The Responsible AI: A Framework for making ethical AI systems

Why AI needs to be responsible?

The Framework

1. Transparency

How can the output churned out by an AI system be explained?

Is the explanation logical and simple to understand to a generic audience?

Does the explanation align with other principles of a responsible AI system?

2. Accountability

What is the potential impact of the outcome of an AI system?

Who is governing the accountability of the AI system developed?

How is the accountability measured and deployed?

3. Fairness

Are the attributes used to train the model prone to data biases?

Can the data that is creating bias eliminated?

Can the data collection methods investigated further to reduce bias?

4. Reliability and Safety

Are the model performance metrics consistent in multiple scenarios, especially in outlier cases?

What will be the impact on the stakeholders if the AI system doesn’t behave as expected?

Is the model output repeatable and reproducible?

5. Privacy and Security

Is the data acquired to train the AI model acquired legally and with transparency?

Will the outcomes delivered by the AI system compromise the privacy of an individual or groups on individuals?

Is the AI system using data securely?

6. Inclusiveness

Does the AI system developed to ensure that it includes different categories of individuals or organizations in the specific context?

Are there any categories of data that need to be handled exceptionally to ensure that they are included?

Does the experience that the AI system provides excludes any specific types of categories? If yes, then is there anything that can be done about it?

Bringing it all together

WITH GREAT POWER COMES GREAT RESPONSIBILITY.

References:

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#Data and #AI Strategist @ Microsoft. Impact driven. Executive-level interpersonal skills. Hands-On. #WorldTraveller. #Blogger

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Pradeep Menon

Pradeep Menon

#Data and #AI Strategist @ Microsoft. Impact driven. Executive-level interpersonal skills. Hands-On. #WorldTraveller. #Blogger

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