Provenance Daisy: Attribution in the age of AI

Context & Motivation

When a mathematician shares an idea with a colleague, the colleague knows where it came from and can give credit. AI conversations are different. Most AI systems were not built with ways to attribute their training data and intellectual property to individual contributors (Ref A). Research into attribution exists, but no widely adopted model has solved attribution. Ref A, B).

We can’t undo the past where the sources of training data for AI aren’t well known, but we can change what happens next. Without a solution to attribution, people may have less incentive to share their knowledge and even take up research, a problem that could persist well into the future (Ref C) with grave societal implications. Trust takes years to build. AI developers need to earn it with verifiable evidence that contributors receive fair credit. (Ref D).

Target Audience

Researchers who have shared original ideas with AI and want a mechanism for attribution when those ideas contribute value. AI labs looking to build trust through attribution. AI governance leads working to prevent value erosion when contributions go unrecognized. Anthropologists interested in how incentives shape the qualities society values, including intelligence and ingenuity. Researchers who need better knowledge management and intellectual property protections when working with AI.

Customer Problems

Imagine a mathematics researcher who spends a lifetime working on a millennium problem and shares a key insight with an AI system. The insight’s value may only become clear years later when ideas collected across multiple researchers helps AI solve one of the millennium problems when the connection to the person who shared it has been lost (Ref E). They could lose recognition for their life’s work, along with the financial rewards it might bring (Ref F). The stakes can be substantial: each of the seven Millennium Prize Problems carries a $1 million prize (Ref G).

AI systems cannot take authorship or receive credit in the way people can, leaving recognition and rewards to flow to other people or companies (Ref H, I). The people whose knowledge advances a field could lose both recognition and a livelihood.

Mathematicians are just one example. The problem extends to anyone whose knowledge contributes to AI, raising broader questions about how we manage and reward knowledge in an AI-driven world. People may feel forced to choose between avoiding AI and falling behind, or avoiding fields where AI could make their contributions harder to protect (Ref J, K). Either way, we risk weakening the incentives that drive human intelligence and ingenuity over time (Ref J).

This also undermines trust. When people cannot see how their contributions are tracked or rewarded, it is difficult to know whether AI developers have a genuine incentive to recognize them fairly (Ref C, L).

The underlying problem is value erosion without attribution: the people whose knowledge makes AI useful can go unrecognized and uncompensated.

Hypotheses

1. The link gets lost. The value of an insight may only become clear after its connection to the person who shared it has disappeared.
2. Credit goes elsewhere. AI cannot hold credit in its own right, so recognition and rewards can end up with other people or companies.
3. Contributors carry the burden of proof. Researchers whose ideas are used without recognition carry the burden to prove that their contributions mattered.
4. Contributions go unpaid. Researchers receive no compensation for the value their knowledge adds to AI.
5. Ideas lose their attribution when shared. Someone who receives an idea through AI may reuse it without carrying its original attribution forward.
6. Promises are not proof. Assurances from the organizations that benefit cannot, on their own, establish that contributors were credited or that their ideas were never used.

Solutions

1. Personal agents. Everyone can have a personal AI agent that learns from their experience and conversations. What they teach it stays within their personal agent rather than becoming part of the shared model (Ref M).
2. Find the right agent. When AI needs help solving a problem, it can ask which personal agents have relevant knowledge (Ref N). If someone’s agent provides a valid, useful answer, the contribution is attributed to that agent and the person who taught it.
3. Keep agents isolated. Personal agents remain separate from one another. The company operating the shared model is also prevented from absorbing what these agents learn, protecting the knowledge contributed by each person.
4. Record every agent to agent exchange. Every idea shared between agents is logged. Attribution is recorded at the point where knowledge passes from one agent to another, where the exchange can be tracked, rather than being left to inference inside a model (Ref B, E).
5. Carry credit forward. Every answer comes with a record of its origin and terms governing its use. Recipients cannot use it to train their own models, and any work built on the answer carries the attribution forward, much like citations in research.
6. Establish what the person contributed. A personal agent still relies on a shared model, so not everything it produces necessarily comes from its owner. To separate the two, the same question is tested repeatedly against the shared model without the personal layer (Ref M). The difference in what the personal agent produces helps identify knowledge contributed through the person’s experience. This would provide evidence of contribution.
7. Give credit without pretending the agent is the person. A personal agent may generate words or ideas its owner would not have produced themselves. The owner can still receive credit for knowledge contributed through the agent, without being treated as the author of every output. The agent must identify itself as an agent, the owner can keep unpublished work sealed until they choose to release it, and they can turn the agent off at any time.
8. Put the burden on the builder. Contributions are recorded automatically. If a dispute arises, the AI developer must show that a contribution was not used, rather than requiring the contributor to reconstruct what happened after the fact.
9. Pay for each contribution. Contributors are paid whenever their knowledge is used, according to terms they set, rather than receiving an allocation from a shared pool. When several people contribute to the conception of a result, they share credit for their respective contributions, drawing on the principle of joint inventorship in patent law (Ref I). An independent reviewer resolves disputes.
10. Back trust with independent evidence. Trust should not depend on assurances from the organizations that benefit. Independent audits, published training-data records, binding commitments and meaningful consequences for breaches can provide evidence that the system is operating fairly (Ref D). Technology cannot prove everything. For example, there is no formal way to prove that a particular person’s data was never used in training. In those cases, independent oversight and governance can provide accountability where technical proof falls short (Ref O).

Outcome

1. People can share their best ideas with AI with greater confidence that their contributions will be recognized and fairly compensated.
2. Society continues to reward human intelligence and ingenuity, giving people stronger reasons to pursue the research that advances humanity.
3. Knowledge management evolves to include attribution as a core part of how knowledge is shared, used and rewarded, rather than leaving contributions buried inside models.
4. Trust in AI is built on evidence and accountability, not promises alone.

Appendix

1. Large language models learn from vast amounts of human writing, yet the people who provide that knowledge often go uncredited. Measuring the influence of each contribution has also been limited by its cost. Read the paper
2. A broad evaluation found that influence functions, a leading approach to attribution, perform relatively poorly on large language models. Read the paper
3. Treating people’s data as free capital overlooks their role in creating it, weakens incentives to contribute and benefits the dominant buyers of data. Read the paper
4. To earn trust, AI developers need to make verifiable claims, accept accountability and open their systems to outside scrutiny. Read the report
5. In large language models, the influence of any single training example on a given answer can be diffuse and difficult to trace. Read the paper
6. An AI system solved a long-standing open problem posed by Paul Erdős that carried a $500 prize. Read the paper
7. Each of the seven Millennium Prize Problems carries a $1 million prize. Read about the prize problems
8. AI tools cannot be listed as authors because they cannot take responsibility for the work. Read the position
9. Revised US patent guidance maintains that only humans can be inventors, with inventorship, including joint inventorship, based on contributions to conception. Read a summary
10. Scientists who use AI publish more, receive more citations and lead projects earlier, while scientific research as a whole becomes more concentrated in its focus. Read the paper
11. In a 2026 survey, 16% of US college students said they had changed their major because of AI. Read the findings
12. Three in five researchers surveyed said AI in research could undermine intellectual property and leave authors unrecognized. Read the findings
13. One proposed approach gives each user a personal module trained on their own history and connected to a frozen shared model, allowing them to own their personalized model. Read the paper
14. An open standard allows independent AI agents to discover one another and delegate tasks. Read the paper
15. Researchers have shown that it is not formally possible to prove that particular data was absent from training. What can be audited is whether a process open to outside scrutiny was followed. Read the paper