Definition: The difference between a model's expected predictions and the true values, indicating a model's systematic error.
Better definition: When your computer stubbornly sticks to its preconceived notions, even when it's dead wrong.
Where does this fit in the AI Landscape?
Bias is an essential concept in AI and machine learning, as it helps researchers understand a model's accuracy and robustness. Balancing bias and variance is crucial for creating reliable and effective models, and it's a key consideration when developing AI systems.
What are the real world impacts of this?
Understanding bias in machine learning models ensures that the predictions made by these models are accurate and fair. This contributes to the reliability and trustworthiness of AI technologies we interact with daily. For developers, understanding and addressing bias is critical for building fair and ethical AI systems.
What could go wrong in the real world with this?
A biased model is trained to predict the outcome of coin tosses but insists on always predicting "heads," refusing to acknowledge the possibility of "tails."
In ML, bias refers to the error introduced by approximating a real-world problem. High bias can lead to underfitting. It's crucial to maintain a balance between bias and variance to ensure the model generalizes well.