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In today's digital marketplace, where data privacy is a critical concern, transparency about how user data is used — especially in AI training — has never been more important.
Many companies struggle to clearly communicate whether and how user data is being used to improve their AI systems or even shared with external organizations. This lack of clarity not only undermines trust but also exposes businesses to legal and reputational risks.
This article explores the key issues surrounding data transparency in AI training and provides actionable steps companies can take to build trust with their data practices.
The Privacy Imperative in an AI-Driven World
Transparency around user data in AI training is no longer optional in a world where privacy concerns dominate public discourse. As AI adoption grows, so does the need for businesses to be transparent about not only what data they collect but also how it trains their systems.
Aman Priyanshu, an AI researcher specializing in privacy-enhancing technologies, emphasized the importance of integrating transparency into AI practices. “This requires specific disclosures about AI uses, clear consent mechanisms and ongoing tracking of how data moves through AI systems. Failure to address these concerns can lead to significant reputational damage and legal repercussions, as users demand more control over their information and lawmakers respond with stricter data governance rules."
The importance of transparency in AI is underscored by global privacy regulations like the General Data Protection Regulation (GDPR), which demand clarity in data handling and AI-driven decisions.
David McInerney, commercial manager of data privacy at Cassie, explained that “automated decisions by AI have potentially huge consequences for consumers. It’s important that businesses can explain how these decisions were made, where the information came from and provide avenues for individuals to request a review where potentially incorrect data has been used.” He added that this is already provisioned under Europe’s GDPR, and US businesses can expect similar customer expectations in the future.
The stakes extend beyond meeting regulatory obligations; they shape how customers perceive a brand's values and long-term reliability. Companies that can clearly articulate how they use, protect and anonymize user data — while providing tangible safeguards against misuse — will set themselves apart in an environment where trust is paramount.
Related Article: What Europe Can Teach North America About AI
What’s Getting in the Way of Clear AI Data Practices
Achieving transparency around user data in AI training involves navigating a complex web of challenges that can strain even the most well-intentioned businesses.
One pressing issue is the lack of clarity around how user data is collected, processed and incorporated into AI systems. Yet the core challenge businesses face in aligning AI training with privacy regulations is specifying the purpose of data usage, as Priyanshu explained, stating, “Most companies have existing data collection practices for their operations, but AI training represents a new use case that needs explicit disclosure. Under GDPR and CCPA [California Consumer Privacy Act], businesses must clearly specify how they plan to use customer data. This means updating privacy policies, sending clear communications to users about the new AI uses of their data, and implementing systems to track both user consent and data usage throughout its lifecycle.”
Many companies struggle to articulate whether data is used for improving algorithms, shared with third parties or retained for future training purposes. This ambiguity not only leaves users in the dark but also complicates internal efforts to enforce consistent data-handling policies, increasing the risk of non-compliance or misuse.
Global regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have raised the stakes for transparency. These laws require businesses to provide clear explanations of data collection practices, obtain user consent, and ensure data is used in compliance with strict legal frameworks. The complexity of aligning AI training processes with these regulations often creates friction, as the scope of data usage in AI models can conflict with user expectations and legal definitions of data protection.
Other common pitfalls in AI data transparency include insufficient anonymization and a failure to secure explicit user consent, as Alex Li, founder of StudyX.AI, explained. “Enterprises must adopt compliant technical means to ensure that all data used for AI training has undergone effective anonymization processing and regularly review the effectiveness of these techniques.” He added that failing to secure explicit user consent is not just a regulatory failure but also a missed opportunity to build trust with users.
Opacity in data practices can severely erode consumer trust, particularly as users become more informed about privacy issues. When companies fail to explain how data is used, it fuels skepticism and concerns about exploitation, ultimately damaging brand reputation. The growing public awareness around data privacy amplifies this risk, making transparency not just a regulatory requirement but a cornerstone of maintaining long-term user loyalty and trust.
