GPT-4: It is plausible that some AI companies may currently not be in full compliance with their privacy and confidentiality commitments

Question 1 of 6 for ChatGPT-4: List all the problems that the FTC is flagging in the blog post "AI Companies: Uphold Your Privacy and Confidentiality Commitments"

ChatGPT-4’s answer:The blog post "AI Companies: Uphold Your Privacy and Confidentiality Commitments" from the Federal Trade Commission highlights several concerns and potential problems related to privacy and confidentiality in AI companies, particularly those offering model-as-a-service. The key issues flagged are:

1. Continuous Appetite for Data: AI companies have a business incentive to constantly ingest additional data, which can conflict with their obligations to protect users' data. This can undermine privacy or result in the appropriation of a firm’s competitively significant data.

2. Risks with Sensitive or Confidential Information: Customers may reveal sensitive or confidential information when using a company's models, such as internal documents and their own users’ data. There's a risk that a model-as-a-service company may infer a range of business data from the companies using its models.

3. Liability for Failing to Abide by Privacy Commitments: Companies that fail to uphold their privacy commitments may be liable under the laws enforced by the FTC. This includes promises not to use customer data for secret purposes, such as to train or update their models.

4. Requirement to Abide by Commitments to Customers: Companies must abide by their commitments, regardless of how or where the commitment was made, including through promotional materials, terms of service, or online marketplaces. Failure to do so can expose a firm to enforcement action by the FTC.

5. Risks of Misrepresentations and Omissions: Misrepresentations, material omissions, and misuse of data associated with AI models pose potential risks to competition and may also violate antitrust laws as well as consumer protection laws.

6. Enforcement Actions for Misuse of Data: The FTC has brought actions against companies that inappropriately use or disclose people's data, including for ad targeting, despite assurances of privacy.

7. Legal Obligations Regarding Data Collection and Use: Companies that deceive customers or users about how their data is collected—explicitly or implicitly, by inclusion or omission—may be violating the law.

These concerns emphasize the importance of AI companies maintaining a balance between their business needs and their legal and ethical obligations regarding data privacy and usage.

Question 2 of 6 for ChatGPT-4: How easy is it for AI makers to comply and how likely is it that they are currently not in compliance?

ChatGPT-4:’s answer:The Federal Trade Commission's blog post notes that for AI companies, particularly those offering model-as-a-service, there is a significant business incentive to continuously collect and ingest additional data. This drive to develop new or customer-specific models or refine existing ones can conflict with the company's obligations to protect user data. Such a conflict can undermine user privacy or result in the appropriation of a firm’s competitively significant data.

Given this business model, it may not be easy for AI companies to comply with privacy and confidentiality commitments, especially when balancing the need for data to improve and personalize their services against the requirement to protect user data. The risk is heightened when customers use these AI models with sensitive or confidential information, such as internal documents and their own users' data. There's also the risk that a model-as-a-service company might infer a range of business data from the companies using its models, like their scale and growth trajectories, through their APIs.

Given these complexities and the inherent tension between data collection for model improvement and data protection obligations, it is plausible that some AI companies may currently not be in full compliance with their privacy and confidentiality commitments. The challenge lies in effectively managing the appetite for data while strictly adhering to privacy standards and legal requirements​​.

Question 3 of 6 for ChatGPT-4: How could the AI makers have prevented this situation from happening in the first place? How easy will it be to fix them retroactively?ChatGPT-4’s answer:To prevent the situation where AI companies are not in compliance with privacy and confidentiality commitments, these companies could have taken several proactive measures:

1. Clear Privacy Policies and Data Usage Terms: Establishing clear and transparent privacy policies and terms of use regarding how customer data will be handled, stored, and used. This includes making explicit commitments not to use customer data for undisclosed purposes, like training or updating models.

2. Robust Data Governance Frameworks: Implementing robust data governance frameworks to ensure that data collection, storage, and usage align with legal and ethical standards, as well as with the company's own privacy commitments.

3. Regular Compliance Audits: Conducting regular audits to ensure ongoing compliance with privacy laws and the company's stated policies. This can help identify and rectify potential issues before they become significant.

4. Stakeholder Engagement and Transparency: Engaging with stakeholders, including customers, to understand their privacy concerns and expectations. Maintaining transparency about data practices can build trust and ensure alignment with user expectations and legal requirements.

Regarding the ease of fixing these issues retroactively, it can be quite challenging. The Federal Trade Commission (FTC) has enforced actions where businesses that unlawfully obtained consumer data were required to delete any products, including models and algorithms, developed in whole or in part using that data. This indicates that retroactively fixing such issues could involve significant operational changes and potentially the loss of valuable intellectual property, which can be costly and time-consuming. Additionally, restoring trust with users and rectifying reputational damage can be a long and difficult process​​.

Question 4 of 6 for ChatGPT-4: List all possible ways in which AI makers could end up being in breach of the laws the FTC has mentioned. Describe technical and operational methods of data scraping/capturing/retrieval and all the potential kinds of data that would be problematic in this context

ChatGPT-4’s answer:AI makers could end up breaching the laws enforced by the Federal Trade Commission (FTC) in several ways:

1. Misrepresentation and Omissions: Making explicit misrepresentations or material omissions about how their AI models are trained and deployed. This includes not disclosing the full extent of data usage or collection methods to customers. Such practices can undermine fair competition and lead to enforcement action for unfair methods of competition​​.

2. Violating Privacy Commitments: Failing to abide by privacy commitments made to users and customers. This includes using customer data for purposes like training or updating their models without explicit consent or through undisclosed workarounds. The FTC may require companies that unlawfully obtain consumer data to delete any products, including models and algorithms, developed using that data​​.

