Perplexity about LLMs disagreeing on the term Synthetic Data: The confusion likely arises because AI and computer algorithms are commonly used to generate synthetic data, especially at scale...

Question 1 of 2 for AI services: When reading reports in the media, there seems to be some confusion as to what Synthetic Data is, in the context of AI. Some say it is merely pseudonymized or anonymized - or otherwise sanitized - real data. Others say it is entirely ‘artificially generated’ data, e.g. doctor-patient conversations conducted by medical experts and med students rather than real patients. Can you provide me with an accurate definition of Synthetic Data and whether or not the examples given are indeed examples of Synthetic Data or not?

ChatGPT-4o’s answer:
Definition of Synthetic Data:

Synthetic data refers to data that is artificially generated rather than being directly obtained by measuring real-world events. It is created through various methods, such as simulations, statistical models, or machine learning algorithms, to mimic the characteristics of real data. The primary purpose of synthetic data is to replicate the statistical properties and patterns of real-world data without revealing any actual sensitive information, thus ensuring privacy and confidentiality.

Clarifying Examples:

1. Pseudonymized or Anonymized Real Data:

   • What it is: Pseudonymization and anonymization are techniques used to protect privacy by removing or replacing personal identifiers in real data. The underlying data still originates from real-world events or transactions, but identifying information is stripped away.

   • Is it Synthetic Data? No. While anonymized or pseudonymized data is altered to protect privacy, it is still based on real-world data. Therefore, it is not synthetic data because it does not involve generating new data from scratch; it is merely a transformation of existing data.

2. Artificially Generated Data (e.g., Simulated Doctor-Patient Conversations):

   • What it is: This data is created from scratch, often by experts or through simulations, to replicate real-world scenarios. For instance, medical experts and students might simulate doctor-patient conversations to create a dataset without involving actual patients.

   • Is it Synthetic Data? Yes. This is a prime example of synthetic data. The data is generated artificially and is designed to resemble real-world interactions without involving any real patients, thereby ensuring privacy while still being useful for training AI models.

Summary:

• Synthetic Data is entirely artificially generated and mimics real data without using any actual data points from real events.

• Anonymized or Pseudonymized Data is not synthetic data; it is real data that has been modified to protect identities but still originates from actual events.

In your examples, the simulated doctor-patient conversations conducted by medical experts and students are synthetic data. However, pseudonymized or anonymized data is not synthetic data.


Perplexity’s answer:
Synthetic data is artificially generated information created to mimic the statistical properties and patterns of real-world data, without containing any actual personal or sensitive information from the original dataset. It is produced by AI algorithms or computer simulations trained on real data samples, rather than being collected from real-world events or individuals. To clarify some common misconceptions:

What Synthetic Data Is Not

• Synthetic data is not simply pseudonymized, anonymized, or sanitized real data. These techniques modify existing data, while synthetic data is entirely artificially generated.

• It is not the same as mock data or randomly created data. Synthetic data is statistically representative of the original data, while mock data typically lacks meaningful statistical properties.

Key Characteristics of Synthetic Data

• Generated by AI models or computer algorithms trained on real data samples

• Mimics the statistical properties, patterns, and structure of original data

• Does not contain actual information about real individuals or events

• Can be customized and produced in large quantities as needed

Examples

The examples you mentioned can be clarified as follows:

1. Doctor-patient conversations conducted by medical experts and students: This would not be considered synthetic data. While artificial, these are human-generated simulations rather than AI-generated data based on statistical patterns from real conversations.

2. AI-generated patient records or conversations: If created by an AI model trained on real medical data to replicate statistical patterns and characteristics, this would be considered synthetic data.

Synthetic data is increasingly used in various fields, including machine learning, software testing, and data analysis, particularly when privacy concerns or data scarcity make using real data challenging. It offers benefits like privacy protection, bias reduction, and the ability to generate large, diverse datasets quickly and cost-effectively.

Claude’s answer:
Synthetic data, in the context of AI, refers to artificially generated data that mimics the statistical properties and patterns of real-world data without containing any actual real-world information. It's created using algorithms and models rather than being collected from real-world sources or events.

