Crafting Bias-Free AI: Transforming Content with Inclusive Prompts - Part 2
Is your content from the 50ies?!
Picture description: This illustration includes a Black woman, a Middle Eastern man, an Indian woman, and a disabled Hispanic man, all engaged in AI development tasks. This scene emphasizes global diversity and collaborative innovation.
Bias means - old world, in the worst way!
Part 1 was all about Bias in Data in AI, and the basic concepts of why we have it.
Algorithms are neither good nor bad, but never neutral
So we have to address this in the content we produce from blog posts to pictures or the next job description and we have to do it now!
We already have an AI gender adaptation gap and considering the numbers are from May 2023 the gap is already widening. The daily usage numbers from a study done in May 2024 are also pointing in this direction. #genAI can be a game-changer for diversity and inclusion, but currently, we are building worlds from the 50ies. And in all bluntness, nearly all of AI is male and predominantly white, a bit of Asian, but that’s it…
Do what you can do - use the tool right
GenAI is easy to use - write something in the chat view and my magic stuff is made. With ease of use, laziness comes in hand so getting your input as bias-free as possible, is work. To make it es easy for you as possible and as stable as you as possible, I will give you prompt frameworks.
Stable?! Yeah… prompts need to match the LLM you use
Not everyone uses ChatGPt, and even there we have different versions. The prompt has the biggest impact on your output, followed by the context you provide. How good the output is on a general level depends on the LLM you choose. So one prompt can work in ChatGPT but performs less in Claude or the other way around. Testing your prompts is necessary and by providing you with a framework to design prompts and not only example prompts, you have a much more stable solution.
Framework for Creating Bias-Reduced AI Prompts
1. Define Clear Objectives with Bias Awareness:
Explicitly aim to create content that includes underrepresented groups.
Understand the type of bias most relevant to the content being created.
2. Incorporate Inclusive and Neutral Language:
Use language that does not assume or impose identities, abilities, or roles.
Describe characters and scenarios using attributes that do not reinforce stereotypes.
3. Contextual Details for Fair Representation:
Ensure representation of diverse characters in roles and situations that are non-stereotypical.
Include a variety of cultural, socioeconomic, and personal backgrounds to enrich narratives.
4. Encourage Realistic and Positive Representation:
Integrate realistic portrayals that avoid idealizing or demonizing any group.
Focus on positive, empowered portrayals of individuals from various backgrounds.
5. Counteract Specific Biases:
For gender bias, rotate genders in different roles or use gender-neutral characters.
To counter racial bias, describe characters of various races and ethnicities in empowered roles and everyday situations.
Address disability bias by including characters with disabilities in active, positive roles.
Mitigate socioeconomic bias by showcasing success and virtue across various economic backgrounds.
Avoid cultural bias by representing different cultural norms and values equally and respectfully.
6. Feedback Loop for Continuous Improvement:
Implement a process for collecting feedback on AI outputs, especially from those who represent the diversity intended in the content.
Regularly update prompts based on feedback to continuously reduce bias.
7. Educate Users on Bias Identification and Mitigation:
Provide users with examples and guidelines on recognizing and adjusting for biases in their prompts.
Offer training on the implications of biases and the importance of diversity and inclusion in AI-generated content.
Some examples, still basic level ( check the other post here)
Example 1:
Original Prompt: "Describe a successful entrepreneur."
Revised Prompt: "Describe a successful entrepreneur who identifies as non-binary and uses they/them pronouns. They founded a tech company that develops software for learning disabilities."
Example 2:
Original Prompt: "Write a romantic story."
Revised Prompt: "Write a romantic story featuring a visually impaired woman and her partner, who is an immigrant from Brazil. The story should highlight their cultural and personal exchanges that enrich their relationship."
Example 3:
Original Prompt: "Tell a story about a family dinner."
Revised Prompt: "Tell a story about a family dinner where family members include a transgender teen, their two moms, and grandparents from a mixed racial background. Focus on the celebration of the teen's recent achievements in science."
To give you a better understanding of the frameworks, I made this depend version for you, addressing the “default settings” we see in most of #GenAI
Deepened Framework with Typical Examples of Bias
1. Gender Bias
Default Setting: AI tends to associate jobs like engineers or CEOs with males and nurses or teachers with females.
Mitigation Prompt: "Write a story about a successful female CEO of a tech company and a male nurse who works in pediatric care, highlighting their professional achievements and personal strengths."
2. Racial and Ethnic Bias
Default Setting: AI might generate content that predominantly features Caucasian individuals in positive roles, while marginalizing or stereotyping other races and ethnicities.
