An organization needs large data sets to perform application testing. Which of the following would BEST fulfill this need?

Open-source data repositories are centralized platforms (e.g., Kaggle, UCI Machine Learning Repository, Google Dataset Search) that host a wide variety of large, often cleaned and labeled, datasets. They are specifically designed to provide data for research, development, and testing of applications, particularly in the AI and machine learning domains. Using these repositories is the most direct, efficient, and common method for an organization to acquire the large-scale data needed for comprehensive application testing without the significant overhead of collecting and preparing the data from scratch.

A. AI model cards are documents that provide transparency about a model's performance, limitations, and ethical considerations, not a source of raw data.

B. Incorporating data from search content is impractical; it is often unstructured, may have copyright or privacy restrictions, and requires extensive effort to scrape, clean, and label.

D. AI data augmentation is a technique to artificially increase the size of an existing dataset. It cannot be used without an initial dataset to augment.

1. ISACA

Artificial Intelligence Audit and Assurance Framework

2023. In the "AI Data Life Cycle" section

the framework discusses "Data Sourcing and Acquisition

" emphasizing the need to obtain relevant and sufficient data for training and testing AI models. Open-source repositories are a primary means of fulfilling this requirement for large-scale data needs.

2. Ng

A. (2023). Machine Learning (CS229) Course Materials. Stanford University. Lecture notes and project guidelines frequently reference the use of public datasets from repositories like the UCI Machine Learning Repository and Kaggle as standard practice for obtaining data for model development and evaluation. (See

for example

project guidelines which list acceptable data sources).

3. Roh

Y.

Heo

G.

& Whang

S. E. (2021). A survey on data collection for machine learning: a big data - AI integration perspective. IEEE Transactions on Knowledge and Data Engineering

33(4)

1328-1347. This paper discusses various data collection methods

highlighting public datasets as a crucial resource. Section 3.1

"Using Existing Datasets

" explicitly states

"The easiest way to collect data is to use publicly available datasets... Many websites share public datasets

such as Kaggle

the UCI ML Repository

and the KDD Cup." This confirms that using such repositories is a primary and efficient method. (DOI: https://doi.org/10.1109/TKDE.2019.2946162)