A company has installed a security camer a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company…

The issue described is a direct result of sampling bias. This type of bias occurs when the data used to train a model is not a representative sample of the real-world population or the environment where the model will be deployed. In this case, the security camera footage used for training likely contained a disproportionately high number of images of people from a specific ethnic group labeled as committing theft. Consequently, the model learned a spurious correlation between that group's features and the act of theft, leading to biased and unfair predictions.

1. Google. (n.d.). Responsible AI Practices. In the "Identify types of bias" section

Sampling bias is defined: "occurs when a dataset doesn't reflect the realities of the environment in which a model will run. This can happen if data is collected in a way that over-represents or under-represents certain groups or characteristics." This directly aligns with the scenario.

2. Barocas

S.

Hardt

M.

& Narayanan

A. (2019). Fairness and machine learning: Limitations and opportunities. In Chapter 2

"Sources of Bias

" the authors detail how the composition of a dataset

if not representative of the broader population (i.e.

sampling bias)

is a primary source of allocative and quality-of-service harms

where a system unfairly disadvantages certain groups.

3. Amazon Web Services. (2023). Amazon SageMaker Developer Guide. In the section "Fairness and Explainability with SageMaker Clarify

" the documentation discusses pre-training bias metrics. It explains how imbalances in the training data

where certain facets or groups are under- or over-represented

must be identified and addressed to prevent biased model outcomes. This describes the core problem of sampling bias.