To clarify the confusion you've mentioned:

1. Pseudonymized, anonymized, or sanitized real data: These are not considered synthetic data. These processes involve modifying real data to remove or obscure identifying information, but the underlying data still comes from real-world sources. While these methods are important for data privacy and protection, they don't create synthetic data.

2. Artificially generated data (e.g., simulated doctor-patient conversations): This is a correct example of synthetic data. When medical experts and students create simulated conversations that mimic real doctor-patient interactions, they are generating synthetic data. This data is artificial but designed to reflect the patterns, structure, and characteristics of real conversations without using any actual patient information.

Key characteristics of synthetic data:

1. Artificially created: It's generated by algorithms, models, or simulations rather than collected from real-world events.

2. Statistically similar: It maintains the statistical properties and relationships found in real data.

3. Privacy-preserving: It doesn't contain any real individuals' information.

4. Customizable: It can be generated to meet specific needs or to represent rare scenarios.

Examples of synthetic data include:

• Computer-generated images for training object recognition systems

• Simulated financial market data for testing trading algorithms

• Artificially created medical records for healthcare research

• Generated text data for natural language processing tasks

In summary, synthetic data is entirely artificially generated to mimic real-world data patterns, while anonymized or pseudonymized data are modified versions of real data. The simulated doctor-patient conversations you mentioned are indeed examples of synthetic data, while sanitized real patient records would not be considered synthetic data.


Google Gemini’s answer:
Synthetic Data: A Clear Definition

Synthetic data is data that is artificially generated, rather than collected from real-world sources. It mimics the characteristics of real data but is created from scratch using algorithms and statistical models.

Key Differences from Real Data

• Origin: Real data is collected from the real world, while synthetic data is created artificially.

• Privacy: Synthetic data inherently protects privacy as it doesn't contain real personal information.

• Customization: Synthetic data can be tailored to specific needs or scenarios, unlike real data which is fixed.

Examples of Synthetic Data

• Generated medical records: These could include patient histories, diagnoses, and treatment plans created by algorithms to simulate real-world data.

• Simulated customer interactions: Conversations between customers and customer service representatives can be generated to train AI models for handling various customer inquiries.

• Artificial financial data: Synthetic data can be used to test financial models or to simulate market conditions.

Distinguishing Synthetic Data from Anonymized or Pseudonymized Data

While anonymized or pseudonymized data is designed to protect privacy, it is still derived from real-world data.Synthetic data, on the other hand, is created from scratch and doesn't rely on existing real-world data.

To summarize:

• Synthetic data: Artificially generated data that mimics real-world characteristics.

• Anonymized or pseudonymized data: Real-world data with personal identifiers removed or replaced.

Based on the examples you provided:

• Doctor-patient conversations conducted by medical experts and med students: This could be considered synthetic data if it's created from scratch without relying on real-world patient interactions.

• Pseudonymized or anonymized data: These would not be considered synthetic data as they are derived from real-world data.

The confusion often arises because synthetic data can be created using real-world data as a template, but the final product is distinct and artificially generated.

Question 2 of 2 for AI services: So, interestingly, the large language models also seem to have differing opinions with ChatGPT-4o arguing that “Simulated Doctor-Patient Conversations” can be labeled Synthetic Data while Perplexity argues that “Doctor-patient conversations (…) would not be considered synthetic data”. It follows up by arguing: “While artificial, these are human-generated simulations rather than AI-generated data based on statistical patterns from real conversations.” So is there a need for Synthetic Data to be both “not real data” AND “produced by an AI model” or at least not by humans? Claude argues that simulated doctor-patient conversations CAN be considered Synthetic Data but it also indicates that one of the requirements for synthetic data is that it is being produced by Artificial Intelligence or computers. What is the correct answer here and does man-made Synthetic Data exist?

ChatGPT-4o’s answer:
The concept of synthetic data is indeed nuanced, and the differing perspectives you've encountered highlight the complexity of defining it.

