Pascal's Chatbot Q&As
Posts
Synthetic data can perpetuate or even amplify biases if generated from unbalanced real-world datasets. This challenges the view that synthetic data inherently improve fairness.

Synthetic data can perpetuate or even amplify biases if generated from unbalanced real-world datasets. This challenges the view that synthetic data inherently improve fairness.

Computational and environmental costs of generating synthetic data can still be substantial. This runs counter to the common assumption that synthetic data are universal resource-efficient.

Pascal Hetzscholdt
January 11, 2025

Question 1 of 3 for ChatGPT-4o: Please read the report “Recommendations on the Use of Synthetic Data to Train AI Models” and tell me what it says and provide me with the perfect definition of Synthetic Data in the context of AI training.

ChatGPT-4o’s answer:
The report, Recommendations on the Use of Synthetic Data to Train AI Models, outlines the use, benefits, risks, and governance of synthetic data in training artificial intelligence models. Here’s a summary of key points:

Definition of Synthetic Data in AI Training

Synthetic data refers to information generated by computer simulations or algorithms that mimic the structural and statistical properties of real-world data. It can take various forms, including images, videos, text, and tabular data, and is typically produced by generative models based on domain knowledge, scientific theories, or collected data. There are three main categories:

Fully synthetic: Entirely artificial and not based on real data.
Partially synthetic: Sensitive elements of real data are replaced with synthetic data.
Hybrid synthetic: Combines both real and synthetic data.

Key Advantages

Data Availability: Overcomes data scarcity, enabling robust AI training.
Privacy Protection: Avoids personally identifiable information, safeguarding user privacy.
Bias Reduction: Helps correct imbalances in training data.
Regulatory Compliance: Enables AI training without breaching data protection laws.
Cost-Effectiveness: Reduces expenses associated with real data collection.

Risks and Challenges

Data Quality: Poorly generated synthetic data can result in inaccurate models.
Security Risks: Synthetic data can be reverse-engineered, compromising privacy.
Bias Propagation: Inherent biases in source data can be reflected in synthetic data.
Misuse: Potential for creating deepfakes, misinformation, or intellectual property infringement.

Recommendations for Responsible Use

Mitigate Bias: Regularly assess and minimize biases in synthetic data.
Transparency: Clearly document methods and parameters used in generating synthetic data.
Cybersecurity Measures: Protect synthetic datasets from unauthorized access and manipulation.
Quality Standards: Establish global and local benchmarks for synthetic data use.
Ethical Guidelines: Ensure ethical considerations such as diversity and inclusiveness in synthetic data generation.

This framework aims to foster the responsible and equitable use of synthetic data, particularly in addressing global disparities and achieving the UN’s Sustainable Development Goals. Let me know if you'd like further details from specific sections of the report.

Question 2 of 3 for ChatGPT-4o: List the most surprising, controversial and valuable statements and findings in the report.

ChatGPT-4o’s answer:
Here’s a list of the most surprising, controversial, and valuable statements and findings from the report Recommendations on the Use of Synthetic Data to Train AI Models:

Surprising Findings

LLMs as Synthetic Data Generators
The report highlights that Large Language Models (LLMs), such as GPT, inherently produce synthetic data. This includes generating outputs that mimic the scarcity or bias present in the original training datasets.
Environmental Costs of Synthetic Data
While synthetic data are often cost-effective compared to real-world data collection, the report emphasizes that the computational and environmental costs of generating synthetic data can still be substantial. This runs counter to the common assumption that synthetic data are universally resource-efficient.
Cross-Disciplinary Debiasing Techniques
The report suggests using techniques from social sciences and humanities (e.g., studying proxies of discrimination) to mitigate qualitative biases in synthetic data. This surprising recommendation reflects the complexity of ensuring fairness in AI.

Controversial Findings

Synthetic Data May Amplify Bias
Synthetic data can perpetuate or even amplify biases if generated from unbalanced real-world datasets. This challenges the view that synthetic data inherently improve fairness and highlights potential risks of automating bias.
Link Between Synthetic and Fake Data
The report discusses the blurred boundary between synthetic data and fake data, noting that synthetic data could be derived from or inadvertently used to generate deepfakes, misinformation, or other malicious outputs. This raises ethical concerns about their broader societal impact.
Global South vs. Global North Divide
The report argues that synthetic data have the potential to reduce the digital divide between the Global South and North, but only if policies are specifically designed to ensure equitable access and inclusion. Without proper governance, synthetic data risks perpetuating the existing imbalance.

Valuable Statements and Recommendations

Privacy Protection and Compliance
Synthetic data, which do not contain personally identifiable information (PII), are positioned as a valuable tool for complying with data protection regulations such as GDPR. This makes synthetic data highly valuable for industries like healthcare and finance.
Watermarking Synthetic Data
A standout recommendation is that synthetic data should be watermarked to ensure traceability and transparency. This would help differentiate synthetic from real data and prevent misuse.
Global Quality Standards for Synthetic Data
The report calls for the establishment of global quality standards to ensure trust and interoperability in synthetic data use. This aligns synthetic data governance with broader efforts for AI regulation.
Synthetic Data for Sustainable Development Goals (SDGs)
Synthetic data are framed as key to achieving SDGs, particularly in areas such as healthcare, climate modeling, and financial forecasting. By addressing data scarcity, synthetic data can help develop AI systems tailored to local needs.