Another big hurdle to AI transparency is the lack of clarity around how models reach their decisions. Without explainability, businesses and end users alike struggle to trust AI's outputs, particularly when those outputs influence critical decisions. Bhaskar Roy, chief of AI products & solutions at Workato, told VKTR that it’s unclear how AI models arrive at their recommendations and decisions.
"We focus on enhancing the explainability of our models by establishing tools for traceability, enabling the tracking of AI model inputs, outputs and decision paths for accountability and fallacy analysis. By improving the transparency of our AI systems, we also build deeper trust in them,” explained Roy. “The last layer to solidify trust in AI decision-making is that we keep a human reviewer in the loop. AI is not perfect, so a human must be included in every step of the process to identify potential biases or errors and provide feedback. This iterative process ensures our AI systems are accountable, accurate and fair with their outputs."
Demystifying Data Use in Large Language Models
One transparency issue in AI today concerns how large language models (LLMs) like ChatGPT, Gemini and others are trained on user data. This practice raises questions about user consent, data protection and ethical AI development.
Generative AI systems, powered by LLMs, often require vast datasets to improve their capabilities. Some companies use user inputs — text, images or other content — to refine and enhance these systems. However, Priyanshu suggested, “Users want to understand the specific ways their data trains AI models. For instance, they need clarity on whether their text data might be used in language models that could potentially repeat sensitive information.” This has created widespread concerns, particularly about the risk of sensitive user data being stored or inadvertently echoed by these systems.
To address these concerns, some companies have opted to clearly state whether or not user data is used for training. For instance, OpenAI allows enterprise users to opt out of having their data included in training sets, while Microsoft ensures that customer data used in Azure services is not fed back into OpenAI’s training pipeline. These measures demonstrate that transparency around LLM training is achievable, but it requires a proactive approach.
In addition to disclosures, companies should adopt privacy-preserving technologies such as differential privacy or fine-tuning with synthetic data to mitigate risks. Tracking data lineage, as Priyanshu explained, is particularly important here. This ensures businesses can demonstrate how specific datasets are used and respond effectively to regulatory or user inquiries.
While much of the conversation around generative AI focuses on technical capabilities, an equally critical aspect is preparing the workforce to use these tools responsibly. “The most agile teams will be the ones that are constantly training and retraining their employees on the newest features, how it will impact their industry and what the company guardrails are to implement it,“ said Roy.
Actionable Ways to Improve AI Data Transparency
Achieving true transparency requires businesses to take concrete, user-focused actions that address how data is collected, used and shared. These steps not only enhance trust but also ensure compliance with evolving regulatory standards and align with customer expectations for ethical data practices.
Update Terms of Service (ToS)
Many businesses fall short of transparency due to overly technical or vague language in their terms of service. To address this:
- Clearly explain how user data may be used for AI training in simple, non-legalistic language that users can easily understand.
- Highlight sections of the ToS that pertain to data usage during key moments in the user journey, such as account sign-up or when updates are made. This approach ensures users are aware of data practices rather than burying them in dense documentation.
“Companies should structure their data usage disclosures around specificity rather than broad statements," Priyanshu advised. "While companies don’t need to reveal their AI architecture details, they should implement privacy-preserving techniques like data anonymization or differential privacy, which provides mathematical guarantees about privacy protection while allowing meaningful analysis.”
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Default Opt-In/Opt-Out Settings
Empowering users to control their data is central to transparency. Organizations can:
- Provide meaningful opt-in and opt-out choices for data sharing, ensuring users have control without being coerced into accepting default settings.
- Design these options to be easily accessible, using clear labels and explanations that outline the implications of each choice.
- Best practices include setting privacy-preserving defaults, such as opting users out of non-essential data sharing unless explicitly agreed upon.
The 'Creepy Test'
Building transparency also involves a cultural shift in how businesses think about data practices.
