Balancing Privacy, Profit, and User Well-Being: The Role of AI Agents in Real-Time Bidding

Date: August 17, 2024
Author: Brandon Williams and ChatGPT

You said:

I want to explore how AI will impact Advertising. I want to use this conversation as a blog post, don't be too verbose, keep your responses more conversational.

ChatGPT said:

Sure thing, let's dive in! How do you see AI shaping the future of advertising, or do you have specific areas you're curious about?

You said: Let's start with the real time bidding process. OpenRTB is the standard currently. There are many layers to how an ad auction is completed. To keep things clear let's focus on the 3 main players, the Demand Side Provider (DSP), Supply Side Provider (SSP) and the end user. Let's examine how an AI bot will impact the everyday user first.

ChatGPT said:

Great place to start! AI’s impact on the everyday user in the context of real-time bidding (RTB) can be pretty significant, even though most users might not realize it.

Personalized Ad Experiences

AI bots can analyze vast amounts of data in real-time, including browsing history, behavior, and demographics. This allows the DSPs to serve highly targeted ads that are more relevant to the user. For example, if a user frequently visits travel websites, AI can identify this pattern and prioritize travel-related ads in real-time.

Dynamic Ad Content

AI can also create dynamic ad content tailored to the user. Instead of seeing a generic ad, the user might see one that’s customized with the product, price, or promotion most likely to catch their attention. This kind of personalization can make ads feel less intrusive and more like useful suggestions.

Improved Ad Frequency and Placement

AI can help manage the frequency and placement of ads. By learning user behavior, AI can determine the best times and platforms to show ads, reducing the chances of overexposing the user to the same ad (which can be annoying) and instead showing it when it’s most likely to be effective.

Privacy Considerations

On the flip side, AI's ability to gather and analyze data in such a detailed way raises privacy concerns. Users might feel uncomfortable with how much is known about them and how it’s being used to target them, especially if they start noticing just how tailored the ads are.

In summary, AI bots in RTB can enhance the ad experience for users by making it more relevant and less disruptive, but this also comes with a need for transparency and privacy safeguards.

How does that sound for a start? Would you like to explore AI's impact on DSPs and SSPs next?

You said: I am imagining something like a smart ad block agent. The agent has access to all my information and make decisions on what ads to show me. How would we preserve the privacy of the user? A profile would need to be maintained somewhere.

ChatGPT said:

A smart ad block agent that curates ads based on user preferences while preserving privacy is an interesting concept. Here’s how it might work:

Localized User Profiles

To maintain privacy, the user profile could be stored locally on the user’s device rather than in the cloud. This profile would contain all the necessary information to make decisions about which ads to show, but it wouldn’t be accessible to external servers or companies. The AI agent would operate within this local environment, using the data without ever transmitting it elsewhere.

Federated Learning

Federated learning could play a role in enhancing this system. In this setup, the AI model would learn from the data on the user’s device and improve its ad selection process. The key here is that the model is updated across many devices, but only the learned insights (not the raw data) are sent back to a central server. This means the user’s personal data never leaves their device, yet the model can still improve over time.

Differential Privacy

Differential privacy techniques could be applied to ensure that even if some data does need to be shared (for example, aggregated statistics to improve the ad model), it’s done in a way that individual users can’t be identified. This method adds a layer of noise to the data, making it difficult to trace any information back to a specific user.

User Control and Transparency

The user should have complete control over their profile. They could set preferences, see what data is being used, and opt-out of certain types of data collection or ad targeting. Transparency is key—users should know what’s happening with their data and why certain ads are being shown.

Edge Computing

Leveraging edge computing, where data processing occurs on the user's device rather than in centralized cloud servers, would also enhance privacy. The smart ad block agent could function efficiently without needing to send data back and forth over the internet, reducing the risk of data breaches or misuse.

Blockchain for Data Integrity

For added security and transparency, blockchain technology could be used to record interactions and data access. This would create a tamper-proof log that the user could audit to see how their data is being used, ensuring that the AI agent adheres to privacy standards.

