Responsible AI is Not an Option. Here’s How You Can Achieve it

  1. Build a diverse team
  2. Establish a robust foundation
  3. Respect the power of data
  4. Ensure explainability
  5. Establish Ethical AI guardrails
  6. Make AI innovation adoption efficient
  7. Establish proper AI governance
  8. Evangelize responsibility

1. Build a diverse team

2. Establish a robust foundation

  • Proper use of historical training and testing data
  • Well-defined metrics for acceptable performance
  • Careful model architecture selection
  • Processes for model stability testing, simulation, and governance

3. Respect the power of data

4. Ensure explainability

  • Was the model built properly?
  • What are the risks of using the model?
  • What features or behaviors drive the model?
  • Is the model ethical?
  • When does the model degrade?

5. Establish ethical AI guardrails

6. Make AI innovation adoption efficient

  • Shared code repositories
  • Approved model architectures
  • Sanctioned variables
  • Approved AI intellectual property (IP) components
  • Established bias testing
  • Stability standards for active models

7. Establish proper AI governance

  • Accountability is achieved only when each decision that occurs during the model development process is recorded in a way that cannot be altered or destroyed.
  • Fairness requires that neither the model, nor the data it consumes, be biased.
  • Transparency is necessary to adapt analytic models to rapidly changing environments without introducing bias.
  • Responsibility is a heavy mantle to bear, but our societal climate underscores the need for companies to use AI technology with deep sensitivity to its impact.

8. Evangelize responsibility

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