Mitigation Prompt: "Describe a day in the life of a highly respected physicist who is of Southeast Asian descent, including details about their latest groundbreaking research and their mentoring of young scientists from diverse backgrounds."
3. Disability Bias
Default Setting: People with disabilities are often portrayed as objects of pity or as inherently less capable.
Mitigation Prompt: "Tell a story about a group of friends who include an individual using a wheelchair, where they are planning and executing an adventure vacation, focusing on the skills and contributions of the person with the disability."
4. Sexual Orientation and Gender Identity Bias
Default Setting: LGBTQ+ characters are often absent, or their narratives are centered around trauma or conflict regarding their identity.
Mitigation Prompt: "Create a narrative about a stable, happy LGBTQ+ family preparing for a festive community event, emphasizing their love, support for each other, and involvement in their community."
5. Socioeconomic Bias
Default Setting: Success and virtue are frequently associated with higher socioeconomic status, while poverty is linked with negative attributes.
Mitigation Prompt: "Develop a story about a resourceful and innovative teacher from a low-income neighborhood who leads her students to win a national science competition."
6. Cultural Bias
Default Setting: Western norms and values are often treated as universal, while non-Western cultures are exoticized or misrepresented.
Mitigation Prompt: "Describe a multinational conference on climate change, highlighting the contributions of scientists from Africa and South America, focusing on their unique approaches and solutions."
It seems I´m writing a workshop sheet for you, but here we go, there is more
Implementing the Framework
This deepened framework provides clear examples of common biases and tailored prompts to help counteract them. By using these examples, AI prompt engineers and users can:
Understand and identify typical biases in AI-generated content.
Create prompts that actively promote diversity and inclusion.
Evaluate and refine AI outputs based on feedback, especially from communities represented in the content.
Implementing and Testing Your Prompts
Once you've crafted your inclusive prompts, the next critical step is implementation and testing. This process is essential because, despite our best intentions, the output from AI can still reflect unintended biases or misinterpretations. Testing allows us to see how the AI interprets the prompts and generates content, providing a clear view of any adjustments needed to achieve truly inclusive results.
Why Testing is Crucial
Testing your prompts with the AI model serves multiple purposes:
Accuracy of Representation: It helps ensure that the AI correctly understands and executes the intent behind the inclusivity of the prompt.
Uncovering Hidden Biases: Sometimes, biases are not apparent until you see them manifested in the outputs. Testing exposes these subtle biases.
Performance Evaluation: It allows you to evaluate whether the AI is producing content that is engaging, accurate, and truly reflective of the diversity intended.
Methods for Testing Prompts
Iterative Testing: Use a trial and error method where prompts are repeatedly tested and refined based on the AI's output. This iterative process helps hone in on the most effective language and structures for promoting inclusivity.
A/B Testing: Compare the outcomes of traditional prompts with your new inclusive prompts. This can illustrate the impact of changes and showcase the benefits of inclusivity in AI-generated content.
User Testing: Involve real users in testing, especially those who represent the demographics mentioned in your prompts. Their insights will be invaluable in assessing the authenticity and sensitivity of the content.
Gathering and Implementing Feedback
Feedback is a cornerstone of refining AI prompts:
Diverse Perspectives: Include feedback from a wide range of people, particularly those from the communities represented in your prompts. This diversity in feedback helps ensure that multiple perspectives are considered, which enhances the inclusivity of the content.
Structured Surveys: Use structured feedback forms to gather specific insights about different aspects of the AI-generated content, such as accuracy, inclusivity, relevance, and engagement.
Community Engagement: Engage with online forums, social media groups, and other communities to get broader feedback. This can also increase awareness and acceptance of your efforts towards creating bias-free AI.
Professional Consultation: In some cases, consulting with experts in diversity, equity, and inclusion can provide deeper insights into the subtleties of biases and how to avoid them.
Implementing Changes Based on Feedback
Once feedback is collected, it's crucial to act on it:
Prompt Adjustments: Modify prompts based on feedback to address any biases or inaccuracies noted by users.
Continuous Learning: AI and societal norms are always evolving. Continuously update your understanding and approach to prompting to keep up with these changes.
Documentation of Changes: Keep a record of the feedback and the changes made. This not only helps in tracking improvements but also supports transparency with your audience or users.
The path to bias-free AI is ongoing and requires our diligent effort. By implementing inclusive prompting strategies, we can significantly enrich AI-generated content, making it truly reflective of our diverse society. I encourage you to apply these strategies, experiment with your prompts, and share your experiences. Together, we can lead the charge towards a more equitable AI landscape.
Barbara - over and out