In summary, by keeping the user profile local, using federated learning, and employing privacy-preserving techniques like differential privacy, we can create a system that gives users control over their ad experience without compromising their privacy.

What do you think of this approach? Would you like to explore more about how this smart ad block agent might interact with DSPs and SSPs?

You said: I am thinking the agent has some mechanism to influence the real time bidding. The profile would need to be shared with the SSP. How can we share information with a potentially untrustworthly 3rd party?

ChatGPT said:

Sharing information with a potentially untrustworthy third party, like an SSP, while maintaining user privacy is a tricky challenge. However, a few advanced techniques could help mitigate the risks:

Zero-Knowledge Proofs (ZKPs) Zero-Knowledge Proofs allow one party (the user’s agent) to prove to another party (the SSP) that a statement is true without revealing the underlying information. In this context, the smart ad block agent could prove that a user falls into a certain category (e.g., “interested in travel”) without revealing specific data. The SSP can then use this proof to tailor ad bids without accessing the actual profile details.
Homomorphic Encryption With homomorphic encryption, the user’s data could be encrypted in such a way that the SSP can perform computations on it (e.g., deciding on ad relevance) without ever decrypting the data. The SSP would receive the encrypted profile, process it, and return the encrypted results. Only the smart ad block agent would be able to decrypt and use these results to influence the bidding process.
Secure Multi-Party Computation (SMPC) In SMPC, data is split into pieces and distributed among different parties, so no single party has access to the entire dataset. The SSP could receive a piece of the user’s profile data, while another piece is held by a trusted entity (or even split among multiple SSPs). They would collaboratively perform the bidding process without any party gaining full access to the user’s profile.
Differential Privacy in Data Sharing By applying differential privacy, the profile shared with the SSP could be perturbed or anonymized so that individual user details are obscured. The SSP would receive data that is useful for ad targeting but doesn’t reveal precise personal information. This ensures that even if the SSP is untrustworthy, they can’t extract meaningful details about the user.
Tokenization and Trusted Execution Environments (TEEs) User data could be tokenized, meaning that sensitive data is replaced with non-sensitive equivalents (tokens) that the SSP can use without exposing the original data. Additionally, TEEs can be used where the SSP processes data in a secure, isolated environment that the SSP itself cannot tamper with. The smart ad block agent could share data securely within these environments, ensuring that only the intended use is possible.
Policy-Based Access Control The smart ad block agent could enforce strict policies on how the SSP can access and use the data. This might involve contractual agreements backed by legal frameworks, combined with technical measures like access control lists (ACLs) and usage monitoring. The SSP would only receive the bare minimum information necessary for ad targeting, with all actions monitored and logged.
Blockchain for Auditable Sharing To add a layer of transparency and trust, blockchain technology could be used to record every instance of data sharing and processing. This would create an immutable log that both the user and third parties could audit to ensure compliance with agreed-upon privacy standards.
Data Anonymization and Aggregation Rather than sharing raw profile data, the smart ad block agent could anonymize and aggregate the data before sharing it with the SSP. This approach ensures that the SSP can still make informed bidding decisions without access to specific, identifiable information.

By leveraging these techniques, the smart ad block agent can influence real-time bidding while minimizing the risk of exposing sensitive user data to untrustworthy SSPs. The key is finding the right balance between utility for the SSP and privacy for the user.

Does this align with what you had in mind, or is there a specific method you’d like to delve deeper into?

You said: Can you see the current structure of an openrtb in json? What parts would we be able to fill in?

ChatGPT said:

In the current structure of an OpenRTB request, various fields are used to provide information about the ad request, the user, the context, and the inventory being bid on. The OpenRTB protocol is quite detailed, and the request is typically represented in JSON format.

