Introduction to AI Ethics

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  • HCD: Human-Centered Design for AI
  • Identifying Bias in AI
  • AI Fairness
  • Model Cards

Ethics is a conversation.

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1. HCD: Human-Centered Design for AI

1. Understand people’s needs to define the problem

2. Ask if AI adds value to any potential solution

3. Consider the potential harms that the AI system could cause

4. Prototype, starting with non-AI solutions

5. Provide ways for people to challenge the system

6. Build safety measures

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2. Identifying Bias in AI

Garbage in, garbage out.

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  1. Historical Bias (from the state of the world in which the data is generated)
  2. Representation bias (when datasets poorly represents the model it will serve)
  3. Measurement bias (when accuracy varies across groups)
  4. Aggregation bias (when groups are inappropriately combined)
  5. Evaluation bias (when the benchmark data does not represent the population the model will serve)
  6. Deployment bias (when the problem the model is intended to solve is different from the way it is actually used)

3. AI Fairness

  • if approval rate is equal across genders
  • if gender is removed from data-set
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  1. Demographic parity / statistical parity
  2. Equal opportunity (True Positive Rate — sensitivity — same for each group)
  3. Equal accuracy (accuracy same for each group)
  4. Group unaware / “Fairness through unawareness” (remove all group membership information from the data-set)

4. Model Cards

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  1. Model details
  2. Intended Use
  3. Factors (what factors impact the model?)
  4. Metrics
  5. Evaluation data
  6. Training data
  7. Quantitative Analyses
  8. Ethical Considerations
  9. Caveats and Recommendations
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A great power comes with great responsibilities.




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Maxime Pawlak

Maxime Pawlak

#dataScientist #techplorator #prototypeur #entrepreneur

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