Core Definition of Synthetic Data:

Synthetic data refers to data that is artificially generated rather than being derived directly from real-world events. It is typically created using algorithms, simulations, or models to replicate the statistical properties and patterns of real-world data. The key characteristic of synthetic data is that it does not include any real personal information or data points; instead, it mimics the structure and behavior of real data without directly using it.

Key Points of Clarification:

1. Origin of Data:

   • Synthetic Data: Is generated from scratch by computer algorithms, statistical models, or simulations. It mimics the real-world data patterns but is not derived from actual events or interactions.

   • Anonymized/Pseudonymized Data: Although this data has been modified to protect privacy (by removing or altering personal identifiers), it is still based on real-world data and thus is not synthetic.

2. Human vs. AI Generation:

   • There is a debate over whether synthetic data must be generated by AI/computer algorithms or if it can also include data created by humans (e.g., simulated doctor-patient conversations conducted by medical experts).

   • AI/Computer-Generated Synthetic Data: Most definitions agree that synthetic data typically involves some form of algorithmic generation, where the data is created by machines rather than humans, ensuring it is free from any real-world data points.

   • Human-Generated Simulations: While human-generated data can be designed to simulate real-world interactions (such as doctor-patient conversations), the classification of this as synthetic data depends on whether the data is artificially generated to a degree that it replicates real-world statistical patterns without any real data.

Man-Made Synthetic Data:

The discussion around whether human-generated simulations (like the doctor-patient conversations example) qualify as synthetic data highlights a broader interpretation of the term. According to some perspectives, such data can be considered synthetic because it is artificially created and not drawn from actual events. However, others argue that true synthetic data should be generated by algorithms or AI models rather than manually by humans.

Conclusion:

The prevailing view is that synthetic data is typically AI-generated or produced by computer models, replicating real-world data's statistical properties without using actual real-world data. However, man-made simulations (e.g., doctor-patient conversations conducted by experts) may also be considered synthetic in a broader sense, especially when they are created to functionally replace real data while maintaining privacy.

In summary, while there is some flexibility in interpretation, the most stringent definition of synthetic data generally excludes human-generated data unless it is produced via computational methods.

Perplexity’s answer:
The differing opinions among AI models highlight the nuanced nature of defining synthetic data. To clarify this issue, let's examine the key aspects of synthetic data and determine whether human-generated simulations can be considered synthetic data.

Definition of Synthetic Data

Synthetic data is artificially created information that mimics the statistical properties, patterns, and structure of real-world data without containing actual personal or sensitive information. The primary characteristics of synthetic data are:

1. It is artificially generated, not collected from real-world events.

2. It maintains statistical similarity to real data.

3. It preserves privacy by not containing real individuals' information.

4. It can be customized to meet specific needs.

Human-Generated vs. AI-Generated Synthetic Data

The core question is whether synthetic data must be generated by AI or computer algorithms, or if human-generated simulations can also qualify. Based on the definition and characteristics of synthetic data, we can conclude:

1. Human-generated synthetic data can exist and is valid.

2. The method of generation (human or AI) is less important than the data's artificial nature and its ability to mimic real-world patterns.

Examples and Clarification

1. Simulated doctor-patient conversations:

   • If created by medical experts and students to mimic real interactions without using actual patient data, these can be considered synthetic data.

   • The key is that they are artificially created, maintain statistical similarity to real conversations, and do not contain real patient information.

2. AI-generated conversations:

   • These are also synthetic data, created using machine learning models trained on real conversation patterns.

Consensus and Correction

The correct answer is that synthetic data does not necessarily need to be produced by an AI model or computer algorithm. The essential criteria are:

1. The data is artificially created, not derived from real-world events.

2. It mimics the statistical properties and patterns of real data.

3. It does not contain actual personal or sensitive information.

Human-made synthetic data can exist and is valid as long as it meets these criteria. The confusion likely arises because AI and computer algorithms are commonly used to generate synthetic data, especially at scale, but they are not a strict requirement for data to be considered synthetic. In summary, while AI-generated synthetic data is common and efficient, manually created synthetic data that meets the above criteria is also valid and can be valuable in certain contexts, such as training simulations or creating small-scale datasets for specific purposes.