Here’s a simplified example of an OpenRTB bid request in JSON:

{
  "id": "1234567890",
  "imp": [
    {
      "id": "1",
      "banner": {
        "w": 300,
        "h": 250,
        "battr": [1, 2, 3]
      },
      "bidfloor": 0.5
    }
  ],
  "site": {
    "id": "102855",
    "domain": "example.com",
    "cat": ["IAB1-1"],
    "page": "http://example.com/home"
  },
  "device": {
    "ua": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36",
    "ip": "123.123.123.123",
    "geo": {
      "country": "USA",
      "region": "CA",
      "city": "San Francisco",
      "zip": "94107"
    },
    "dnt": 0,
    "lmt": 0,
    "language": "en"
  },
  "user": {
    "id": "55816b39711f9b5acf3b90e313ed29e51665623f",
    "yob": 1984,
    "gender": "M",
    "geo": {
      "country": "USA",
      "region": "CA",
      "city": "San Francisco",
      "zip": "94107"
    }
  },
  "at": 2,
  "tmax": 120,
  "cur": ["USD"],
  "bcat": ["IAB25", "IAB7-28", "IAB8-5", "IAB8-18"],
  "badv": ["example.com"]
}

Parts Where the Smart Ad Block Agent Could Intervene

User Information (user)
    User ID (user.id): The agent could decide whether to share a specific user ID or an anonymized version. This might involve using a hashed identifier or a pseudonymous ID.
    Year of Birth (user.yob): The agent might obfuscate or generalize this information (e.g., providing an age range instead of a specific year).
    Gender (user.gender): The agent could choose to share this information if it’s relevant to the ad, or omit it entirely.
    User Geo Data (user.geo): The agent could share generalized location data, such as only the country or region, to preserve privacy while still allowing for location-based targeting.

Device Information (device)
    User Agent (device.ua): The agent might modify or generalize the user agent string to prevent fingerprinting.
    IP Address (device.ip): Instead of sharing the exact IP, the agent could use a proxy or a VPN to mask the user’s true location.
    Do Not Track (device.dnt): The agent would ensure that the dnt flag reflects the user's preference regarding tracking.

Contextual Data (site)
    Domain (site.domain): The agent could allow the sharing of this data since it's often necessary for contextual targeting. However, it could potentially provide a more generalized domain if privacy concerns arise.
    Page (site.page): The agent might decide whether to share the specific page URL or a generalized version of it.

Impression Data (imp)
    Bid Floor (imp.bidfloor): The agent could influence the bid floor based on the user’s preferences or perceived value of their data, effectively setting a minimum bid amount that it deems acceptable.
    Blocked Creative Attributes (imp.banner.battr): The agent could decide which types of ads to block (e.g., autoplay videos, pop-ups) by setting specific creative attributes.

Blacklists (bcat and badv)
    The agent could manage lists of blocked categories (bcat) and advertisers (badv) based on the user’s preferences, ensuring that only acceptable content is shown.

Sharing Sensitive Information

When it comes to sharing sensitive information with the SSP:

Anonymization: Before sharing, the agent could anonymize or aggregate data to minimize the risk of identifying the user.
Encryption: The agent might encrypt certain fields, allowing only authorized SSPs to decrypt and use the data.
Selective Sharing: The agent could choose to share only the fields that are absolutely necessary for the SSP to process the bid, keeping more sensitive data either withheld or generalized.

Conclusion

The smart ad block agent could fill in many parts of the OpenRTB structure while ensuring privacy. It would act as a gatekeeper, deciding what data to share, how to share it, and under what conditions, based on user preferences and privacy considerations.

Does this breakdown help? Would you like to explore how specific fields might be managed in more detail?

You said: What if we included an id for the agent. This id would allow an agent pair. The pair agent would exist on the DSP side, looking at the bid stream. How could we leverage these two agents together?

