Note: This piece was written by Bettina Lippisch, our VP of Privacy and Governance. Learn more about our privacy program here.
Many media companies now rely on artificial intelligence (AI) for enhancing creativity, streamlining workflows, and generating engaging content at scale. AI can do wonderful things for content creators when used the right way, but it can also create unnecessary risk and potential legal issues if used incorrectly.
The rise of AI-driven content creation capabilities comes with its own set of privacy concerns. To protect your business and team members, it’s critical to understand the associated risks and implement effective data governance measures. Let’s dive into some of the privacy challenges tied to generative AI content creation and explore ways for mitigating them:
Data Privacy & Security
Generative AI models used in content creation are trained on large datasets, which are often sourced from publicly available information or licensed data. This helps deliver high-quality outputs, but it raises concerns about data privacy. Training datasets may contain sensitive, proprietary or personal information that, if not properly handled, could lead to privacy or security issues.
For example, large language models (LLMs) like GPT-4 generate text based on patterns learned from many external datasets. If these datasets are not adequately anonymized, there’s a risk of AI unintentionally reproducing personal information or confidential data embedded in the training data. A lack of transparency into training data further complicates the matter, leaving end-users guessing about any ethical and privacy implications of AI-generated content.
TIP: When generating your content using a public AI model, confirm any content returned independently and find additional sources to validate the output.
Additionally, avoid feeding any copyrighted, proprietary or secure data as inputs into a public model. With little transparency into how these models use training data, it’s hard to effectively protect your data.
Assumed (Inferred) Data
AI models have the ability to deduce sensitive information in their outputs that are not explicitly provided. For example, an AI content generator might analyze user behavior, preferences, and content consumption patterns to create personalized content for a user. While this enhances the user experience, it also means that the AI is constantly learning and potentially making assumptions about users that could compromise their privacy and communication preferences.
This inferred data could become highly sensitive, ranging from assuming a subscriber’s political views and personal beliefs or even health information, all based on seemingly innocuous content interactions (I think most people remember the issue involving Target). If such assumed data is exposed or misused, it can lead to substantial privacy harms, legal repercussions, and undermine the trust in your brand.
TIP: Ensure that any user behaviors or data used to support your AI content generation is data that a user has opted in for, and give them controls to opt-out or be removed from content recommendations. Your Privacy Policy should at minimum contain references to the use of AI, the type of processing you do and why, as well as a clear and easy way to opt out of automated processing.
Data Ownership & Intellectual Property (IP)
AI-generated content raises questions about intellectual property (IP) and data ownership, even when the AI is trained on publicly available articles, books, or other data. Who owns the resulting content? And what happens if the AI was trained on copyrighted or proprietary information without proper authorization? The intersection of privacy and IP rights in AI-generated content is still very much a gray area.
Businesses and content creators should carefully research and evaluate the AI models they are using for content creation, confirming that the training data complies with privacy laws, such as the GDPR or CCPA, and does not infringe on the IP rights of original creators, but also what the use means for content ownership.
TIP: Inform yourself about the types of data used to train the AI you are using and involve your legal counsel to understand what IP laws and privacy regulations might cover your generated content, as well as your business in general.
Also consider how to protect your own data and content from being used if public models pick it up. Inform your employees on responsible AI use and create policies and a training curriculum that capture the dos and don’ts of responsible AI use in easy to follow instructions.
Consent and Ethical Considerations
Obtaining informed consent is a core principle of privacy. And in the context of AI-driven content creation, obtaining meaningful consent can quickly become complex. If an AI is trained on user-generated content scraped from your website (e.g. user comments or posts), were those users made aware their content might be used in this way? Frequently, the answer is no.
The lack of explicit consent from individuals about how their content or data is used in AI training sets poses serious ethical questions, and content creators and businesses must build transparent AI practices and guidelines. Clearly communicating to your subscribers how data is used, stored, and protected is a must in today’s privacy-conscious subscriber world.
TIP: Establish solid privacy disclosures when it comes to AI use to build trust and safeguard your subscribers’ personal information. Your preference center should contain simple controls to allow a user to understand and change their privacy preferences to their liking.
Data Governance and AI Regulation
Regulatory bodies worldwide are beginning to recognize the need to address the privacy challenges posed by AI-generated content and are moving towards tighter regulations, including the European Union’s recently passed AI Act.
Companies leveraging AI for content creation should proactively implement strong data governance frameworks to pre-empt and align with emerging regulations. Conducting regular AI risk audits, ensuring data minimization, and embedding privacy by design into AI development and deployment processes will help mitigate privacy risks and position companies as leaders in ethical and compliant AI use.
Additional Tips to Mitigate AI Privacy Risks in Content Creation
Here are some additional tips to effectively tackle AI privacy concerns in content creation:
- Data Anonymization and Minimization: Less data means less risk. Effectively anonymize datasets used prior to training AI models to prevent the leakage of personally identifiable information (PII), and minimize personal data collected and processed by AI tools.
- Transparency and User Control: Clearly communicate how someone’s information is being used in your AI systems and provide users with options to control their data, including obtaining explicit consent and allowing users to opt out of data collection for AI training.
- Ethical AI Guidelines: Develop and adhere to a set of ethical guidelines that govern the use of AI in content creation as part of your AI governance program. These guidelines should address data privacy, consent, IP rights, and the potential for bias in AI-generated content and content recommendations.
- Robust Data Governance Frameworks: Implement a comprehensive data governance framework that ensures compliance with privacy regulations such as GDPR and CCPA. Regularly review and update your framework to align with evolving AI regulations and technologies.
- Consider Privacy-Preserving AI Technologies: Explore and invest in privacy-preserving AI technologies such as federated learning and differential privacy, which help protect user data while still enabling AI-driven innovation.
AI-powered content creation offers tremendous opportunities for publishers and media companies, but innovation and efficiency must come with awareness of privacy concerns. Understanding the data privacy challenges and implementing robust data & AI governance practices can harness the power of AI while respecting user privacy and building a foundation of trust. As the regulatory landscape continues to evolve, proactive measures will be essential to navigating the complexities of AI privacy for content creators and media operators.
