GPT-4o: If the court finds Lendistry’s practices unlawful, it could create stricter standards for data privacy, especially regarding AI-powered systems that collect or analyze sensitive information.

Question 1 of 4 for ChatGPT-4o: Please read the complaint in the “Onisko & Schulz vs B.S.D. Capital & DBA Lendistry” case and tell me what it says in easy to understand language

ChatGPT-4o’s answer:
The complaint in the “Onisko & Schulz vs B.S.D. Capital & DBA Lendistry” case involves a class action against Lendistry, a company contracted by the California Governor’s Office to manage grants for businesses impacted by COVID-19. The plaintiffs, Onisko & Scholz, LLP, and other affected businesses claim that Lendistry mishandled the administration of these grants, especially regarding applicant privacy and data handling.

Here’s a simplified breakdown of the main points:

1. Privacy Violations: The complaint alleges that Lendistry, through a third-party technology partner (referred to as "TBVP"), misled applicants about the security and privacy of their bank information. Applicants were led to believe their banking credentials were used only once for account verification. However, the third-party partner continued accessing applicants' bank accounts without their knowledge to collect data.

2. Data Harvesting and Misuse: The complaint claims that this third-party partner repeatedly accessed applicants' bank data, collecting transaction histories and other sensitive details, allegedly for its own commercial purposes. This ongoing access was not disclosed to applicants, nor were they given an option to opt out.

3. AI and Tracking: Lendistry is accused of using AI and tracking tools to monitor applicants’ interactions on its app and website without proper consent. These tools allegedly collected extensive data on applicants' behaviors, such as browsing patterns, device information, and more, which were used for data mining and shared with third parties.

4. Misrepresentation of Grant Funds: The plaintiffs argue that Lendistry improperly evaluated payroll records and offered less grant funding than what applicants were entitled to under the statute. When applicants challenged these decisions, Lendistry allegedly pressured them to accept lower amounts or risk withdrawal of their applications.

5. Unauthorized Monitoring: The complaint also highlights that Lendistry’s customer service interactions (such as online chat) were monitored and recorded by a third-party AI tool, which analyzed applicants' conversations in real-time without disclosing this monitoring to users.

6. Class Action Claims: The plaintiffs argue that these practices breach Lendistry’s contract with the state and violate California privacy laws. They are seeking damages and other relief on behalf of all affected businesses, claiming that Lendistry's actions caused financial harm and exposed their data without consent.

The case highlights concerns over data privacy, misuse of personal information, and mismanagement of funds meant to aid small businesses.

Question 2 of 4 for ChatGPT-4o: List all issues mentioned in the complaint that in any way relate to the use of AI

ChatGPT-4o’s answer:
Here’s a list of issues related to the use of AI mentioned in the complaint:

1. Unauthorized Data Access and Analysis: The complaint alleges that Lendistry, through its third-party partner (referred to as "TBVP"), used AI and machine learning to continuously access and analyze applicants' financial data after the initial verification, without notifying applicants or obtaining their consent.

2. Behavioral Tracking and Analysis: Lendistry’s website and app allegedly included AI-powered tracking tools that monitored and analyzed applicants' behaviors, such as browsing patterns, interactions on the site, and device information. This tracking was done without transparency or consent and used for data mining purposes.

3. Real-Time Chat Monitoring and Analysis: The customer service chat feature on Lendistry’s platform was allegedly powered by a third-party AI (Qualified, Inc.) that intercepted, recorded, and analyzed chat conversations in real time. This AI used behavioral data to analyze user interactions and route users to representatives without informing them of the AI's involvement.

4. Collection and Use of Biometric Data: The AI used by Lendistry’s third-party partners reportedly gathered biometric data from applicants, including physiological, biological, or behavioral characteristics, without the applicants' knowledge. This included data derived from voice recordings, keystrokes, and other identifying behaviors.

5. AI-Driven Data Monetization: The complaint asserts that the third-party AI systems leveraged user data collected from Lendistry’s platform for their own purposes, including enriching datasets and monetizing insights derived from applicants' private financial information. This included sharing data with other fintech applications, partners, and clients.

