I have spent more than 41 years in music and the creative industries, working as an executive, songwriter, author, educator, and mentor. Over those decades, I have watched technology repeatedly transform how music is recorded, distributed, marketed, and monetized. Each shift created new possibilities. It also tended to reward those who understood the new system before everyone else did.

Artificial intelligence is the most far-reaching technological change I have encountered because it reaches beyond the mechanics of creativity into questions of identity, authorship, employment, and trust. The public debate often revolves around whether AI will replace artists. But the more urgent question is whether creators and everyday users will have the knowledge, protections, and influence necessary to shape how AI is used before its norms become entrenched.

For the past two years, I have volunteered roughly 20 hours each week teaching beginner-friendly AI and mentoring between five and ten learners on a typical day. I have never charged for this work. The people I meet are curious, capable, and willing to learn. What many lack is not ability, but a clear way into a conversation that often seems to be moving faster than they can follow.

They rarely begin by asking technical questions about model architecture. Their concerns are more immediate and more human: How can AI help me without taking away my voice? How do I know whether an answer is reliable? What happens to the information I enter into a system? How can a musician protect a song, performance, name, or likeness? Who is accountable when an AI system causes harm?

Those questions have convinced me that AI literacy is not a luxury reserved for technologists. It is a form of public-interest infrastructure. It should be available, in plain language, to working adults, students, artists, small-business owners, and communities that are too often invited into technological change only after someone else has written the rules.

The international community has already established a useful foundation. UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted by 193 member states, links responsible AI to transparency, accountability, privacy, education, and culture. The OECD AI Principles similarly emphasize human-centered values, meaningful transparency, and investment in human capacity. The OECD also urges governments to equip people with the skills required to use AI and to support a fair transition as work changes.

In the United States, the National Institute of Standards and Technology’s AI Risk Management Framework offers organizations a voluntary structure for governing, mapping, measuring, and managing AI-related risks. These frameworks differ in scope and purpose, but they share a basic premise: trust cannot simply be added after a system has been deployed. It has to be built from the beginning through human oversight, transparency, accountability, and practical education.

That last element is too often treated as secondary. It should not be. Rules matter, but rules are far less useful when the people most affected by them do not understand the technology those rules are supposed to govern.

Creative work carries personal identity in ways that many other forms of data do not. A voice, face, performance, or songwriting style may be both an artistic signature and the foundation of someone’s livelihood. Generative AI can dramatically expand access to production, experimentation, and distribution. It can also make imitation, misattribution, and unauthorized digital replicas easier and cheaper than ever.

A recent example described by the World Intellectual Property Organization involved the Indian singer Arijit Singh and unauthorized uses of his voice and likeness. The case illustrates why protections for creators can no longer stop at traditional copyright questions. Consent, personality rights, provenance, and clear attribution must become part of the broader global conversation about AI.

None of this is an argument against artificial intelligence. It is an argument for adopting it responsibly. Powerful tools have always required boundaries. Innovation and creators’ rights are not opposing forces. Well-designed safeguards can give artists, educators, platforms, and technology companies greater confidence to experiment without reducing human beings to raw material for technological systems.

First, access should come before adoption. Free, beginner-level education should be available before institutions expect people to use AI at work, in school, or in creative production. Training should explain not only what a system can do, but what it cannot do, where its limitations lie, and when a human being must verify the result.

Second, opportunity and risk should be taught together. People deserve to learn how AI can support research, planning, marketing, experimentation, and creative exploration. They also need practical guidance on privacy, bias, misinformation, security, copyright, and the limits of automated outputs. AI education that sells only the possibilities is advertising, not literacy.

Third, meaningful decisions should remain under human authority. Human oversight must be substantive rather than ceremonial. People affected by an AI-assisted decision should know that AI was involved, understand the basis of the outcome where possible, and have a meaningful way to question or appeal that decision.

Fourth, consent, credit, and provenance should become standard practice. Creative platforms should establish clear permissions, disclose synthetic media, preserve reliable metadata, and offer accessible ways for creators to report unauthorized uses of their work, identity, or likeness.

Fifth, progress should be measured by who is included. A program cannot credibly call itself innovative if it reaches only people who already have the time, money, technical confidence, and institutional support required to participate. Success must also be measured by whether beginners, older adults, independent creators, and underserved communities are being brought into the conversation.

Governments, universities, libraries, media organizations, music companies, and technology platforms all have a role to play. They can fund free introductory education, invite working creators into policy discussions, publish plain-language rules governing AI use, and build practical pathways for consent and accountability. They can also support the educators and mentors who help people move from uncertainty and fear toward informed participation.

At 60, I continue to learn because four decades in creative industries have taught me that no industry stands still. But learning should not require people to surrender their rights, nor should technological enthusiasm require us to pretend that every new tool automatically represents progress.

The future of creativity should not be framed as a contest between human beings and machines. It should be a disciplined partnership in which technology expands human possibility without erasing human ownership, dignity, or voice.

The communities being changed most rapidly by artificial intelligence should not be the last to understand it. They should be among the first to decide what its rules will be.