A World Economic Forum analysis published in February 2021 warned that an information gap separated the people building artificial-intelligence systems from those responsible for governing them.
Authors Adriana Bora and David Alexandru Timis argued for regular dialogue, stronger ethics education for technical students and better technical literacy among policymakers.
The problem was not that one group knew everything and the other knew nothing. Each held knowledge the other needed.
Technical Detail Shaped Policy Outcomes
Rules written without understanding how models were trained could demand explanations that were impossible or overlook risks created by data and deployment.
Policymakers did not need to become machine-learning engineers. They needed enough literacy to ask which data was used, how performance was measured and what happened when a system encountered a new population.
Technical advisers could translate concepts without presenting design choices as inevitable.
Engineers Needed Social Context
Developers could optimise a model while misunderstanding the institution around it. A hiring system, for example, reflected labour law, organisational incentives and historical inequality.
Ethics education helped teams recognise that choosing a target variable was also a value decision. Predicting who resembled previous successful employees could reproduce past exclusion.
Social-science and humanities perspectives made technical work more accurate by revealing what the data failed to represent.
Biased Data Offered a Clear Example
Historical records frequently encoded unequal treatment. A model trained on those records might learn the inequality as a useful predictive pattern.
Removing a protected attribute did not necessarily solve the problem because postcode, education or employment history could act as proxies.
Policymakers needed to understand those mechanisms, while developers needed guidance on acceptable outcomes and evidence. Neither group could address discrimination alone.
Facial Recognition Exposed the Gap
Facial-recognition systems had shown uneven accuracy across demographic groups and raised questions about surveillance. Technical improvements could reduce some error without resolving whether a use was proportionate.
A highly accurate system used for continuous public tracking still created a civil-liberties concern. Conversely, a blanket debate about “AI” could miss safer, consent-based identity uses.
Shared language allowed governance to distinguish among contexts rather than treating the technology as one thing.
Dialogue Needed to Be Continuous
One consultation at the end of development arrived too late. Teams and regulators needed channels for discussing emerging capabilities, incidents and evidence throughout the lifecycle.
Regulatory sandboxes could support controlled experiments, while standards bodies could define documentation and testing. Civil society and affected communities also needed seats at the table.
Dialogue should not become a substitute for enforceable rules. It improved the information on which rules were based.
Education Could Build Institutional Capacity
Computer-science programmes could include ethics, law and communication as core professional skills rather than optional additions. Policy schools could teach data, statistics and the limits of automated inference.
Joint projects gave students experience working across disciplines. Public agencies also needed careers that attracted and retained technical specialists.
Without internal expertise, governments depended heavily on the companies they were meant to oversee.
Clear Documentation Created a Bridge
Model cards, data sheets and impact assessments translated technical work into structured evidence. They could state intended use, known limitations, demographic performance and responsible owners.
Documents were useful only when accurate and updated. A generic template filled with marketing language increased paperwork without reducing the information gap.
Regulators could standardise core fields while allowing sector-specific detail.
Accountability Had to Remain Visible
Complexity sometimes encouraged institutions to blame “the algorithm” for a decision. The report’s logic pointed in the opposite direction.
People selected the data, objective, threshold and deployment setting. Organisations decided whether to use the output and how someone could appeal.
Shared understanding made those choices easier to identify and assign to responsible leaders.
Better Communication Improved Both Innovation and Rules
Creators benefited from knowing regulatory expectations before investing in a product. Policymakers benefited from evidence about actual capabilities and limitations rather than hype.
This could produce narrower, more effective rules. High-risk applications might receive strict testing and oversight, while low-risk tools faced lighter obligations.
Clarity reduced uncertainty without treating every automated system as harmless.
The Gap Was an Institutional Problem
The 2021 analysis correctly framed AI governance as a problem of sustained capacity, not a one-time translation exercise.
Technical systems and social rules changed continually. Governments, universities, companies and civil society needed durable relationships capable of learning together while preserving independent scrutiny.
The enduring goal was not complete agreement. It was informed disagreement: a shared factual foundation that allowed society to make visible choices about where AI belonged and what safeguards it required.
