Understanding the source of AI bias
AI bias can happen at any point in the deep learning life cycle. Let’s go through bias at those stages one by one:
- Planning: During the planning stage of the machine learning life cycle, biases can emerge as decisions are made regarding project objectives, data collection methods, and model design. Bias may arise from subjective choices, assumptions, or the use of unrepresentative data sources. Project planners need to maintain a critical perspective, actively consider potential biases, engage diverse perspectives, and prioritize fairness and ethical considerations.
- Data preparation: This stage involves the following phases:
- Data collection: During the data collection phase, bias can creep in if the collected data fails to represent the target population accurately. Several factors can contribute to this bias, including sampling bias, selection bias, or the underrepresentation of specific groups. These issues can lead to the creation...
