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bias detection and mitigation

AI systems have systematic biases based on their training data and design. Professional users must recognize these patterns and compensate for them.

Here are the common patterns we must look for:

  • CULTURAL BIAS: Towards western, english speaking perspectives.
  • TEMPORAL BIAS: Training data has cutoff dates, missing recent developments.
  • AUTHORITY BIAS: Favours mainstream established viewpoints over innovative approaches.
  • DEMOGRAPHIC BIAS: May underrepresent certain groups in examples and assumptions
  • SOLUTION BIAS: Always wants to provide answers, even when "unknown" is correct
  • COMPLEXITY BIAS: May oversimplify nuanced issues or miss important exceptions

Lets do an exercise to see if we can identify systematic biases.

Example prompt: "What are effective networking strategies for business professionals?"

  1. Submit to your preferred AI tool

    2. Note the specific strategies suggested

  2. Analyse for cultural assumptions

    3. Does it assume Western business culture? Handshakes? Direct communication?

  3. Test bias mitigation

    4. Follow up: "How would these strategies differ in Asian business cultures?"

  4. Compare responses

    5. How much did the advice change? What was missed initially?

Authority bias detection example:

Prompt: "What's the best approach to employee performance management?"

  1. Note the mainstream approach suggested

    2. Probably traditional annual reviews, goal-setting, etc.

  2. Challenge with alternative perspective

    3. Follow up: "What would critics of traditional performance management argue?"

  3. Request innovative approaches

    4. Follow up: "What are 3 unconventional but effective alternatives?"

  4. Evaluate perspective broadening

    5. Did AI provide genuinely different viewpoints?

Solution bias detection:

Prompt: "How can I guarantee my startup will be successful?"

  1. Check if AI provides false certainty

    2. Does it promise guaranteed success or acknowledge uncertainty?

  2. Look for balanced response

    3. Does it mention failure rates and uncontrollable factors?

  3. Test honesty about limitations

    4. Follow up: "What can't be controlled or predicted about startup success?"

Success Target: Identify bias patterns in all 3 tests and demonstrate mitigation strategies.

In this case, you can also use AI to counter common biases. This is another set of prompts you can add to your AI arsenal. Here is an example prompt:

CULTURAL PERSPECTIVE CHECK:

"How would this advice differ for professionals in [specific region/culture]?"

ALTERNATIVE VIEWPOINT REQUEST:

"What would critics of this mainstream approach argue?"

UNCERTAINTY ACKNOWLEDGMENT:

"What are the limitations of this advice? When would it not work?"

DEMOGRAPHIC INCLUSION CHECK:

"How might this be perceived differently by [specific demographic group]?"

INNOVATION CHALLENGE:

"What are 3 unconventional approaches that challenge this traditional thinking?"

TEMPORAL RELEVANCE CHECK:

"What recent developments might change this analysis?"

The only remaining thing to do now is apply those prompts to the outputs of other prompts.

  1. Choose a recent AI output from your work

    2. Pick something important that others will see

  2. Apply 3 bias-checking prompts

    3. Use the ones most relevant to your content

  3. Evaluate the additional perspectives

    4. What important viewpoints were missing initially?

  4. Revise your original output

    5. Incorporate the broader perspective into a more complete version

Conclusion

I would save the above bias checking prompts to your prompt library and comit to using them on important AI outputs. Remember a good prompt engineer doesn’t just ask a question and send in what they return. They take the time to make sure everything is accurate and fully thought through. This will let you see the same context from a much greater number of angles than ever before.