The AI Effect: The Impact of AI Transparency on Privacy and Data Governance

• Pages: 18-24
• Date: 01 July 2025
• Published date: 01 July 2025
• Author: Shaundra Watson

18 · ANTITRUST

COVER STORIES

which a U.S. federal agency articulated in 1973,1 and the

Organisation for Economic Co-operation and Develop-

ment’s seminal 1980 Privacy Guidelines, which incorpo-

rated an “openness” principle,2 transparency has been an

integral component of influential policy frameworks that

undergird privacy laws around the world.

Modern privacy laws reiterate the importance of transpar-

ency. For example, the EU’s General Data Protection Reg-

ulation (GDPR) incorporates explicit transparency-related

requirements throughout the legislation, stating in Recital

58 that the principle of transparency requires information

provided to the public or the data subject to be “concise,

easily accessible and easy to understand.”3 For example, the

GDPR requires entities that direct how data is collected,

disclosed, or used to provide a variety of information to

individuals whose PI is collected, such as information about

the purposes for which their information will be processed;

the “legal basis” for such processing; any recipients or cate-

gories of recipients of their PI; if applicable, the fact that the

business intends to transfer their PI to a country outside the

European Economic Area; information about certain indi-

vidual rights concerning their data; and, if applicable, that

their data will be used in certain types of automated deci-

sion-making and meaningful information about the logic

involved, as well as the significance and envisioned conse-

quence of the processing.4

Privacy laws in the United States also prioritize transpar-

ency, despite the lack of a comprehensive federal privacy law.

Federal sectoral laws, including in the health and financial

sectors, require initial and recurring privacy notices, impose

disclosure requirements regarding the sharing of covered

information to certain recipients, and in some cases require

covered entities to provide notices to individuals about how

they can exercise their legal rights. For example, the Fair

Credit Reporting Act requires notification when entities

take certain types of adverse actions based on information in

a “consumer report.”5 State privacy laws also require trans-

parency. For instance, the California Consumer Privacy Act

(CCPA) requires businesses to inform consumers of the pur-

pose for which PI is collected, whether PI is sold or shared,

the categories of any recipients of PI, and how to exercise

The AI Effect: The Impact of AI Transparency

on Privacy and Data Governance

BY SHAUNDRA WATSON

Introduction

Protecting privacy is not a destination; it’s a journey with

a new adventure at every turn. Privacy lawyers are adept

at navigating ambiguous, complex, and varied regulatory

requirements in companies with global operations. Such

lawyers must not only grapple with how these issues inter-

sect with business functions but also how to harmonize dif-

ferent jurisdictions’ approaches across the business in order

to ease compliance obligations, decrease friction in prod-

uct delivery, minimize costs, and enable continued business

growth. Against this backdrop, privacy lawyers also invari-

ably confront one perennial issue: change. Several factors

have contributed to the continuing evolution of the privacy

regulatory landscape, but one key recent driver of these

changes is artificial intelligence (AI).

At the heart of AI is data, which can include personal

information (PI). As a result, AI technological advances,

increased adoption, and new regulatory requirements per-

meate privacy considerations and legal obligations. Indeed,

the impact of AI on privacy compliance—and data gov-

ernance more broadly—is wide-ranging, affecting thresh-

old issues such as the scope of data subject to obligations,

including a potentially expanded scope of sensitive PI in

jurisdictions where AI enables sensitive insights derived

from non-sensitive data, and whether companies can use PI

for secondary uses, such as training AI systems.

One recurring issue that has emerged in global AI legisla-

tive proposals and regulatory requirements is transparency.

Although policymakers have coalesced around prioritizing

AI transparency, jurisdictions have diverged in the scope

and application of transparency requirements.

Transparency in Privacy—A Critical Foundation

Transparency is a bedrock principle of information privacy

law. As a priority in the Fair Information Practice Principles,

Shaundra Watson is the President of PolicyStack AI, a global initiative

for amplifying unique voices in AI policy dialogues, and she works for a

global trade association for the enterprise software industry. The views

expressed in the article are her own and do not necessarily represent

the views of her employer.