AI principles have become commonplace over the past few years. Fairness, accountability, transparency, human-centered design—these are now standard talking points among tech companies and in the broader AI ecosystem. And yet, if you look at AI in the real world, results often fall short of these ideals. Algorithms still show bias and lack clarity, such that ethical commitments oftentimes remain more aspirational than operational. There is still much work to be done to bridge the gap between principles and practice.
Principles are important. They give us a moral compass and help define what society expects from AI. But principles alone cannot guide the work of an engineer or a data scientist who is building (or perhaps more appropriately given the lack of legibility, growing) a model today. They don’t tell you how to measure fairness in a dataset, how to make a recommendation system more transparent, or how to document complex design decisions in a way that others can understand. That is where technical standards come in.
Making Principles Practical
Technical standards take abstract ideas and make them actionable. They provide concrete guidance on things like data labeling, algorithm testing, and documentation. They create a shared understanding of what principles require in practice, setting industry-wide expectations. When a team follows a standard, they can be confident they are taking steps to align with broader principles such as fairness and transparency.
Standards also make it easier to compare and evaluate different AI systems. Regulators, researchers, and civil society can use them to spot outliers and take action. Companies can use them as internal roadmaps. Standards create a level of clarity and consistency that principles alone simply cannot provide.
Collaboration Is Key
Developing useful technical standards is not something any single group can do alone. Engineers bring technical expertise. Legal scholars and ethicists bring insight into societal values and human rights. Civil society voices ensure that standards reflect real-world impacts. All of these perspectives are necessary to make standards meaningful and practical.
International collaboration is equally important. AI development is global, and the companies shaping these technologies operate across borders. Standards created in one country can influence practices worldwide. Organizations like the International Organization for Standardization (ISO) and the IEEE are already working on AI-specific standards. These efforts are promising, but they need to be more inclusive, adaptable, and responsive to the rapid pace of technological change.
A Tool for Accountability
Standards do something even more important: they make accountability possible. When expectations are clear, we can measure outcomes. If a standard defines how to test for bias, we can audit systems and hold organizations accountable. Standards transform abstract commitments into concrete actions that can be monitored, reported on, and improved over time.
It is important to note that standards are not a replacement for laws or ethical reflection. They complement both. Principles define what we value, laws specify what is required, and standards show how to get there in practice. Together, they form a toolkit for building responsible AI.
Turning Ideas Into Action
One of the biggest challenges in AI governance is moving from statements of intent to real-world implementation. Technical standards offer a pathway to get there. They provide engineers and organizations clarity on what actions to take, and they give the public and regulators more confidence that AI systems are being developed responsibly.
Standards are not static. AI is evolving quickly, and standards must evolve with it. That requires ongoing engagement between technologists, policymakers, and affected communities. Standardization is not just a technical exercise—it is a continuous process of governance, reflection, and collaboration.
Why This Matters
Closing the gap between AI principles and practice is not just a technical concern; it is a societal one. Principles are meaningful only if they shape the systems that touch our lives. Technical standards translate those principles into clear instructions for those developing and deploying AI systems on the ground. Standards transform principles and commitments into actionable, measurable, and enforceable practices.
In essence, technical standards turn ideas into action so that the lofty ambitions we set for AI translate into real-world outcomes. Building responsible AI is not about statements on paper. It is about the choices we make every day in design, implementation, and oversight. Standards help individuals and organizations do so in a way that is thoughtful, accountable, and aligned with the values we care about.