Shea Brown does an excellent job both as Instructor and Mentor. Shea and the BABL AI team were always kind and helpful. We did a cohort based training as the first movers, I must admit that the cohort was amazing and made the training a great journey but it is also suitable for individuals who can do it on their own, adjusting their pace with their other responsibilities.

– Mert

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Algorithmic Risk & Impact Assessments

Essential Tools for AI and Algorithm Auditing

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