Edward Starkie, director of governance, risk and compliance at Thomas Murray, shared how informal yet practical approaches can guide ethical data usage. “One of the best cultural considerations I’ve seen is the ‘creepy test’ — a simple, human question: ‘Does this feel right?' Informal yet effective, this test forces organizations to consider the ethical implications of their data use.”
Transparency in APIs and Integrations
Businesses often rely on third-party APIs and integrations that interact with user data, making this a critical area for transparency. Companies should:
- Disclose how external tools and APIs access and use user data, both to users and enterprise customers.
- Maintain detailed documentation that outlines data flows, retention policies and safeguards for API use. This ensures that enterprise customers, especially those in regulated industries, can assess the privacy implications of integrating with your systems.
- Include data-sharing disclosures directly within API onboarding guides to provide clarity from the outset.
By implementing these practical steps, businesses can cultivate a culture of openness that strengthens user trust, aligns with regulatory demands and positions their brand as a leader in ethical AI and data practices.
Related Article: AI Trust Issues: What You Need to Know
Best Practices for Explaining Data Use
Clear communication about how user data is employed in AI training is fundamental to building trust. Avoiding jargon and using plain, concise language ensures that users can easily understand your practices. For instance, instead of saying, "Your data may be used for AI model optimization," a user-friendly alternative would be, "We use some of your data to improve how our AI works."
Incorporating tools such as privacy dashboards can further enhance transparency, giving users an accessible way to review how their data is collected and used while also managing their sharing preferences.
Priyanshu highlighted the value of tracking systems to ensure accountability and user trust in data handling, suggesting that “Tracking both consent and data usage through a lineage system enables organizations to respond effectively to data subject access requests (DSARs).” This enables companies to show users exactly how their data has been used while maintaining a competitive position.
Another important aspect is explaining the aggregation and anonymization of data, when applicable. Users are more likely to trust your processes if they understand that their data is combined with others and stripped of identifying information before being used. For example, you might clarify that, “We combine your data with thousands of others to identify trends, but we don’t store anything that can be traced back to you.” Providing specific examples of safeguards, such as protections against re-identification, can reassure users that their privacy is taken seriously.
Clarity about the specific purposes of AI training data use is essential to maintain user trust and regulatory compliance, as Priyanshu explained. “Companies should define clear boundaries around AI training purposes — if data is collected for improving customer service responses, it shouldn’t later be used for marketing prediction or recommendation systems without additional consent. These boundaries need to remain fixed even as AI capabilities expand.”
Maintaining transparency also requires regular communication about changes to data practices. Users should be promptly informed when policies or processes evolve, especially when it affects how their data is used for AI training. These updates should be direct and easy to understand, avoiding ambiguous language like “updated terms and conditions.” Instead, highlight significant changes in clear, concise summaries and direct users to detailed explanations if they wish to learn more. By being proactive and transparent, businesses can build confidence and demonstrate their commitment to respecting user privacy.
Clear communication not only builds trust but also combats the perception of unethical data practices, McInerney pointed out, stating that there is a widespread consumer perception of unethical data practices within the corporate realm. “To fight this stigma and regain public trust, businesses need to provide clear and concise information about how they process customer data and invest in data management tools.”
Effective communication about AI training can also benefit from tying transparency to existing legal frameworks. Edward Tian, CEO of GPTZero, suggested that companies mention specific regulations like GDPR or CCPA in their disclosures. “This can help users realize how seriously the company is taking their privacy. It also builds trust by showing alignment with well-known standards.”
Privacy Laws and the Price of Noncompliance
Adhering to privacy laws is a critical component of transparent data practices, particularly when it comes to AI training. Privacy regulations impose strict requirements for how businesses collect, process and disclose user data. These laws emphasize the importance of user consent, clarity in terms of service and the right for individuals to access and control their data.