These findings and recommendations shed light on the complex trade-offs in using synthetic data and provide actionable insights for practitioners and policymakers. Let me know if you'd like further details on any specific point!

Question 3 of 3 for ChatGPT-4o: Considering all these factors, list all the topics to be aware of before using Synthetic Data either as an AI maker or - indirectly - as an AI user. Is there anything regulators should be doing in this context?

ChatGPT-4o’s answer:
Topics to Be Aware of Before Using Synthetic Data

Here’s a comprehensive list of considerations for AI makers and AI users regarding synthetic data, along with actions regulators should take:

Technical Considerations

Data Quality
- Synthetic data must be realistic, representative, and free of artifacts to ensure the accuracy and reliability of AI models. Poor-quality synthetic data can lead to incorrect or biased results.
- Ensure that data generation techniques align with the intended AI model’s requirements (e.g., image resolution, dataset size).
Bias and Fairness
- Synthetic data can amplify biases present in the original datasets. It’s crucial to assess and debias both the real and synthetic datasets.
- Use social science and domain expertise to identify and minimize qualitative biases.
Generative Techniques
- Different generative models (e.g., GANs, LLMs) have varying strengths and weaknesses. AI makers must understand the limitations and risks of their chosen technique.
Validation and Testing
- Validate AI models using real-world data to ensure robustness. Regularly update synthetic datasets to reflect changing data distributions.

Transparency
- Clearly document the methods and parameters used to generate synthetic data. This includes providing metadata and lineage for the datasets.
Data Privacy and Security
- Synthetic data must protect individual privacy by avoiding any re-identifiable information. Robust cybersecurity measures should be in place to prevent unauthorized access or manipulation.
Representation of Marginalized Groups
- Ensure synthetic data captures diverse populations to avoid excluding or misrepresenting marginalized groups. This is particularly important for applications like healthcare and public policy.
Link to Real-World Impacts
- Be cautious about the potential of synthetic data to propagate misinformation, influence public opinion, or perpetuate harmful stereotypes.

Legal and Regulatory Considerations

Intellectual Property (IP)
- Ensure synthetic data generation does not infringe on IP rights. Using copyrighted material (e.g., images, artistic works) as input for generative models could lead to legal disputes.
Compliance with Data Protection Laws
- Synthetic data must adhere to regulations like GDPR, HIPAA, and other data privacy frameworks. AI users must ensure that data privacy protections remain intact.
Traceability and Accountability
- Implement watermarking or other traceability mechanisms for synthetic data to ensure accountability and prevent misuse (e.g., creating deepfakes).

Practical and Economic Considerations

Cost
- While synthetic data reduces the need for expensive data collection, it comes with computational and environmental costs. Consider the carbon footprint and resource implications of generating synthetic data.
Interoperability
- Synthetic datasets should be compatible with other datasets and systems. Global quality standards can ensure consistency and usability across AI applications.

Risks to Consider

Misinformation and Deepfakes
- Synthetic data may be exploited to produce fake content or disinformation, posing risks to elections, public trust, and global security.
Security Risks
- Reverse-engineering synthetic data could reveal sensitive information about the real data or underlying models.
Global Disparities
- Without intentional design, synthetic data could widen the digital divide between the Global North and South by favoring datasets and models that benefit wealthier regions.

Recommendations for Regulators

Establish Global Standards
- Create global quality and security standards for synthetic data. These should address issues like data accuracy, bias reduction, and ethical use.
Mandatory Documentation
- Require AI makers to document synthetic data generation methods, parameters, and intended use cases. Transparency should be a legal obligation.
Watermarking and Traceability
- Mandate the use of watermarks to distinguish synthetic from real data and track its origin. This would help combat misuse and foster trust.
Regular Audits and Certification
- Implement independent audits to ensure compliance with ethical guidelines and regulatory frameworks. Certify organizations that meet these standards.
Support Research and Development
- Fund research into debiasing techniques, robust generative models, and cybersecurity measures for synthetic data.
Bridge the Global Divide
- Develop policies and partnerships to ensure synthetic data helps reduce inequalities between the Global North and South. Encourage the creation of localized synthetic datasets to improve AI performance in underrepresented regions.
Develop Ethical Guidelines
- Establish clear ethical guidelines for the use of synthetic data, emphasizing privacy, fairness, and inclusivity. These guidelines should align with broader AI governance frameworks.
Public Awareness Campaigns
- Educate AI users and the general public about the benefits and risks of synthetic data, including how to identify and report misuse.

By addressing these factors, stakeholders can use synthetic data responsibly and ensure it contributes positively to AI development and societal progress. Let me know if you'd like further elaboration on any specific point!