Values Alignment Intelligence: A New Discipline for Detecting Systemic Drift
Introduction
Systemic failures don't just happen. When artefacts like regulatory investigations reports and royal commission findings are examined closely, you'll often find that they are preceded by gradual values drift years before any breaches or violations. Values are complex and subtle things, which means that when institutional behaviour shifts into misalignment, it's often undetectable until it's too late.
The current governance tools that institutions have at their disposal can't detect failures until after the harm has occurred. We monitor financial liquidity and cyber threats in real time, but we only monitor values alignment episodically. The corrective feedback loops that our institutions employ to detect values drift, things like elections, audits, and investigations, are always reactive.
Aligning a system to its goals, which are constructed from the logic of the values of its stakeholders, is a central function of leadership. The core responsibility of senior leaders is to keep organisations on track so that they are achieving their purpose in line with community expectations. The absence of information on misalignment in systems, which sits upstream from goals and operational design, is a gap which means that there are delays in the feedback loops that exposes institutions to risks.
Advances in artificial intelligence may provide us with the opportunity to measure values alignment in human systems in real time. Large language models (LLMs) can extract normative values statements from the artefacts produced by leaders and stakeholders in a system, and can compare them to the processes, actions, behaviours, and motives the system produces. When analysed as an intelligence domain, this information can inform risk owners and senior leadership of systemic misalignment before reputational damage or harms occur.
The objective of this piece is to advocate for the establishment of Values Alignment Intelligence as a discipline to provide early warning of systemic misalignment.
The Problem: Governance Latency in Complex Systems
All complex systems will eventually drift from their stated values due to internal incentives, efficiency optimisation, and process stagnation. This is a feature of systems rather than the fault of any individual or group within an organisation.
Leaders combat this drift through interventions and reforms. But current feedback loops built to respond to values drift are reactive. They rely on lagging indicators like complaints, whistleblowers, media reports, and regulatory interventions. By the time the signal reaches leadership, the harm has already occurred and the consequences are unavoidable.
Traditional risk management focusses on compliance with rules rather than alignment with intent. There are practical and structural reasons why this is the case. Our society is built on bureaucratic processes and laws. Rules are binary, simpler for systems with numerous sub-units and distributed accountabilities to comprehend. They employ specialist staff who advise leaders on rules compliance alongside a belief that to follow process is the same as achieving a fair result. This conflation is understandable: process is visible and auditable, while values alignment is neither. It does however mean that systems frequently comply with rules while violating the values that led to their creation.
The Solution: Values Alignment Intelligence
Values Alignment Intelligence is the systematic collection, processing, analysis, and dissemination of semantic signals (the meaning embedded in language) to detect drift between a system's stated values and its observed behaviours. It leverages the power of modern artificial intelligence framed by the professional rigour of intelligence analysis.
The discipline applies the intelligence cycle to the problem of values alignment:
- Planning and Direction: The organisation formalises its values and goals alongside identifying key stakeholders.
- Collection: Systems ingest unstructured text, such as complaints, internal communications, policy and process documents, and external narratives.
- Processing: LLMs extract normative claims and values signals at scale.
- Analysis: Signals are aggregated and analysed to identify patterns of drift, contradiction, and trade-off.
- Dissemination: Leaders are provided independent early warning assessments of values misalignment.
Values alignment intelligence provides foresight, not prescriptions. It identifies the gaps while leaders decide how to close them.
Why Intelligence Tradecraft?
Intelligence has effective mechanisms to deal with ambiguity, contradictions, and incomplete information. Unlike rules, values are fuzzy, and their individual definitions are contextual and contested. Intelligence tradecraft is designed to extract useful signals from ambiguous noise.
As a discipline, intelligence has always operated in environments where adversaries have employed methods to hide their actions and intentions. Intelligence aims to find the truth behind the narrative. This is similar to dealing with systems which obscure their own drift, either through information being unavailable or through unconscious self-deception. The analytic techniques developed in intelligence analysis are well equipped to deal with complex, internally inconsistent informational environments.
When implemented in line with best practice, intelligence professionals are independent and are not involved in decision-making. Values Alignment Intelligence is designed to expose signals of drift and communicate them to a decision-maker, not to intervene. It means that the Values Intelligence Analyst can be an independent assessor outside of the capture of a system.
While traditional audit may compel organisations to act, intelligence is meant only to inform and make recommendations. Intelligence feeds into risk management and decision-making processes but does not itself act. This separation is crucial as only operational leaders have the full organisational context to make decisions.
Finally, as with all intelligence functions, Values Alignment Intelligence carries epistemic risks. Any system that analyses narratives and institutional behaviour can be misused as a political instrument if safeguards are not embedded into its design. Intelligence analysts, particularly from my experience in Australia, operate with a strong internal culture of proportionality and restraint. They are trusted with the secrets of the system while also ensuring that their invasive powers are only used when operationally necessary.
