This sounds like extra work. It is actually the cheapest form of risk management your company is not yet doing.
The principle is simple. When an AI system and a human director must both comply with the same corporate policy, that policy is doing two different jobs at once. For the human, it is a set of principles to be interpreted using judgment, context, and experience. For the machine, it is a set of instructions to be executed using data points, thresholds, and measurable signals. When you write one document and ask both parties to follow it, you are optimizing for neither.
⚖️ Consider a foundational corporate governance principle: the fair treatment of all shareholders. For a human director, this means something rich and contextual — weighing competing interests, reading a room, sensing when a decision will erode trust even if it is technically defensible. For an AI system, fairness must be formalized through concrete mechanisms: informed consent protocols, non-discrimination constraints, fair statistical distribution of risk across identifiable groups. These are not contradictions. They are translations. And both translations are necessary.
📋 The practical proposal emerging from recent research is straightforward. Corporate policies, regulations, and codes of conduct should exist in two parallel versions.
One version speaks to physical persons — directors, officers, employees. It is written in the language of principles and expectations, drawing on the shared worldview of human actors who can interpret ambiguity and weigh context against rule.
🤖 The other version speaks to autonomous AI systems. It is written in the language of measurable indicators, decision thresholds, and machine-readable constraints. It translates the same principles into something an algorithm can actually execute.
🎯 For corporate leaders, the competitive logic is hard to ignore. Companies that maintain only human-language policies will find their AI systems either under-used because nobody trusts them or dangerously over-used because nobody can audit them. Companies that translate their governance framework into machine-readable parallels will deploy AI confidently, prove compliance cleanly, and adapt faster as regulation evolves.
💡 This is not a theoretical exercise. It is the next phase of corporate documentation — as consequential as the move from paper records to digital systems a generation ago. Firms that invest now in dual-track governance documents are building the scaffolding that every major AI deployment of the next decade will require.
The question to bring to your next board meeting: what does our most important policy say to a machine?
Please vote – What should the autonomous director do?:
Link to the article: https://www.researchgate.net/publication/385566142_Dedicated_operational_context_as_a_basis_for_the_implementation_of_autonomous_artificial_intelligence_systems
Link to the podcast: https://youtube.com/@annaromanova7380
Link to the blog: https://boardmachines.com/
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