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Peru can shape its AI future by building practical AI and data literacy, directing projects toward clearly defined public and economic value, and making carefully governed public data useful for learning and problem-solving. Those are the central recommendations in Bill Schmarzo’s open letter to Peru—not a report that these measures have already been implemented or proven to work in the country.
What does Schmarzo’s letter ask Peru to do?
Schmarzo frames the issue as a question of national agency: “What measures can Peru take today to ensure the ethical and appropriate use of AI?” His answer is a set of five proposals about people, priorities, and culture, rather than a blueprint for buying or deploying a particular technology.
He writes that his travels prompted him to share ideas for countries such as Peru, “endowed with abundant natural and human resources,” to shape their AI destiny. The letter’s argument is that AI should serve local needs and shared public value, with decisions grounded in Peru’s own circumstances.
1. Teach AI as a way to create value
Training should help people identify where AI and data can create useful outcomes, not just explain technical tools. In practice, that means connecting learning to real needs and to the decisions people may make with data.
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2. Prioritize results over technology
Schmarzo recommends beginning with economic or social outcomes, then considering whether AI is an appropriate means to achieve them. This approach discourages investment in technology simply because it is new, and instead asks what problem a proposed system is meant to solve.
3. Start AI and data literacy early
The letter calls for introducing literacy from an early age. The goal is not to turn every student into a data scientist, but to help people understand how data and AI shape information, choices, and services.
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4. Teach collaborative, data-science thinking
Schmarzo advocates a way of working that brings people together to frame questions, examine evidence, and interpret results. That collaborative habit matters because a data problem is rarely only technical: defining the problem and judging whether an answer is useful require human context.
5. Nurture cultural innovation
The final recommendation is to encourage innovation that grows from Peru’s cultural distinctiveness. In this view, AI’s purpose is not to make Peru imitate another country’s priorities, but to help people develop solutions suited to local challenges and knowledge.
Why connect AI literacy with public data?
The letter proposes a public open-data initiative as a way to support learning among citizens and students, as well as to help focus scarce analytical capacity on a limited number of high-priority social problems. The proposal is not evidence that such an initiative, or the letter’s other recommendations, has already been put into practice.
Peru’s Presidency of the Council of Ministers (PCM) describes a relevant existing policy context in its 2024 guide to open data. The guide defines open data as government information made available online in standardized, comparable digital form so it can be found and reused for applications, research, business opportunities, economic activity, citizen oversight, and policymaking. It identifies the National Open Data Platform as a place to find, explore, and reuse government datasets.
That access has limits and responsibilities. The guide says open-data use must not compromise personal-data protection, and it places data governance within Peru’s digital-government framework. It assigns the PCM, through its digital-government secretariat, responsibility for directing, supervising, and evaluating the state data-governance framework, while also recognizing distinct public responsibilities for official statistics and personal-data protection. The guide is dated March 25, 2024; it provides context for the letter, not a complete account of every legal or program change since then.
What would make the proposal responsible and useful?
Schmarzo argues that public data efforts need transparent guidelines, measures, governance, oversight, and prioritized use cases. Read alongside the PCM guide, this suggests a practical set of questions for any initiative that makes public information available or uses it in AI work:
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- Purpose: Which defined public or economic problem is the data meant to help address?
- Access and reuse: Can people find, understand, and reuse the information in a consistent digital form?
- Privacy: What safeguards prevent the release or use of data from compromising personal information?
- Accountability: Which public bodies oversee governance, and how will decisions and outcomes be made transparent?
- Public capability: Can students, citizens, and public servants build the skills to interpret the data and question AI-assisted conclusions?
These are considerations raised by the letter’s recommendations and the guide’s description of Peru’s framework; they should not be mistaken for a claim that every safeguard or process is already in place for every dataset or AI application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can people exercise judgment around AI?
Literacy is more than knowing that an algorithm exists. Schmarzo’s letter encourages people to ask what their online activity may have signaled to an AI system when it selects an article, and what action the system may be trying to prompt after they read it. Those questions help make recommendation and persuasion mechanisms more visible, while reminding readers that their data and attention have value.
The PCM guide also names Schmarzo’s book AI & Data Literacy: Empowering Citizens of Data Science as related further reading, describing it as focused on basic data-science tools and a better understanding of AI and data literacy. It is a resource cited by the guide, not an official Peruvian curriculum.
What the letter does—and does not—establish
The letter offers a policy argument: build literacy, focus on outcomes, collaborate, make responsible use of data, and support culturally grounded innovation. It does not evaluate existing Peruvian AI programs, provide evidence that its proposals have delivered results, or compare national strategies to establish which approach works best. Its value is as a framework for public discussion and prioritization, with Peru’s official open-data context offering a concrete point of connection.
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