A Systems Perspective
Understanding where Values Alignment Intelligence sits within a system clarifies its scope and limits. A mapping of Values Alignment Intelligence to Donella Meadows' Leverage Points: Places to Intervene in a System places Values Alignment Intelligence at point 6, the structure of information flows, and point 9, the length of delays. It introduces new information into a system by producing previously unavailable intelligence of misalignment. It also decreases the length of delays in the system significantly by introducing indicators of misalignment proactively rather than waiting for retrospective examinations.
It provides leaders with opportunities to intervene in at point 3, the goals of the system, as well as all the downstream intervention levers. Values themselves are paradigms, core components of worldview, at point 2 of this hierarchy of systemic intervention. The values that the leaders impart on an organisation, along with those of the stakeholders in a community, are vitally important to inform the goals that a system is trying to achieve and how it is permitted to achieve them.
What Values Alignment Intelligence doesn't do is introduce any new interventions. Instead, it complements existing risk management and governance mechanisms. It is best thought of as a new leading indicator about something that boards, executives, and elected representatives already care about. Conduct risks, culture risks, operational risks, and ESG risks can all be better managed by using the outputs of Values Alignment Intelligence.
Viewed through this framework, Values Alignment Intelligence is a modest, low-friction intervention with the potential to use the tools they already have in a more effective way.
Three Horizons of Utility
Values Alignment Intelligence has utility at three horizon scales:
Horizon 1: Operational Resilience
Focus: Mitigating reputational and regulatory risk and proactively minimising harm.
The aim of this horizon is to detect misalignments and values trade-offs that organisations necessarily create daily. It moves measurement of values drift from periodic assessments to real-time telemetry. By employing Values Alignment Intelligence, organisations can better manage their risks and minimise friction with stakeholders.
Horizon 2: Institutional Legitimacy
Focus: Restoring trust through responsiveness.
This horizon is about reducing the feedback latency between citizens and the institutions that support them. Creating a continuous signal loop allows bureaucracies to adapt to feedback faster than existing mechanisms and align processes to stated values. This gives governments a way to anticipate and respond to misalignments in the execution of policies more rapidly than the four-year election cycle.
Horizon 3: Automated Alignment
Focus: Safe scaling of automated decision-making.
If we automate misaligned processes with artificial intelligence, we scale harm at machine speed. This is a core concern in AI safety research. Values Alignment Intelligence provides a moral sense that automated systems, including future AGI, will need to function safely within human societies. By leveraging artificial intelligence to perform continuous values analysis we can provide machine decision-makers with guidance that matches their speed and context.
The methodology is the same at each horizon, only the stakes and the speed of the systems change.
Building the Field
Developing Values Alignment Intelligence as an intelligence discipline is a non-trivial task. At this point in development, some necessary milestones have been achieved:
- Several projects have demonstrated that LLMs are able to extract normative statements from unstructured text.
- The Political Values Analysis and Regulator Values Analysis projects have demonstrated that LLMs can categorise values into a taxonomy.
- The Terms of Service Evaluator and System Values Analysis Tool have demonstrated that LLMs can assess values alignment from institutional documents and media reporting.
- Ontologies for consistent mapping of values and easy integration into LLM workflows already exist.
These are however prototypes and don't produce results that are sufficiently consistent to scale. There is much that needs to be done before Values Alignment Intelligence becomes operationalised.
While Values Alignment Intelligence is based on intelligence tradecraft, it will need to incorporate new practices from other fields. These include moral philosophy, discourse analysis, AI alignment research, systems thinking and cybernetics, psychology and anthropology, and natural language processing. It's only through the novel recombination of these discrete disciplines that analysts will be able to work such a challenging target.
It will need to be supported by data engineering, platform architecture, machine learning operations, and data science to build out the artificial intelligence tooling and infrastructure that this intelligence discipline requires. Current frontier models are surprisingly good at values analysis out of the box, but there are no doubt optimisations that will produce more consistent outputs at scale against what will be a diverse range of collection sources. As with any intelligence discipline, the collection and processing infrastructure will need to be developed to meet the intelligence requirements.
Finally, there needs to be a dedicated, non-profit body to steward the discipline, ensuring it remains a public good rather than a proprietary control mechanism. I propose the establishment of a Centre for Values Intelligence as steward of the craft. It would be built with the goal of ensuring that human values are accounted for in the age of artificial intelligence. This is particularly important given that values are plural and operate within cultural context.
Conclusion
Values Alignment Intelligence offers the potential to address current gaps in operational risk management while also building a field to manage emerging existential risk from artificial intelligence. As we enter an era of high-velocity machine decision making, we need to build values alignment infrastructure that can detect systemic drift at the speed of artificial intelligence. The legacy mechanisms we've inherited, holdovers from the twentieth century, are not up to the challenge. With Values Alignment Intelligence, we can build the moral nervous system for our institutions now so that they can see their own drift before they lose legitimacy and control. It positions us to remain the moral stewards of this world even after we're sharing it with artificial intelligence.