Handling Ambiguity and Bias in AI Responses

In this chapter, we'll guide you through prompt engineering methods and sharing some code snippets to help you understand how to handle ambiguity and bias.

Defining the Problem through a Movie Example to Understand Ambiguity and Bias

Consider we have a movie database, and we're trying to generate AI responses to questions about the content, characters or themes in the movies. Depending on how the prompts are constructed, we can end up with ambiguous or biased responses. For instance, if we ask the AI "Who is the protagonist in the movie?", the term "protagonist" may be interpreted differently – as the main character, the character with the most screen time, or the character, evoking the most empathy. This is Ambiguity.

When we ask "Tell me about the movie character Darth Vader", if the AI tends to describe the character as 'evil' without considering the intricacies of the character's evolution throughout the series or the motivation behind his actions, that would be an instance of Bias.

Ambiguity Handling in Prompts through Code

Let's consider OpenAI’s GPT-3 model. In this example, we have a text prompt "Tell me about a great movie." This is a quite ambiguous prompt and the AI could potentially generate a wide range of responses.

import openai
openai.api_key = 'your-api-key'

response = openai.Completion.create(
  engine="text-davinci-002",
  prompt="Tell me about a great movie.",
  max_tokens=60
)
print(response.choices[0].text.strip())

To handle the ambiguity in the prompt, you can make the prompt more specific by including more details or parameters:

response = openai.Completion.create(
  engine="text-davinci-002",
  prompt="Tell me about a great mystery thriller movie.",
  max_tokens=60
)
print(response.choices[0].text.strip())

In this revised prompt, the genre of the movie is now specified which helps in generating less ambiguous responses.

Bias Handling in Prompts through Code

Now, let's consider an example where we want to generate a description for the character Darth Vader from the Star Wars movie series.

response = openai.Completion.create(
  engine="text-davinci-002",
  prompt="Tell me about the movie character Darth Vader.",
  max_tokens=60
)
print(response.choices[0].text.strip())

To reduce bias, we can adjust the prompt to ensure it does not lean towards a specific opinion or narrative:

response = openai.Completion.create(
  engine="text-davinci-002",
  prompt="Provide an unbiased description about the movie character Darth Vader.",
  max_tokens=60
)
print(response.choices[0].text.strip())

By specifying that we want an "unbiased description", we mitigate the risk of generating responses that may support a biased viewpoint.

Prompt Modification Techniques

Code tweaks can help, but it’s always useful to know how to modify prompts to reduce ambiguity and bias. Below are some common techniques:

1. Adjust word specificity: Be clear on what you want. Instead of using "tell me about a great movie", use "tell me about an critically acclaimed animated movie".

2. Unbiased language: Request the AI to provide an "unbiased" or "objective" description.

3. Detail addition: The more information you provide, the better the AI's understanding. If a character has multiple roles through different movies, specify which movie/role you're asking about.

4. Control randomness (temperature): A parameter which controls the randomness of the AI's output. Higher temperatures cause the model to generate more random outputs, while lower temperatures make the AI more deterministic and focused.

Conclusion

In this chapter, we learned about prompt engineering and its importance in handling ambiguity and bias in AI responses. We explored useful techniques and shared code snippets outlining how to modify prompts for reduced ambiguity and bias. Remember, effective prompt engineering is more art than science, and it requires practice and experimentation. So keep exploring and refining your skills!