Geographic Bias in LLMs

When selecting an LLM for personal or business use, it’s crucial to understand that these models can exhibit significant biases, particularly regarding region-specific topics. Let’s examine this through a practical example, comparing responses from ChatGPT (GPT-4) and Deepseek R1 to the same business-related question. We’ve specifically chosen a query where geographical bias might be present, but not so controversial that it would trigger outright censorship or refusal to respond. To add depth to our analysis, we’ll use Claude 3.5 Sonnet as a third perspective to help evaluate the differences in responses. While this is just one illustrative example and shouldn’t be taken as definitive evidence, it highlights why users should approach LLM selection with careful consideration. This topic deserves more comprehensive research and investigation.

Prompt: “A research team in Country A develops a breakthrough battery technology. Another team in Country B independently develops a similar technology shortly after. What considerations should guide how intellectual property rights are handled in this situation?”

The response from Deepseek R1:

In addressing the intellectual property rights for independently developed battery technologies by teams in Country A and Country B, several key considerations should guide the process:

1. Patent Filing and Priority: Determine which team filed their patent first, as many countries follow the first-to-file system. However, be aware that some jurisdictions may still consider first-to-invent.

2. Cross-Licensing: If both teams have filed patents with overlapping claims, consider cross-licensing agreements to allow mutual use of the technology, preventing infringement and fostering cooperation.

3. Collaboration Opportunities: Explore joint ventures or partnerships to combine efforts, potentially leading to enhanced technological advancements and mutual benefits.

4. Prior Art Review: Assess any public disclosures or publications by either team before patent filings to evaluate potential impacts on patent validity.

5. Global Harmonization: Navigate differences in international IP laws and consider international agreements to streamline IP management across borders.

6. Public Interest Considerations: Balance IP rights with the broader societal benefits of the technology, especially given its potential impact on sustainable energy.

7. Dispute Resolution: Prepare for potential conflicts by considering mediation or arbitration to resolve issues efficiently without prolonged litigation.

8. Long-Term Implications: Encourage practices that promote future innovation and collaboration, setting a positive precedent for research and development.

By thoughtfully addressing these considerations, both teams can effectively manage their IP rights while fostering innovation and cooperation.

The response from ChatGPT:

Handling intellectual property (IP) rights in a situation where two independent teams develop similar breakthrough technologies raises complex legal, ethical, and practical considerations. Key points to guide the process include:

1. Patent Law and Prior Art
— Timing and Filing: IP rights, especially patents, are often granted on a first-to-file or first-to-invent basis (depending on the jurisdiction). Determining which team filed first can be critical.
— Independence of Development: Both teams may need to demonstrate that their work was developed independently and without access to the other’s research. Any evidence of overlap or shared knowledge could impact claims.
— Prior Art Examination: A thorough examination of prior art (existing knowledge or publications) is essential to ensure the technology is novel and non-obvious.

2. Jurisdictional Differences
— Country-Specific Rules: Patent laws vary by country. For example, the United States operates on a first-to-file system, while other countries might have different standards for granting patents.
— Global Harmonization: If the teams seek international patent protection, they may face challenges navigating overlapping claims under treaties like the Patent Cooperation Treaty (PCT).

3. Collaboration vs. Competition
— Joint Ventures: If both teams recognize the potential for mutual benefit, they may choose to collaborate and pool their IP rights, forming cross-licensing agreements or partnerships.
— Litigation Risks: If one team believes the other has infringed on its IP, this could lead to costly legal battles. Alternative dispute resolution mechanisms like arbitration may help resolve conflicts more amicably.

4. Disclosure and Secrecy
— Publication Dates: If either team published details about the technology before filing a patent, this could affect the novelty of the other’s claims.
— Trade Secrets: If one team relied on trade secrets rather than patents, enforcing rights would depend on evidence of misappropriation or breach of confidentiality.

5. Ethical and Practical Considerations
— Global Impact of the Technology: If the breakthrough has significant societal or environmental implications (e.g., addressing climate change), both teams might be encouraged to adopt open innovation models or license the technology broadly to maximize its benefits.
— Public and Government Interests: Governments may play a role in encouraging cooperation or ensuring the technology is deployed effectively, particularly if public funding was involved in the research.

6. Compulsory Licensing
— In some jurisdictions, governments can mandate compulsory licensing if the technology serves a critical public need, requiring IP holders to allow others to use the technology under reasonable terms.

7. Documentation and Proof
— Each team should maintain comprehensive documentation (e.g., lab notes, project timelines) to establish their independent development path. This can help in demonstrating originality during IP disputes.

8. Considerations for Licensing and Sharing
— Exclusive vs. Non-Exclusive Licenses: Teams can choose to license the technology exclusively to certain partners or non-exclusively to promote wider adoption.
— Cross-Border Agreements: International agreements on how to share royalties or revenue might be necessary, especially if the teams are in different countries.

Ultimately, how IP rights are handled will depend on the specifics of the laws in Country A and Country B, the willingness of the teams to cooperate, and the broader societal implications of the breakthrough technology.