Additionally, emerging AI-specific legislation in various jurisdictions further reinforces the need for transparency around how user data feeds into AI systems. Businesses must ensure they are not only meeting the technical requirements of these laws but also embedding them into everyday practices. This includes maintaining clear records of data usage and making it easy for users to understand and manage their privacy preferences.
Privacy regulations bring unique challenges, especially in balancing user rights with AI training processes. Starkie explained how one core principle — the "right to be forgotten" — can create operational hurdles, reiterating that, “If personal data is used for training a model and then a request for deletion is received, this becomes a significant challenge for organizations to manage.” This highlights the need for robust systems that can effectively respond to such requests while maintaining compliance.
Transparency also plays a pivotal role in mitigating risks associated with non-compliance. Failing to align with privacy regulations can result in significant fines, lawsuits or damage to a brand’s reputation. For example, companies that fail to disclose how data is used in AI training risk alienating customers or facing backlash if their practices are perceived as exploitative or opaque.
In early 2024, reports emerged that Reddit had entered into a $60 million annual content licensing deal, allowing an unnamed AI company to use Reddit's vast repository of user-generated content to train LLMs. This move, aimed at monetizing Reddit's data ahead of a planned IPO, sparked significant backlash from the Reddit community.
Many Reddit users felt betrayed by the lack of transparency and consent regarding the use of their posts and comments for AI training. Concerns were raised about privacy implications and the ethical considerations of profiting from user-generated content without explicit permission. In response, some users proposed strategies to undermine the AI training process, such as deliberately posting nonsensical or misleading information to degrade the quality of the data being used.
Companies that fail to clearly communicate how user data is used, especially for purposes like AI training, risk eroding user trust and facing community backlash.
Transparency as a Competitive Advantage
Transparency about data practices is no longer just a regulatory requirement; it is a powerful tool for building competitive advantage. Businesses that prioritize openness can build stronger user loyalty by demonstrating respect for customer privacy and control.
For example, Apple’s privacy initiatives, including its App Tracking Transparency framework, have not only bolstered customer confidence but also positioned the company as a leader in ethical technology practices. These efforts show that when businesses align their operations with user values, they can cultivate a loyal customer base that views transparency as a marker of trustworthiness.
Transparency can also act as a competitive differentiator, particularly when businesses empower users and actively communicate their practices. Li emphasized the value of regular transparency reports and user tools, stating that “enterprises should regularly release transparency reports to explain to users in detail the purpose and scope of data usage. Simple and transparent tools can help users manage their data and authorization, further building trust.”
Transparency also serves as a foundation for ethical AI development. In an age where public scrutiny of AI is growing, businesses that openly share how their AI systems are trained and deployed can reinforce their commitment to responsible innovation. By ensuring that data usage aligns with ethical standards and user expectations, companies not only mitigate risks but also set a precedent for sustainable and trustworthy AI practices. Transparency, in this context, becomes a catalyst for innovation by creating an environment where ethical considerations are integrated into technological progress.
For enterprise clients, transparency in data practices is a significant differentiator. Privacy-conscious partners, especially in regulated industries such as healthcare or finance, are more likely to collaborate with organizations that demonstrate accountability and clarity. Businesses that provide detailed documentation of their data usage, compliance with privacy laws and measures to ensure data security can position themselves as reliable and forward-thinking partners. This not only strengthens existing relationships but also attracts new clients who value transparency as a critical component of their own operational standards.
Related Article: Making Self-Service Generative AI Data Safer
Making Transparency Actionable
Transparency in data practices has shifted from being a compliance issue to a cornerstone of modern business strategy. By openly communicating how user data informs AI training and offering clear opt-in/opt-out mechanisms, businesses can transform privacy concerns into opportunities to deepen trust.
As generative AI becomes mainstream, LLM developers must set a higher standard for transparency, clearly defining how user data is — or isn’t — used in model training. These policies establish a foundation for ethical AI development. Companies that embrace these principles will position themselves as leaders in responsible innovation, standing out in a rapidly evolving AI environment.
