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International AI Treaty = Soft Text

PLUS: IAPP's AI Governance Course Notes

I’m testing out a new format this week. I’ll list some of the latest headlines in AI legal news andddd that’s it. Click on a headline if you want more deets. Let’s dive in!

On the docket today:

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IAPP’s AI Governance Course Notes

Alrighty, it’s time to geek out and keep learning about AI Governance!!!

We are in Module 3: Understanding the AI Development Life Cycle. Luckily, this is a very short module and I will be able to share my notes from module 4 next week. 🚀📚

Module 3: Understanding the AI Development Life Cycle

The AI development life cycle requires meticulous planning, design, development, and implementation phases. This structured approach ensures AI projects align with organizational goals while mitigating risks and adapting to evolving needs.

Planning Phase:

  • Business Objectives and Requirements: It's crucial to define the business problem at the outset. AI can address various business problems, predominantly classified into three categories:

    • Classification: Differentiating data into categories.

    • Regression: Predicting future outcomes based on past data.

    • Recommendation: Suggesting actions or choices.

  • AI Use Cases: These must align with the organization’s mission. This involves identifying gaps in current processes and determining how AI can bridge these gaps.

  • Project Scope and Governance:

    • Scope Determination: Prioritize use cases based on business needs, considering impact, effort, and fit with organizational goals.

    • Governance Structure: Establish who is responsible for AI governance, policy formulation, and overseeing the development and testing processes. An executive champion can facilitate broader support.

Design Phase:

  • Data Strategy:

    • Data Collection: Assess the required type, amount, and source of data, whether internal or external.

    • Data Quality and Format: Focus on data quality to avoid 'garbage in, garbage out' scenarios and consider the data format (structured, unstructured, static, streaming).

    • Privacy Technologies: Implement techniques like anonymization, data minimization, differential privacy, and federated learning to enhance privacy.

  • AI System Architecture and Model Selection:

    • Algorithm Choice: Select based on accuracy, interpretability, and alignment with the objectives.

    • Model Requirements: Consider constraints such as time and accuracy.

Development Phase:

  • Model Building and Feature Engineering:

    • Feature Definition and Engineering: Use domain knowledge to create and select relevant features efficiently.

    • Model Training and Testing: Iteratively train, test, evaluate, and retrain models to ensure performance aligns with business objectives and can handle new data effectively.

Implementation Phase:

  • Readiness Assessment and Deployment:

    • Readiness Assessment: Evaluate if the model meets quality standards and is ready for production.

    • Deployment: Plan for continuous monitoring post-deployment.

  • Continuous Monitoring and Maintenance:

    • Performance Monitoring: Watch for accuracy deviations, irregular decisions, and data drift.

    • Ongoing Maintenance: Regularly update the model to address changes in data and operational environments.

In summary, the AI development life cycle emphasizes defining clear business objectives, ensuring high-quality and privacy-compliant data, careful model selection and training, and ongoing monitoring and maintenance post-deployment.

That’s all for today!

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