ChatGPT said:

Introducing an agent pair, one on the user side and one on the DSP side, opens up intriguing possibilities for enhancing both user privacy and ad targeting efficiency. Here’s how these two agents could work together:

Secure and Coordinated Communication The agent on the user’s device could generate an ID that both agents recognize as a secure channel for communication. This ID would be unique to the user but wouldn't reveal their identity. When the user’s agent sends data in an OpenRTB bid request, the DSP-side agent can recognize this ID and know that it’s dealing with a trusted, privacy-preserving counterpart.
Enhanced Data Matching Instead of sharing the full user profile with the SSP, the user-side agent could send an encrypted, minimal version of the profile data to the DSP-side agent. The DSP-side agent, having access to the bid stream and the DSP’s models, could match this data against existing audience segments or bidding strategies without exposing the full profile to the broader system. This matching could include interests, behaviors, or contextual relevance based on data the DSP already holds.
Real-Time Negotiation and Optimization With an agent pair, real-time negotiation becomes possible. For example, the user-side agent could set parameters or preferences (e.g., a minimum bid floor, acceptable ad types, or preferred brands). The DSP-side agent could then negotiate with the DSP to meet these conditions, optimizing both the ad's relevance to the user and the bid's value to the advertiser.
Dynamic Consent and User Control The user-side agent could allow the user to adjust privacy settings on the fly, influencing what data is shared and under what conditions. The DSP-side agent could instantly adjust the bidding strategy based on these settings. If the user, for instance, toggles a preference to see fewer ads about a certain topic, the DSP-side agent could quickly recalibrate its targeting strategy.
Collaborative Filtering and Ad Personalization The two agents could collaborate to refine ad personalization without sharing raw data. The user-side agent might know detailed personal preferences, while the DSP-side agent understands which ads perform best within certain contexts. By sharing insights (rather than raw data), they could co-create highly personalized ads that respect the user's privacy.
Privacy-Aware Bidding Strategies The DSP-side agent could implement privacy-aware bidding strategies based on signals from the user-side agent. For example, if the user’s agent indicates a high privacy preference, the DSP-side agent might opt for less intrusive ad formats or avoid bidding altogether in scenarios where sensitive data might be at risk.
Reporting and Feedback Loops After an ad is served, the DSP-side agent could provide anonymized performance feedback to the user-side agent, which could help refine future targeting without compromising user privacy. This feedback loop ensures that both agents are continuously learning and optimizing, improving ad relevance over time.
Trust and Transparency The existence of the agent pair could be made transparent to the user, enhancing trust. The user could receive insights into how their data is being used and how ads are being selected. This transparency could be a key differentiator, making users more comfortable with ad targeting because they know an agent is working on their behalf.
Regulation Compliance Both agents could work together to ensure that all data processing and sharing comply with regulations like GDPR or CCPA. The user-side agent could enforce the user’s rights (like data access or deletion requests), while the DSP-side agent could ensure that the DSP’s operations remain compliant with these demands.
Adaptive Ad Delivery The DSP-side agent could adapt the delivery of ads based on real-time feedback from the user-side agent. If the user reacts negatively to a certain type of ad, the DSP-side agent can instantly adjust its strategy to avoid similar ads in the future, ensuring a more positive ad experience.

Implementation Considerations

Synchronization: The two agents would need a reliable and secure method to synchronize data and decisions, ensuring that the user experience is seamless.
Scalability: The system should be scalable, allowing millions of agent pairs to operate without causing delays or significant overhead in the ad bidding process.
Security: Strong encryption and authentication mechanisms would be crucial to prevent malicious actors from hijacking the communication between the agents.

This agent-pair approach could revolutionize how ads are delivered, making the process more user-centric, privacy-conscious, and efficient. How does this align with your vision? Any specific areas you’d like to explore further?

You said: There are competing forces. The profit motives of companies can often work against the best interest of the user. I am thinking of addictive things like gambling and alcohol. A user can work against there own interest. I realize this is an impossible problem to solve, but what could these agents do to help?

ChatGPT said:

You’re absolutely right—there’s a delicate balance between profit motives and the best interests of users, especially when it comes to potentially addictive or harmful products like gambling or alcohol. While it might be impossible to fully solve this problem, the agent pair system could implement several strategies to mitigate the risks and help users make healthier choices.

