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AI tools are gaining a place at work even as many people remain unsure that companies will use AI responsibly. That gap gives GitHub a strategic opportunity: make Copilot’s data choices, safeguards, and review process clear enough for developers and organizations to judge for themselves. It is an opportunity, not proof that GitHub has earned greater trust or gained business from it.
AI adoption and AI confidence are different measures
Public confidence in AI companies is one part of the trust question. In a Pew Research Center survey of U.S. adults conducted February 17–23, 2026, about six in ten said they were not confident that U.S. companies would develop and use AI responsibly. The finding describes U.S. adults broadly; it does not measure developers, GitHub customers, or Copilot users. Pew Research Center’s 2026 report also found that 67% had little or no confidence in the U.S. government to regulate AI effectively, compared with 62% in 2024.
Meanwhile, workplace permission for AI tools is common in a separate, narrower survey. In 2024, GitHub and Wakefield Research surveyed 2,000 non-student, non-manager enterprise workers at companies with at least 1,000 employees: 500 each in the United States, Brazil, India, and Germany. Across those markets, 59% to 88% said their companies actively encouraged or allowed AI tools. That is a measure of reported employer policy, not workers’ personal confidence in AI or its outputs. The survey was commissioned by GitHub. GitHub’s survey summary provides the context.
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For Copilot, trust has at least two more specific dimensions: whether a developer can assess the code it produces, and whether the handling of prompts and interaction data matches the user’s expectations. A company allowing AI at work does not settle either question. Nor does public concern about AI companies establish what Copilot users think.
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Why this matters specifically for Copilot
Code assistants can influence software that people depend on, so a plausible answer is not the same as a safe or correct answer. GitHub’s own guidance says Copilot Chat should be used as a tool, not as a replacement for human programming. It tells users: “Always review and test code generated by GitHub Copilot Chat to ensure it meets your requirements and is free of errors or security concerns.” That is product guidance from GitHub, not an independent assessment of how often review catches problems. GitHub’s responsible-use guidance sets out the recommendation.
Reviewability matters because developers can inspect a proposed change, check its fit with the surrounding code, run tests, and decide whether to accept it. Those steps give users a way to evaluate outputs rather than treating the assistant as an authority. They reduce reliance on blind acceptance; they do not guarantee that every defect or security issue will be found.
A 2026 arXiv preprint offers a limited example of why that distinction matters. Hamza Khalid, Ronald E. Thompson, Alejandra Sabater, Perucy Mussiba, Kelsey R. Fulton, and Daniel Votipka report a study in which 100 participants evaluated AI-generated options on four C linked-list tasks. The sample and tightly bounded task setting do not establish how all developers judge generated code or how Copilot performs in everyday projects. The study is best read as evidence about a specific evaluation setting, not a broad measure of developer trust. The preprint is available on arXiv.
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The data-use policy puts the trust question in concrete terms
GitHub announced on March 25, 2026 that, starting April 24, it would use interaction data from Copilot Free, Pro, and Pro+ users to train and improve models unless those users opted out. GitHub said Business and Enterprise users were not affected by the change. The distinction is important: the announcement described a change for specified individual plans, not all Copilot users. GitHub’s policy announcement gives the dates, scope, and opt-out framing.
For a user deciding what to do, the key questions are practical: Does the policy apply to this plan? What interaction data is covered? Where is the opt-out control, and does changing it affect future use only or something else? GitHub’s announcement establishes the plan scope and opt-out option, but a user should consult the current policy and account settings for the operative details before relying on an older announcement. Policies and interfaces can change.
This kind of specificity is where trust becomes testable. A clear notice should identify affected plans, data categories, the default, the date a change takes effect, and the steps for changing the setting. A choice that is difficult to find or understand may be formally available without being meaningful to a user. The evidence here documents GitHub’s stated policy; it does not establish how users responded to it or whether it changed their confidence.
What a credible trust response would look like
GitHub’s Trust Center describes commitments to security, privacy, compliance, and transparency. It says the company follows Microsoft’s Responsible AI Standard, which it describes as aligned with the NIST AI Risk Management Framework, and states: “GitHub is committed to the advancement of safe, secure, and trustworthy AI.” These are company statements about its approach, not independent verification that every risk is prevented or that users trust the product. GitHub’s Trust Center is the source for those commitments.
For developers and organizations, a credible response is less a slogan than a set of visible, usable practices:
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- Make data controls legible. Explain what data is used, for which plans, by default or by choice, and how to change the setting.
- Keep policy scope precise. Distinguish individual plans from Business and Enterprise plans, and date policy changes so customers can assess whether information is current.
- Support human verification in the workflow. Encourage inspection and testing of generated code, and make it straightforward to evaluate proposed changes before accepting them.
- Separate commitments from evidence. State what policies and standards the company follows without presenting those statements as proof of effectiveness.
The useful question for a developer is not simply whether AI is trusted in the abstract. It is: “How much do you trust the output from AI tools or AI agents as part of your workflow?” For Copilot, an answer should account for both the output being reviewed and the data policy governing interactions. Clear choices and inspectable results give developers grounds to make that judgment; no survey or corporate commitment can make it on their behalf.
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The evidence shows a live tension: many employers permit or encourage AI tools, while a majority of U.S. adults surveyed by Pew lacked confidence that companies would develop and use AI responsibly. It also shows that GitHub has published responsible-use advice, a Trust Center, and a policy change with an opt-out for specified individual plans. It does not show that developers broadly distrust Copilot, that GitHub’s practices have earned trust, or that trust has increased Copilot sales or market share.
That is why the opportunity is strategic rather than proven. GitHub can make trust more assessable by keeping data choices transparent and by building workflows around review and testing. Developers and organizations can then judge the product on concrete controls and verifiable work, instead of being asked to accept either AI’s promise or a company’s assurances on faith.
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