Algorithmic Bias: The Ghost in the Machine
Definition: Stereotyping, prejudice, or favouritism toward certain groups, affecting data collection, system design, and user interaction.
The “Vibe” Can Be Biased
“Vibe” is subjective. AI models are trained on the internet. The internet is biased. Therefore, the “Vibe” is biased.
- Scenario: You ask for “a list of CEOs.”
- Result: The AI lists 10 men.
- Code Impact: If you use this data to mock up a UI, your UI will implicitly exclude women.
Coding Bias
- Western Bias: AI models are trained mostly on English/Western code. They are great at
Moment.js(Western time) but might struggle with non-Gregorian calendars or RTL (Right-to-Left) text handling for Arabic/Hebrew. - Library Bias: The AI loves React because React is popular. It might tell you “jQuery is bad” even if jQuery is the perfect lightweight tool for your specific static site.
Mitigating Bias in Vibe Coding
- ** Explicit Constraints**: “Generate a diverse set of user personas.”
- Tech Agnosticism: “Evaluate the best tool for this specific requirement, ignoring popularity trends.”
- Audit: Always check AI-generated content (images, copy) for representation before shipping.
The Ethical Responsibility
As a Vibe Coder, you are the Editor in Chief. The AI generates the raw material, but you are responsible for the publication. You cannot blame the algorithm for shipping a biased product.