Students will be able to define AI bias, identify its sources, and apply strategies to mitigate bias in AI-assisted creative work.
Key Ideas
What is AI Bias?
AI bias occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process.
It's not intentional malice, but a reflection of the data it's trained on.
Sources of AI Bias
Training Data Bias: The most common source. If the data used to train the AI reflects existing societal biases (e.g., underrepresentation of certain groups, historical stereotypes), the AI will learn and perpetuate those biases.
Algorithmic Bias: Flaws in the algorithm's design or how it processes information.
Human Bias in Design: The biases of the developers who choose the data, design the algorithms, and interpret the results.
Examples of AI Bias in Creative Contexts
Image Generation: AI generating images that default to certain genders, ethnicities, or body types for professions (e.g., all doctors are male, all nurses are female).
Text Generation: AI producing stereotypical language, reinforcing harmful narratives, or excluding diverse perspectives.
Facial Recognition: AI performing poorly on certain skin tones or facial features.
Strategies for Mitigating Bias
Diverse Training Data: Advocating for and using AI models trained on more representative and balanced datasets.
Bias Detection Tools: Using tools that can analyze AI outputs for signs of bias.
Human-in-the-Loop Review: Always having human oversight and critical review of AI-generated content, especially for sensitive topics.
Prompt Engineering for Inclusivity: Explicitly prompting AI for diversity (e.g., "Generate an image of a diverse group of engineers," "Write a story featuring characters from various cultural backgrounds").
Ethical Guidelines & Audits: Companies and individuals adopting ethical AI principles and conducting regular audits of AI systems.
In-Lesson Activities
Bias Spotting Challenge: Provide students with several AI-generated images or text snippets (some with subtle biases, some without). Have them identify any biases they observe and explain why they think it's biased.
Inclusive Prompting Workshop: Give students a biased AI output (e.g., an image of only male CEOs). Challenge them to rewrite the prompt to generate a more diverse and inclusive result.
Talking Points
"AI is a mirror, reflecting the biases present in the data we feed it. Our responsibility is to clean that mirror."
"Diversity in data and diversity in development teams are crucial for building fairer AI."
"As creative professionals, we have a powerful role to play in shaping how AI is used responsibly and ethically."