Module 5 Flashcards
What should stakeholders do early in the AI system development process?
- Define and agree on the goal for using AI
- Assess whether AI is suitable for the mission and purpose
- Set parameters defining what is important to success
- Determine at what frequency does the stakeholder group need to meet to evaluate success and mitigate issues during the development lifecycle
- Establish who is ultimately responsible for any risks and mitigations, and of any failures of the system once it is implemented
- Follow the organization’s guide (or create one!)
What questions can the stakeholder group answer to define the business case?
- What is the cost/benefit analysis?
- What are the trade-offs in using AI/ML vs. other solutions?
- What will the organization’s declared position on AI use be, both externally and internally?
Who should be the stakeholders?
- AI governance officers
- Privacy experts
- Security experts
- Procurement experts (sometimes)
- Subject matter experts
- Legal team
What steps must stakeholders take to evaluate whether the AI system is meeting its goals appropriately?
Know:
- The data needed for the training algorithm
- What policies are applicable
- What happens if the AI performs poorly – what impacts could this have on individuals and the organization
- What is the organization’s risk tolerance (ensure the stakeholder group agrees on it and document any decisions)
- Have different methodologies to evaluate risk that you can use routinely
List 4 risk evaluation methodologies
- Probability and severity harms matrix
- HUDERIA risk index number
- Risk mitigation hierarchy
- Confusion matrix
Who should be included in your communication plan?
- Regulators
- Consumers
What should you communicate to regulators?
- Compliance and disclosure obligations
- Explainability
- Risks and mitigation processes
- Data and risk classifications
What should you communicate to consumers?
- Transparency as to the functionality of AI
- What data will be used and how
What should be included in an AI algorithmic assessment?
- Data issues
- Decisions the stakeholder group has made (risks and mitigations, individual responsible, etc.)
- Document appropriate uses of the AI system
How do stakeholders identify risks?
- Conduct a risk analysis and determine the contributing factors
- Classify risks appropriately
- Determine what risks can be mitigated
How should you determine what tests to run on your AI system?
The risks you have should inform testing and should consider:
- Purpose
- Algorithm type
- Whether you are integrating with third party tools
- The regulations applicable to your sector
List 7 types of testing
- Accuracy
- Robustness
- Reliability
- Privacy
- Interpretability
- Safety
- Bias
List 3 types of bias
- Computational bias
- Cognitive bias
- Societal bias
What can you do to ensure your testing is comprehensive?
- Include cases the AI has not previously seen (“edge” cases) and “unseen” data (data not part of the training data set)
- Include potentially malicious data in the test
- Conduct repeatability assessments to determine if the AI produces the same (or a similar) outcome consistently
What are counterfactual explanations?
A counterfactual is essentially a statement of how the world would have to change in order to achieve a different outcome
