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AI Data Acquisition and Governance:
Considerations for Success
Presented by:
Kirsten Gokay
Senior Product Manager
Defining AI Governance
2
Defining AI Governance
AI Governance is the framework that guides an
organization's AI usage and implementation.
There is ...
Key Areas of AI Governance
• Performance
• Transparency
• Ethics
Performance
• Accuracy
• How well does AI on real-world data?
• Bias/fairness
• Are you coding human bias into your
AI?
Performance
Accuracy
• Precision/recall
• Which is more important and
when
• Completeness of context
• Ensuring AI has all...
Transparency
• Explainability
• Provide clear insight into why AI
performs a certain way
• Objective
• What is the objecti...
Ethics
• Intent
• What is the intent of the AI
implementation – why is it being built?
• Responsibility
• What does respon...
Case Study: The Consequences of Bias
• COMPAS algorithm
• Facial recognition
• Amazon hiring
• US healthcare
Data Governance
10
Data Governance for AI
Data governance is crucial for working
within the organization's AI governance
framework.
It includ...
Data Governance
Availability
• The data is accessible and
consumable by those who need
it
Usability
• The data is structur...
Data Governance
Integrity
• Data maintains its structure,
qualities, completeness across
its lifecycle
• Data consistency
...
Case Study: When Security Fails
• Equifax Data Breach: 143 million
• Facebook: 419 million
• Aadhaar: 1.1billion
The Growing Necessity of AI
15
The Growing Necessity of AI
AI allows organizations to scale quickly and
efficiently.
It can provide data quickly, improve...
Opportune Areas for AI
User Experience
• Chatbots
• Product
recommendations
• Routine/regimen
recommendations
Process Scal...
Training Data Pipeline &
Maintenance
18
Training Data Pipeline & Maintenance
• Data acquisition
• Data annotation
• Auditing the data
• Updating the model(s)
Training Data Pipeline
Data Acquisition
• Readily available data
• Open-source datasets
• Third party vendors
Data Annotat...
Training Data Pipeline & Maintenance
Auditing the Data
• Ensure the annotated data is
accurate
• Datasets should be well-
...
Case Study: Maintaining AI
• Wellio: using human-in-the-loop data
annotation in the data pipeline
• Microsoft Tay: a publi...
Thank You!
23
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data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset.

This session will cover:

Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations.
Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training.
AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently.
Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances.
Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy.
3 key takeaways or attendee benefits of the session:

Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost.
How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework.
Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.

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AI Data Acquisition and Governance: Considerations for Success

  1. 1. AI Data Acquisition and Governance: Considerations for Success Presented by: Kirsten Gokay Senior Product Manager
  2. 2. Defining AI Governance 2
  3. 3. Defining AI Governance AI Governance is the framework that guides an organization's AI usage and implementation. There is no one-size-fits-all framework; each org must determine what best models its values and suits its needs.
  4. 4. Key Areas of AI Governance • Performance • Transparency • Ethics
  5. 5. Performance • Accuracy • How well does AI on real-world data? • Bias/fairness • Are you coding human bias into your AI?
  6. 6. Performance Accuracy • Precision/recall • Which is more important and when • Completeness of context • Ensuring AI has all the context when making predictions Bias/Fairness • Sampling bias • How you collect data, from where, by whom • Training data bias • Who is annotating the data
  7. 7. Transparency • Explainability • Provide clear insight into why AI performs a certain way • Objective • What is the objective or goal of this AI implementation
  8. 8. Ethics • Intent • What is the intent of the AI implementation – why is it being built? • Responsibility • What does responsible AI look like
  9. 9. Case Study: The Consequences of Bias • COMPAS algorithm • Facial recognition • Amazon hiring • US healthcare
  10. 10. Data Governance 10
  11. 11. Data Governance for AI Data governance is crucial for working within the organization's AI governance framework. It includes: • Availability • Usability • Integrity • Security
  12. 12. Data Governance Availability • The data is accessible and consumable by those who need it Usability • The data is structured, labeled, and easy to use
  13. 13. Data Governance Integrity • Data maintains its structure, qualities, completeness across its lifecycle • Data consistency Security • Data is protected from corruption, unauthorized use, or modification across its lifecycle
  14. 14. Case Study: When Security Fails • Equifax Data Breach: 143 million • Facebook: 419 million • Aadhaar: 1.1billion
  15. 15. The Growing Necessity of AI 15
  16. 16. The Growing Necessity of AI AI allows organizations to scale quickly and efficiently. It can provide data quickly, improve user experiences, and even save lives.
  17. 17. Opportune Areas for AI User Experience • Chatbots • Product recommendations • Routine/regimen recommendations Process Scaling • Legal/financial document extraction • Security screening • Updating information across systems Robotics • Vehicles • Surgery • Manufacturing • Agriculture 17
  18. 18. Training Data Pipeline & Maintenance 18
  19. 19. Training Data Pipeline & Maintenance • Data acquisition • Data annotation • Auditing the data • Updating the model(s)
  20. 20. Training Data Pipeline Data Acquisition • Readily available data • Open-source datasets • Third party vendors Data Annotation • E.g., image classification, audio transcription, video event annotation
  21. 21. Training Data Pipeline & Maintenance Auditing the Data • Ensure the annotated data is accurate • Datasets should be well- rounded • Account for edge cases Updating the Model(s) • Models don't need to be static! • Update models frequently to reflect the real world and changing data
  22. 22. Case Study: Maintaining AI • Wellio: using human-in-the-loop data annotation in the data pipeline • Microsoft Tay: a public chatbot gone wrong
  23. 23. Thank You! 23

data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset. This session will cover: Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations. Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training. AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently. Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances. Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy. 3 key takeaways or attendee benefits of the session: Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost. How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework. Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.

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