From the course: Applied AI Auditing in Python
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Making audit recommendations - Python Tutorial
From the course: Applied AI Auditing in Python
Making audit recommendations
- [Instructor] So you've come to the conclusion of an AI audit, but there's still work to do. Informing decisions about what mitigations AI developers make is half the battle. Here, the heuristics aren't so cut and dry and it can be hard to tell what recommendations will make the most tangible changes. Keep in mind that recent research has shown algorithmic audits don't always result in increased accountability. Since our goal is to improve accountability, let's understand how to influence what improvements an AI developer makes post audit. Given what an auditor knows about the AI developer, they have to decide what mitigations may be the most feasible and prioritizes recommendations. Recommendations can range from technical solutions, to increased documentation, model destruction and more. Often auditors are in a good position to understand the organization's ML infrastructure to recommend various solutions that they can make. It's a good idea that auditors provide AI developers with…
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