Which of the following is the BEST way to support the development and design of high-risk AI systems?

The development and design of high-risk AI systems are fundamentally dependent on the data used for training, validation, and testing. Ensuring the availability of trustworthy data sets—which are relevant, representative, accurate, and free from inappropriate bias—is the most critical prerequisite. This foundational element directly impacts the model's performance, reliability, fairness, and safety. Without high-quality, trustworthy data, it is impossible to develop a high-risk AI system that is robust and aligned with its intended purpose, making this the most crucial support mechanism during the design phase.

A: Data backup is a critical operational control for business continuity and disaster recovery, not a primary input for the initial development and design of the AI model.

B: User training is an important organizational and governance control for data handling but does not directly support the technical process of designing and building the AI system.

D: Multi-factor authentication is an essential access control measure for securing the operational system, not a foundational component supporting the AI model's core design and development.

1. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0).

Section 3.3.1

MAP 1: "Data is a critical component of many AI systems. Understanding the data’s characteristics and limitations is essential for mapping risks... This includes data quality

relevance

provenance

and potential for unwanted bias." This highlights that data understanding is a core part of the initial AI lifecycle stages.

2. European Commission. (2021). Proposal for a Regulation on a European approach for Artificial Intelligence (EU AI Act).

Article 10

"Data and data governance": This article specifies that for high-risk AI systems

"training

validation and testing data sets shall be subject to appropriate data governance and management practices." It mandates that data sets be "relevant

representative

free of errors and complete" to the greatest extent possible

establishing this as a core design requirement.

3. Floridi

L.

& Cowls

J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review

1(1). https://doi.org/10.1162/99608f92.8cd550d1

The paper discusses the principle of "Beneficence

" which includes ensuring AI systems do not cause harm. It implicitly and explicitly links this to the quality and integrity of the data used to build them

stating

"The quality of the data used is paramount."