6. Use of AI for Risk Assessment: Lendistry reportedly used AI to automate processes that traditionally required human oversight, such as evaluating financial records and determining grant eligibility, with the risk that AI-based decisions could improperly affect the outcome of applications.

7. AI in Real-Time Bidding (RTB): The data collected through Lendistry’s platform, including applicants' unique identifiers and browsing behavior, was allegedly used in a real-time bidding (RTB) system. This system leveraged AI to profile users and target them in advertising auctions without their consent, sharing sensitive demographic and behavioral data with thousands of auction participants.

8. AI-Driven Fingerprinting for Tracking: Lendistry’s app and website reportedly used AI-based fingerprinting techniques (such as WebGL and Canvas fingerprinting) to uniquely identify users across sessions and devices, enabling persistent tracking even after users left Lendistry’s platform.

9. AI Integration with Third-Party Analytics: Lendistry’s platform allegedly integrated with various third-party AI tools to analyze and track user engagement across sessions, providing insights into user journeys and behaviors, which were then used to improve marketing and analytics.

10. AI-Based Profiling and Segmentation: The complaint states that AI algorithms were used to segment and categorize applicants into various targetable groups, including highly sensitive categories such as race, religion, health, and sexual orientation, which were then shared with third-party advertisers and auction participants without applicants' consent.

These issues underscore how Lendistry’s use of AI and machine learning intersected with privacy concerns, data tracking, unauthorized monitoring, and the monetization of sensitive user data.


Question 3 of 4 for ChatGPT-4o: Explain all ways in which the outcome of this case can pose future challenges for companies that either use or develop AI

ChatGPT-4o’s answer:
The outcome of the “Onisko & Schulz vs. B.S.D. Capital & DBA Lendistry” case could set precedents that challenge AI-using and AI-developing companies in several key areas:

1. Data Privacy and Consent

   • Challenge: If the court finds Lendistry’s practices unlawful, it could create stricter standards for data privacy, especially regarding AI-powered systems that collect or analyze sensitive information. Companies may be required to obtain more explicit and detailed user consent, especially when using AI to monitor or analyze user behavior.

   • Implications: Companies may need to rethink how they collect, store, and use data, ensuring full transparency and user control over AI-driven data collection processes. This may involve re-engineering data pipelines, especially for companies that rely on behavioral data for targeted services or analytics.

2. AI Transparency and Explainability

   • Challenge: The case could emphasize the need for companies to clearly explain AI functionalities, particularly when AI is used for decision-making, continuous tracking, or biometric analysis.

   • Implications: Organizations might face new requirements to disclose when and how AI is involved in their services. This could involve providing more detailed information on AI operations, particularly in user agreements, or even public-facing documentation explaining how the AI functions and its impacts.

3. Limitations on Behavioral Tracking and Profiling

   • Challenge: A ruling against Lendistry’s AI-based tracking could impose limits on AI-powered profiling and behavioral analytics, especially for collecting data across sessions or devices without explicit consent.

   • Implications: Many companies rely on AI-driven behavioral analysis for targeted marketing, personalized experiences, or fraud detection. Limits in this area could mean companies would need to scale back these activities or find alternative methods that don’t rely on continuous, cross-device tracking.

4. Third-Party Data Sharing and Responsibility

   • Challenge: This case could set a precedent holding companies accountable for how their third-party partners use and protect shared data, particularly in the case of AI-driven tools that aggregate and monetize data.

   • Implications: Companies may need to take greater responsibility for data privacy even beyond their own operations. Contractual obligations might expand to require strict control and monitoring of third-party partners, limiting how these partners can use AI to process or analyze shared data. This could lead to increased legal scrutiny and audit requirements for companies working with external AI vendors.

5. Real-Time Bidding and Ad Tech

   • Challenge: If the court finds real-time bidding (RTB) and related profiling methods unlawful due to privacy violations, it could have a significant impact on the ad tech industry, which often uses AI to profile users and bid on ad space in milliseconds.

   • Implications: Companies involved in RTB might need to reevaluate their use of AI to process bidstream data and consider anonymizing or aggregating data more rigorously. A ruling could also encourage broader regulations on RTB practices, requiring ad tech companies to build in robust privacy safeguards.

