Auditing AI
Where to Start and What You Should Know
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Experience the best and only credible AI auditing and assessing course material on the internet, coupled with deep research. This course, led by Jax Scott, is meticulously designed to equip you with the skills needed to audit AI systems effectively.
Check it OutWe believe in the power of knowledge sharing. That’s why we’re offering you free access to the content used in the research for the creation of our AI Auditing Course. Dive into these resources to expand your knowledge and stay ahead in the field of AI auditing.
AI Assurance Audit of FakeFinder, an Open-Source Deepfake Detection Tool
The Algorithm Audit: Scoring the Algorithms That Score Us
Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies
Algorithmic Fairness: Choices, Assumptions, and Definitions
Algorithms for Decision Making
Attention and Augmented Recurrent Neural Networks
Bias Testing for Generalized Machine Learning Application
Biases in Large Language Models: Origins, Inventory, and Discussions
Black Loans Matter: Fighting Bias for AI Fairness in Lending
The Conversation - Biased Algorithms: Here's a More Radical Approach to Creating Fairness
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
Deep Learning Ideas That Have Stood the Test of Time
Diversity in Sociotechnical Machine Learning Systems
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models
Examining the Black Box: Tools for assessing algorithmic systems
Extracting Private Data from a Neural Network
Fairness and Machine Learning: Limitations and Opportunities Book
Fairness and Machine Learning: Limitations and Opportunities PDF
GAO - Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities
Google DeepMind Reinforcement Learning Series 2021
Google Machine Learning Crash Course with TensorFlow APIs
HELM: A Holistic Framework for Evaluating Foundation Models
H.R.2231 - Algorithmic Accountability Act of 2019
How to Build Accountability into Your AI
ICO - Explaining Decisions Made with AI
ICO - Guidance on the AI Auditing Framework: Draft guidance for consultation
Machine Learning for High-Risk Applications: Approaches to Responsible AI
OECD: Recommendation of the Council on Artificial Intelligence
ProPublica: How We Analyzed the COMPAS Recidivism Algorithm
Qiskit Course - Machine Learning Course
Real-World Strategies for Model Debugging
The Seductive Diversion of 'Solving' Bias in Artificial Intelligence
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
Stanford CS221: Artificial Intelligence: Principles and Techniques
StereoSet: Measuring Stereotypical Bias in Pretrained Language Models
A Survey on Bias and Fairness in Machine Learning
TL;DS - 21 Fairness Definition and Their Politics
Training language models to follow instructions with human feedback
Tutorial #1: Bias and fairness in AI
UC Berkeley CS 294: Fairness in Machine Learning
UCLA NLP Tutorial: Bias and Fairness in Natural Language Processing
Upturn: An Examination of Hiring Algorithms, Equity, and Bias
What does "fairness" mean for machine learning systems?
What is AI Bias Mitigation, and How Can it Improve AI Fairness?
The Algorithm Audit: Scoring the Algorithms that Score Us
Algorithmic Bias and Risk Assessments: Lessons from Practice
Algorithmic Impact Assessment: User Guide
Assembling Accountability: Algorithmic Impact Assessment for the Public Interest
babl - The Current State of AI Governance
California AB No. 331: Automated Decision Tools
Colorado SB21-169: Protecting Consumers from Unfair Discrimination in Insurance Practices
COSO: Realize the Full Potential of Artificial Intelligence
European Commission - Ethics Guidelines for Trustworthy AI
Federal Reserve SR 11-7: Guidance on Model Risk Management
ForHumanity AI Risk Management Framework
FTC: Using Artificial Intelligence and Algorithms
Getting from commitment to content in AI and data ethics: Justice and explainability
An Institutional View of Algorithmic Impact Assessments
IBM: Everyday Ethics for Artificial Intelligence
Model Cards for Model Reporting
Model Selection Bias Invalidates Significance Tests
NIST - AI Risk Management Framework
Risk Management in the Artificial Intelligence Act
Unfairness by Algorithm: Distilling the Harms of Automated Decision-Making
Neal Bridges, a veteran cybersecurity expert and influencer, has an impressive cybersecurity track record spanning the United States Air Force, NSA, FBI, and Department of Defense. His expertise in building state-sponsored hacker training units in the Air Force and leading red and purple teams for Fortune 100 companies has made him a formidable figure in the field. Neal has always wanted to give back to the cybersecurity community and have a one-stop shop for important resources out in the wild. That’s why he created Cyber Insecurity.
As a CISO and content creator, he reaches thousands of professionals through the Cyber Insecurity podcast and live stream. He is a respected voice in media on cybersecurity matters, with appearances on Bloomberg, CBS, WRAL News, and KVEO-TV.
Jaclyn "Jax" Scott combines 18+ years of military service with deep expertise in cybersecurity. A seasoned Special Operations Warrant Officer, she's also a cybersecurity leader, engaging in electronic warfare and global cyber operations. As the founder of Outpost Gray and co-host of 2CyberChicks, Jax shares her knowledge widely. Her advocacy includes the "Jax Act" for female veterans. With a Master’s in Cybersecurity from Georgetown, her work spans NATO operations to cybersecurity education, emphasizing risk management, AI security, and penetration testing. Jax's contributions to cybersecurity and national security are both profound and educational.
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