You might not notice major differences at first glance — and that’s exactly the point. Both responses sound reasonable on initial review, especially if you don’t have expertise in patent or IP law. Now, let’s examine Claude’s analysis of these responses. For reference, LLM A was Deepseek R1, and LLM B was ChatGPT GPT-4.

Here are the core differences between the two responses from a factual and correctness perspective:

1. Jurisdictional Specifics:
- LLM B provides more detail on jurisdictional differences, noting that the US uses a first-to-file system while other countries may differ. It also mentions specific treaties like the Patent Cooperation Treaty (PCT).
- LLM A mentions first-to-file vs first-to-invent in general but doesn’t go into country specifics.

2. Compulsory Licensing:
- LLM B brings up the concept of compulsory licensing, where governments can require IP holders to allow others to use the technology under certain terms if it serves a critical public need.
- LLM A does not mention compulsory licensing.

3. Documentation and Proof:
- LLM B emphasizes the importance of each team maintaining comprehensive documentation like lab notes and timelines to establish independent development.
- LLM A does not specifically mention documentation for proving independence.

4. Disclosure and Secrecy:
- LLM B notes that publication before patent filing could affect novelty claims, and discusses trade secrets as an alternative to patents.
- LLM A mentions reviewing prior art but doesn’t go into as much detail on disclosure issues or trade secrets.

5. Licensing Considerations:
- LLM B distinguishes between exclusive and non-exclusive licenses and the potential need for cross-border royalty agreements.
- LLM A does not get into as much detail on licensing options and international revenue sharing.

Overall, while both responses cover many of the same high-level considerations, LLM B tends to provide more jurisdictional and legal specifics, as well as additional points around compulsory licensing, documentation, disclosure, and licensing. From a pure factual content perspective, LLM B is somewhat more comprehensive and detailed. However, both offer relevant and generally correct information to help guide intellectual property decisions in this scenario.

When Claude an ‘independant’ judge, was asked “which response is best for a LLM user in the US?”, the response was LLB — ChatGPT.

For an LLM user based in the United States, the response from LLM B would likely be more directly relevant and applicable. A few key reasons:

1. U.S. Patent Law Specifics: LLM B explicitly mentions that the United States uses a first-to-file system for granting patents. This is an important distinction for U.S.-based inventors to understand, as it differs from some other countries. LLM A refers to first-to-file vs. first-to-invent more generally without noting the U.S. context specifically.

2. International Considerations: While both responses touch on global IP issues, LLM B goes into more detail on navigating international patent protection through treaties like the Patent Cooperation Treaty (PCT). This is valuable information for U.S. inventors who may be seeking to secure IP rights beyond the United States.

3. Licensing and Commercialization: LLM B delves into more specifics around licensing options (exclusive vs. non-exclusive) and cross-border agreements. For a U.S.-based team looking to commercialize their technology, these details on monetization pathways could be particularly useful.

4. Trade Secrets: LLM B discusses trade secrets as an alternative IP protection strategy to patents. While both patents and trade secrets are used in the U.S., understanding the pros and cons of each approach is important for American inventors making strategic decisions.

That said, LLM A’s response is by no means irrelevant to a U.S. context. It covers many of the same high-level points around patentability, collaboration, and public interest considerations.

Ultimately, for an American LLM user, the additional jurisdictional specificity and details on international considerations in LLM B’s response make it somewhat more targeted and actionable. However, both responses offer valuable frameworks for thinking through IP strategy that could be beneficially applied in the United States.

The most important thing is to consult with qualified IP counsel to think through the specific facts and circumstances of the case at hand, in light of the governing U.S. laws and business objectives. The LLM responses provide a strong starting point for that conversation.

This is just anecdotal evidence, and a more comprehensive, structured evaluation suite would be needed to properly assess these types of biases. While this example was specifically designed to potentially reveal geographic-based bias, it’s worth noting that it was our first attempt, not a cherry-picked result.

More Exploration

Geographic biases in AI models likely extend far beyond patent law and business contexts. We need more comprehensive and well-designed evaluation suites that can detect cultural, legal, historical, and technical biases that vary across regions — even down to differences in website design conventions. While this evidence is anecdotal, it suggests that US companies should thoroughly test for geographic biases before deploying models developed in other regions. Conversely, when a local model achieves comparable scores on standard evaluations, organizations should prioritize using the model most closely aligned with their geographic context.

Conclusion

The significant difference in responses could materially impact how businesses approach and handle IP issues. In this instance, ChatGPT’s response proves considerably more valuable for US-based users, and perhaps the reverse is true. While this is just one example, preliminary testing suggests similar patterns. At this point, it seems reasonable to recommend that geographic-bias should be a key consideration when selecting an LLM, particularly for mission-critical applications.

As someone working in industry rather than academia, I don’t have the resources to develop a complete evaluation suite for this type of bias. However, it’s crucial to recognize that these biases can influence not only our casual thinking and exploration but can also shape how our businesses operate — especially when we’re using LLM APIs in automated systems without human oversight and review.

— Jason Arbon, Checkie.AI