6. Responsibility for AI-Induced Privacy Breaches

   • Challenge: If AI-driven privacy breaches (like biometric data collection or continuous unauthorized access) are deemed unlawful, companies may face increased liability for AI-driven breaches, particularly those that happen without user knowledge.

   • Implications: Companies might have to invest in stronger oversight mechanisms for AI systems, particularly those that handle sensitive data. This could lead to additional compliance costs, as companies may need to implement continuous monitoring and more stringent access controls, even within automated AI systems.

7. Broader Implications for AI Ethics and Trust

   • Challenge: The outcome could lead to public distrust in AI systems if the case demonstrates how AI-driven technologies can be used to exploit or misuse personal data. Future cases may rely on this precedent to argue against opaque or overly intrusive AI practices.

   • Implications: Companies might be required to adopt more rigorous AI ethics standards, balancing profit motives with user rights and privacy concerns. This could push companies toward AI solutions that are more transparent and user-friendly, and away from those perceived as invasive or overly complex for users to understand and control.

8. Stricter Regulations on Data Monetization

   • Challenge: If monetizing sensitive user data (such as through third-party AI partners) is found unlawful, it could curb the ability of companies to monetize user data collected via AI.

   • Implications: Companies may face limits on AI applications that turn user data into profitable insights, possibly requiring companies to find alternative revenue sources or shift their business models. Regulations might require firms to fully anonymize or aggregate data before using AI to monetize insights.

9. Increased Compliance and Legal Costs

   • Challenge: If companies are held to higher standards for managing and auditing third-party AI vendors, it could increase compliance burdens, especially for small to medium businesses that rely on third-party AI services.

   • Implications: Many companies may have to allocate additional resources toward legal compliance, risk management, and data protection in AI operations. This could impact smaller companies disproportionately, as they may lack the resources to navigate these requirements as easily as larger corporations.

This case could signal a shift toward stricter AI governance, and companies might need to adapt quickly to avoid potential liabilities. Future AI solutions may need to be designed with built-in privacy and compliance measures, even when used in third-party partnerships.

Question 4 of 4 for ChatGPT-4o: List all the activities relevant to AI training and dissemination of model output that AI makers should be wary of in the future, as a result of the potential outcome of this case

ChatGPT-4o’s answer:
If the outcome of this case leads to increased scrutiny on privacy, consent, and transparency in AI practices, AI makers should be wary of the following activities in relation to AI training and model output dissemination:

1. Using Personal Data for AI Training Without Clear Consent

   • Risk: Training AI models on personal data (such as financial transactions, behavioral data, or biometric data) without explicit, informed user consent could be deemed unlawful or unethical.

   • Precaution: AI makers should ensure they obtain clear, documented consent specifically for AI training purposes, possibly allowing users to opt out of having their data used for model training.

2. Collecting Sensitive Data Types Without Disclosure

   • Risk: Using sensitive data like biometric information, financial details, or health-related data for AI training without fully informing users can lead to privacy violations.

   • Precaution: AI developers should explicitly disclose any use of sensitive data types, particularly those involving AI-based profiling, and consider limiting model training to less sensitive data.

3. Using Third-Party Data for AI Training Without a Full Vetting Process

   • Risk: Relying on data obtained from third-party partners without verifying its consent status or origin could lead to legal risks if the data is found to be improperly collected.

   • Precaution: AI makers should ensure that third-party data providers follow stringent data collection practices and obtain proper consent for data used in AI training. This may involve contractual agreements that guarantee the data was legally obtained.

4. Creating Persistent User Profiles Without User Control

   • Risk: AI models that create detailed, persistent profiles (e.g., based on browsing patterns, financial behaviors, or location data) without enabling users to manage or delete this data could violate privacy rights.

   • Precaution: AI makers should provide users with access to and control over their profiles, including options to delete data or opt out of certain tracking practices that feed into AI models.

5. Embedding Model Outputs in Systems that Continuously Access User Data

   • Risk: Using model outputs that enable ongoing, passive data collection or “surveillance-like” tracking of user behaviors (e.g., financial activity monitoring) could be seen as invasive.