Claude’s answer:
Thank you for bringing attention to this interesting discrepancy in the responses from different AI models. Let's clarify the concept of synthetic data and address the specific question about man-made synthetic data.

Synthetic data, in its broadest definition, is artificially generated data that mimics the properties and patterns of real-world data without containing actual real-world information. The key aspects are:

1. It's artificially created, not collected from real-world events.

2. It maintains statistical similarities to real data.

3. It doesn't contain real individuals' information.

The main point of contention seems to be whether synthetic data must be generated by AI/computer algorithms or if it can be created by humans. Let's address this:

1. AI/Computer-generated synthetic data: This is the most common and scalable form of synthetic data. It's created using algorithms, statistical models, or machine learning techniques. This type of synthetic data can easily produce large volumes of data and capture complex patterns.

2. Human-generated synthetic data: While less common, human-generated synthetic data can exist. The simulated doctor-patient conversations created by medical experts and students fall into this category. These are artificial constructs designed to mimic real-world scenarios without using actual patient data.

The correct answer is that both AI-generated and human-generated synthetic data can exist, although AI-generated data is more common and typically what's referred to in technical contexts. Here's why:

1. Purpose: Both types serve the same purpose - to create artificial data that mimics real-world patterns without using actual sensitive information.

2. Scale: AI-generated synthetic data is preferred for its ability to produce large volumes of data quickly and consistently.

3. Complexity: AI can capture and reproduce complex patterns that might be difficult for humans to replicate manually.

4. Use cases: In some scenarios, like the medical conversation example, human-generated synthetic data might be preferred for its domain expertise and nuanced understanding.

To reconcile the different AI responses:

• ChatGPT-4o and Claude are correct in considering simulated doctor-patient conversations as synthetic data, as they meet the core criteria of being artificial and mimicking real-world patterns.

• Perplexity's response is more restrictive, focusing on the common technical definition that usually implies AI generation. However, this narrower definition isn't universally accepted.

• Google Gemini's response is the most comprehensive, acknowledging that synthetic data is typically associated with algorithmic generation but not explicitly excluding human-generated synthetic data.

In conclusion, while synthetic data is most commonly associated with AI and computer algorithms, man-made synthetic data can exist and is valid in certain contexts. The key is that the data is artificially created to mimic real-world patterns without using actual real-world information, regardless of the specific method of creation.

Gemini’s answer:
The differing opinions among the large language models highlight the nuances and complexities of defining synthetic data. While there is a general consensus that synthetic data is artificially generated and mimics real-world data, the specific criteria for qualification can vary.

Here's a breakdown of the key points:

1. Human-Generated vs. AI-Generated:

• Perplexity argues that human-generated simulations, even if they are artificial, are not synthetic data.

• ChatGPT-4o and Claude seem more open to considering human-generated simulations as synthetic data if they are created from scratch and mimic real-world characteristics.

2. AI Involvement:

• Claude explicitly mentions that synthetic data should be produced by artificial intelligence or computers.

• ChatGPT-4o and Perplexity don't explicitly state AI involvement as a requirement, but they do emphasize that synthetic data is generated through algorithms and models.

3. Real-World Data Basis:

• Perplexity suggests that synthetic data should be based on statistical patterns from real conversations.

• ChatGPT-4o and Claude don't explicitly mention the need for a real-world data basis.

Conclusion:

While there are some variations in the definitions provided by the large language models, the core concept of synthetic data remains consistent: it is artificially generated data that mimics real-world characteristics. The specific criteria for qualifying as synthetic data can be more nuanced, depending on the context and the specific definition being used.

Man-made synthetic data does exist. Human experts can create simulations or scenarios that mimic real-world data, as long as they are not directly derived from existing real-world data. This is often done for training AI models or for testing various scenarios.

In essence, the key factors to consider when determining whether something is synthetic data include:

• Artificial generation: Is it created from scratch rather than being derived from real-world data?

• Mimicry of real-world characteristics: Does it resemble real-world data in terms of statistical properties and patterns?

• Privacy protection: Does it avoid using any actual personal or sensitive information from real individuals or events?

By understanding these criteria, you can more accurately identify and classify synthetic data in various contexts.