User-Defined Boundaries and Preferences The user-side agent could allow users to set boundaries around the types of content they wish to avoid, such as gambling or alcohol ads. These preferences could be enforced rigorously by the agent, preventing such ads from being shown even if they might be highly profitable for advertisers. The DSP-side agent would respect these boundaries, reducing the bid value or excluding these categories from the bidding process altogether.
Ethical AI Principles The agents could be designed to follow a set of ethical AI principles, prioritizing user well-being over short-term profit. For example, if the user-side agent detects that the user has a history of engaging with gambling content, it could take proactive steps to reduce the frequency of such ads, even if the user hasn't explicitly set a boundary. This could involve applying a higher threshold for allowing these ads through or dynamically adjusting the targeting criteria to minimize exposure.
Adaptive Ad Filtering The user-side agent could implement adaptive filtering, learning from the user’s behavior over time. If the agent notices that the user is frequently engaging with potentially harmful content, it could gradually reduce exposure to related ads. This wouldn’t be an outright block but rather a subtle shift towards less risky content, helping to nudge the user in a healthier direction.
Real-Time Health Indicators The user-side agent could incorporate real-time health indicators or warnings, alerting users when they are exposed to potentially addictive content. For example, if a user frequently clicks on gambling ads, the agent could trigger a gentle reminder about the risks associated with gambling. This might not prevent the user from engaging but would provide a moment of reflection before proceeding.
Collaborative Filtering with a Health Focus The agent pair could collaborate to filter and prioritize ads that align with the user’s long-term well-being. For example, the DSP-side agent could prioritize bids for ads related to healthier alternatives (e.g., mindfulness apps instead of gambling sites) when it detects that the user might be at risk of over-engaging with harmful content. The user-side agent would ensure that these alternatives are shown more frequently.
Incentivizing Positive Behavior The system could introduce a rewards mechanism for users who engage with healthier content. For example, if a user opts to watch a fitness-related ad instead of a gambling ad, they could receive some form of reward (e.g., loyalty points, discounts on related products). The DSP-side agent could adjust the bidding process to include these incentives, making healthier ad content more attractive.
Regulatory Compliance and Transparency Both agents could enforce strict compliance with regulatory frameworks that govern advertising for harmful products. The user-side agent could give users access to tools that allow them to see exactly how their data is being used, and why certain ads are being shown. Transparency would empower users to make informed decisions about what they’re exposed to.
Dynamic Risk Assessment The agents could perform real-time risk assessments based on the user’s behavior and history. If the user’s behavior suggests a growing dependency on certain types of content (like frequent engagement with alcohol ads), the agents could dynamically adjust the ad targeting criteria to reduce exposure to those categories.
Community and Peer Support Integration The user-side agent could integrate with community or peer support systems. For example, if a user is at risk of developing a gambling problem, the agent could suggest community resources, helplines, or even block access to certain types of content during vulnerable times. The DSP-side agent could reinforce this by deprioritizing bids from categories that contribute to these risks.
Incorporating User Feedback Loops The user-side agent could allow users to provide feedback on the ads they see, specifically asking whether they found certain ads helpful or harmful. This feedback could then inform future ad targeting, helping the agents learn and adapt to better support the user’s well-being.

Conclusion

While it’s impossible to completely eliminate the risks associated with addictive content, these strategies could help mitigate the impact and guide users toward healthier behaviors. By empowering users with more control, incorporating ethical principles, and leveraging AI’s ability to learn and adapt, the agent pair could play a significant role in balancing profit motives with the user’s best interests.

Would you like to explore any of these ideas further, or add more to the mix?

You said: What should we title this conversation?

ChatGPT said:

Suggested Title: "Balancing Privacy, Profit, and User Well-Being: The Role of AI Agents in Real-Time Bidding"

You said: Thanks chatgpt

ChatGPT said:

You're welcome, Brandon! If you need anything else, feel free to reach out. Good luck with your blog post!