   • Precaution: AI makers should minimize or avoid continuous, unauthorized data access in AI systems, especially in consumer-facing applications, and consider periodic re-authorization to reaffirm user consent.

6. Automating Personal Decision-Making Without Adequate Transparency

   • Risk: Automated decision-making (e.g., assessing grant eligibility, loan approvals) powered by AI without transparency into how decisions are made could expose companies to legal scrutiny.

   • Precaution: AI makers should ensure that users are informed about the nature of AI-driven decisions, including clear explanations of the decision criteria and the data influencing the output.

7. Using AI in Real-Time Bidding (RTB) and Behavioral Advertising

   • Risk: Employing AI models to facilitate real-time bidding based on sensitive or personal data without clear user consent can raise privacy issues, as users may not expect such profiling.

   • Precaution: AI makers involved in ad tech should provide explicit consent mechanisms for RTB-related profiling and ensure that sensitive data is anonymized or aggregated before being used in bidding processes.

8. Selling or Sharing Model Outputs Containing User-Specific Insights

   • Risk: Disseminating AI model outputs that include identifiable insights about users (such as spending habits or health data) to third parties could result in privacy breaches.

   • Precaution: AI makers should anonymize or generalize outputs shared with third parties and verify that downstream uses of model outputs comply with data protection and privacy standards.

9. Allowing Continuous AI-Driven Access to User Accounts or Data Sources

   • Risk: Granting AI systems or third-party applications unrestricted, continuous access to user accounts or data sources (e.g., bank accounts, emails) can infringe on user privacy if not properly disclosed.

   • Precaution: AI makers should limit ongoing data access and ensure users have control over revoking or modifying access as needed. Clear disclosure and re-consent practices should also be in place.

10. Using AI to Infer or Predict Sensitive Characteristics Without Consent

• Risk: Inferring sensitive information (e.g., religion, sexual orientation, health conditions) through AI predictions based on user data could be seen as a privacy intrusion.

• Precaution: AI makers should either avoid or anonymize sensitive inferences in model outputs and ensure users are aware when sensitive characteristics could be inferred, allowing for opt-out options.

11. Embedding Data Collection Mechanisms in Model Outputs or User Interfaces

• Risk: AI systems that passively collect data from user interfaces (such as by tracking keystrokes, mouse movements, or screen interactions) can inadvertently collect excessive data, leading to privacy concerns.

• Precaution: AI makers should avoid embedding passive data collection features in user-facing model outputs or interfaces unless strictly necessary, and always disclose and obtain consent for such mechanisms.

12. Lack of Oversight on AI Models Used by Third-Party Partners

• Risk: When third parties utilize AI models for their own purposes, any misuse or improper handling of data associated with these models could expose the original AI developer to liability.

• Precaution: AI makers should implement robust oversight mechanisms to ensure third-party partners comply with privacy standards, potentially including audits, usage restrictions, and data-sharing controls.

13. Continuous Access and Use of AI for Customer Interaction Analysis

• Risk: AI-powered analysis of customer interactions (e.g., chat transcripts, call recordings) without transparency or user control could breach privacy expectations.

• Precaution: AI makers should ensure that users are fully informed if AI will analyze their interactions and allow users to opt out or control the extent of AI involvement in customer service settings.

14. Training AI Models on User Data without a Clear Data Retention Policy

• Risk: Retaining user data for extended periods for AI training purposes could contravene data minimization principles if not adequately justified.

• Precaution: AI makers should adopt clear data retention and deletion policies, ensuring that any training data not necessary for the model’s operation is securely deleted or anonymized within a specified timeframe.

15. Allowing AI-Driven Biometric Data Analysis Without Explicit Consent

• Risk: Using biometric data (such as voice patterns, keystrokes, or facial features) to train or inform AI models without explicit consent could be deemed overly intrusive.

• Precaution: AI makers should obtain explicit consent for any biometric data collection and explain its intended use, allowing users to opt out where feasible.

By carefully managing these activities, AI makers can better align their practices with privacy expectations, minimize the risk of legal challenges, and build greater trust with users in the era of increasingly stringent data protection standards.