3. Continuous Data Ingestion: AI companies, especially model-as-a-service companies, have an inherent business drive to constantly collect more data. This can conflict with obligations to protect user data, potentially leading to the appropriation of competitively significant data or undermining user privacy. The risk is increased when customers provide sensitive or confidential information​​.

4. Failure to Disclose Material Facts: Not disclosing material facts that could influence customers' decisions, such as specific data collection and usage practices. This includes cases where companies make claims about their technology (e.g., facial recognition) but omit key details that qualify these claims​​.

5. Breach of Promotional or Service Commitments: Not adhering to commitments made through promotional materials, terms of service, or online marketplaces. Violations can occur if companies disclose user data for purposes like ad targeting, contrary to their privacy policies or assurances made during the registration process. This also includes changing terms of service or privacy policies without clear notice and affirmative consent from users​​.

Technical and operational methods of data scraping, capturing, or retrieval that could be problematic include:

• Web Scraping: Extracting large amounts of data from websites, which might include personal or sensitive information.

• API Data Extraction: Using APIs to infer business data from companies using their models, which could reveal sensitive details like scale and growth trajectories.

• Data Mining from User Interactions: Collecting data from user interactions with AI models, potentially including personal, financial, or health information.

Problematic types of data in this context are:

• Personal Identifiable Information (PII): Such as names, addresses, social security numbers, and financial information.

• Sensitive Business Information: Including trade secrets, internal documents, and strategic plans.

• User Behavior Data: Data that can reveal personal habits, preferences, or health-related information.

• Confidential Communication: Private communications that might be intercepted or collected without consent.

Addressing these issues requires AI makers to balance their data needs with legal and ethical considerations, ensuring transparency and adherence to privacy commitments.

Question 5 of 6 for ChatGPT-4: How could potential victims of data privacy issues or other litigants against AI makers best collect evidence in this context, and what kind of evidence would they need to collect and what kind of grievances should they be bringing forward?

ChatGPT-4’s answer:How could potential victims of data privacy issues or other litigants against AI makers best collect evidence in this context, and what kind of evidence would they need to collect and what kind of grievances should they be bringing forward?

Potential victims of data privacy issues or other litigants against AI makers can best collect evidence by focusing on the following areas, and they should bring forward grievances related to these aspects:

1. Evidence of Non-Compliance with Privacy Commitments: Collecting evidence that AI companies failed to abide by their privacy commitments to users and customers, such as using customer data for undisclosed purposes like training or updating their models. This could include emails, terms of service agreements, or privacy policies where these commitments were stated​​.

2. Misrepresentations and Omissions: Gathering evidence of explicit misrepresentations or material omissions regarding data practices. This could include promotional materials, public statements, or documentation where companies misrepresented how their data is collected and used. Evidence of unfair competition practices, such as appropriating competitively significant information, would also be relevant​​.

3. Violations of Explicit Commitments: Documentation of cases where the company failed to abide by commitments made through promotional materials, terms of service, or on their website. This could involve evidence showing that the company disclosed user data for purposes like ad targeting, contrary to their privacy assurances​​.

4. Failure to Disclose Material Facts: Collecting evidence of what a company failed to disclose that might have affected customers' decisions. For example, if a company claimed not to use certain technologies on consumers’ data unless explicitly activated but omitted that this was only true in some jurisdictions, such evidence would be crucial​​.

Types of evidence that could be collected include:

• Digital Communications: Emails, chat logs, or other digital communications where misrepresentations or failures to disclose were made.

• Policy Documents: Privacy policies, terms of service, and other official documents where commitments were stated or changed.

• Marketing Materials: Advertisements, promotional emails, and website content that could have misled customers.

• Technical Data: Logs, data files, or other technical evidence showing unauthorized data usage or collection.

It is important for litigants to precisely document how they were misled or how their data was used in violation of commitments or regulations. Legal advice should also be sought to ensure the right kind of evidence is collected and presented effectively.

Question 6 of 6 for ChatGPT-4: List the potential sanctions for AI makers

ChatGPT-4’s answer:The potential sanctions for AI makers, particularly model-as-a-service companies, for failing to comply with their privacy and confidentiality commitments are enforced by the Federal Trade Commission (FTC) and can include:

1. Liability Under FTC Laws: AI companies may be liable under the laws enforced by the FTC for failing to abide by their privacy commitments to users and customers. This includes promises not to use customer data for secret purposes, such as training or updating models, whether done directly or through workarounds​​.

2. Requirement to Delete Unlawfully Obtained Data Products: The FTC has required businesses that unlawfully obtain consumer data to delete any products, including models and algorithms, developed in whole or in part using that unlawfully obtained data. The FTC continues to ensure that firms do not benefit from violating the law​​.

3. Enforcement Actions for Non-Compliance with Commitments: Companies that fail to abide by their commitments to customers, made through various mediums like promotional materials, terms of service, or online marketplaces, can be subjected to enforcement action by the FTC. This can include actions against companies that disclosed user data for ad targeting despite assurances in their privacy policies or during the registration process that they would keep the data private​​.

4. Sanctions for Misuse of Consumer Data: If companies retain or use consumer data for purposes other than those agreed upon, without providing clear and conspicuous notice and obtaining affirmative express consent—especially if changes are made surreptitiously to terms of service or privacy policies—they may face legal repercussions​​.

These sanctions emphasize the importance of AI companies adhering strictly to their privacy and confidentiality commitments and being transparent with their data practices to avoid legal and reputational risks.