Generative AI Fundamentals: How Bias Can Enter the System at Any Point in the ML Lifecycle

Learn how bias can affect the ML lifecycle at any stage, such as data collection, model development, or model deployment. Find out how Google’s AI Principles aim to prevent and mitigate bias throughout the ML lifecycle.

Question

According to Google’s AI Principles, bias can enter into the system at only specific points in the ML lifecycle.

A. False
B. True

Answer

A. False

Explanation

The correct answer is A. False.

According to Google’s AI Principles, bias can enter into the system at any point in the ML lifecycle, not only at specific points. The ML lifecycle consists of several stages, such as data collection, data preparation, model development, model deployment, and model monitoring. Bias can arise at any of these stages due to various factors, such as:

• Data bias. This refers to the quality, quantity, diversity, and representativeness of the data used to train and evaluate the ML model. Data bias can result from incomplete, inaccurate, outdated, or skewed data that does not reflect the real-world distribution or diversity of the target population or domain.
• Algorithmic bias. This refers to the design, implementation, and optimization of the ML algorithm or model. Algorithmic bias can result from the choice of the model architecture, the objective function, the hyperparameters, the regularization techniques, or the evaluation metrics that affect the model’s performance and behavior.
• Human bias. This refers to the influence of human values, preferences, assumptions, and expectations on the ML model. Human bias can result from the lack of awareness, transparency, accountability, or diversity of the stakeholders involved in the ML lifecycle, such as the developers, the users, the customers, or the regulators.

Google’s AI Principles emphasize the importance of addressing bias throughout the entire ML lifecycle, rather than suggesting that bias can only enter the system at specific points. The goal is to mitigate and prevent bias at every stage of the ML development process, by applying best practices, such as:

• Data auditing and analysis. This involves inspecting and understanding the data sources, the data collection methods, the data labels, and the data distribution, to identify and remove any potential sources of data bias.
• Model testing and evaluation. This involves measuring and comparing the model’s performance and behavior across different groups, scenarios, and domains, to identify and reduce any potential sources of algorithmic bias.
• Human oversight and feedback. This involves involving and empowering diverse and representative stakeholders in the ML lifecycle, to provide input, guidance, and evaluation of the model’s outcomes and impacts, to identify and address any potential sources of human bias.

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