Canonical vocabulary

Glossary

A standard fragments when the same idea travels under two names. Every term is fixed here in both languages with one numbered definition.

One term, two languages, one numbered definition. Where the English term is used untranslated in Spanish practice, both entries carry the same word — that is a deliberate decision to stop the vocabulary from splitting across the two editions of this standard.

Acronyms do not translate. WRM, HWFA and the HWF- clause identifiers stay identical in every edition of this standard, present and future; only the words they expand to are localized. A reader who cites WRM or HWF-44 in any language is pointing at the same thing.

G-01AI EmployeeSpanish: Empleado IA: A persistent, role-bound software worker that autonomously executes recurring business responsibilities within explicit limits, with traceable identity, measurable performance, escalation paths and human accountability.
G-02WRM — Work Resource ManagementSpanish: WRM — Administración de Recursos de Trabajo: The discipline that designs, assigns, governs and optimizes work regardless of whether the resource executing it is human or artificial.
G-03AI Role Contract: The operational contract of an artificial position: mission, responsibilities, results, KPIs, authority, exclusions, tools, access, service level, escalation, suspension criteria, governance calendar and accountable owner. Versioned. The equivalent of a job description plus an explicit operating agreement — a prompt gives instructions, a role contract gives responsibility.
G-04Accountable owner: The single identified human, or human governance body, that answers for an AI Employee’s configuration, authority, performance and exceptions. Exactly one, even when the resource receives work from several areas and even when its day-to-day supervision has been delegated. Accountability is never delegable to an artificial resource, because answering for an outcome requires the capacity to bear a consequence. Primary, not exclusive: system, data, security, compliance, vendor and director obligations survive intact (HWF-21). A governance body qualifies as owner only with an identified chair, stated decision rules and emergency capacity.
G-05Shadow mode: A stage in which the artificial resource executes the work but its actions do not affect the operation. Results are compared against a human baseline. The operational equivalent of probation.
G-06Context Provisioning: The onboarding equivalent for an artificial resource. Divided into must know, may consult and must not access. The third category has two legitimate grounds: protecting the information from the resource, and protecting the decision from the information (HWF-35); either way, the restriction is recorded, never silent.
G-07Authority MatrixSpanish: Matriz de autoridad: The record of what a resource may read, write, decide, spend, communicate or execute, and which of those require approval. Autonomy without an authority matrix is an empty word.
G-08Escalation treeSpanish: Árbol de escalamiento: The explicit mapping of exception type, risk and urgency to a destination — a person, a team or a fallback rule. Every exception needs a destination.
G-09Governed memorySpanish: Memoria gobernada: Memory with provenance, scope, retention and deletion rules, plus a named owner and an update mechanism. An undated policy produces answers that are consistent and wrong.
G-10Human Intervention Rate: The proportion of cases or decisions requiring human correction. Reveals how much of the declared autonomy is real and how much is automation quietly propped up by people.
G-11Cost per Successful Outcome: Total cost of producing a correct, accepted result — including platform, integration, supervision, rework, incidents and residual human operation. Comparing a subscription against a salary is the wrong benchmark.
G-12Post-Transition Performance Delta: The change in performance after switching resource or configuration. It does not ask whether the agent is fast; it asks whether the position got better. Without a baseline, an organization can celebrate an improvement that never happened.
G-13HWFA — Hybrid Workforce Fit Assessment: A structured instrument in three stages — eligibility, risk, economics — for deciding whether a responsibility should be Human, Assisted human, Deterministic automation or Artificial. It returns an argument, not a number: an allocation, a risk class, a starting autonomy rung and the conditions that would change the answer. Formerly the Fit Score; renamed because an instrument that refuses to produce a number should not be called one.
G-14Hybrid Workforce ManagerSpanish: Gerente de Fuerza Laboral Híbrida: The role accountable for ensuring each responsibility is executed by the configuration that produces the best result. Neutral by design: never measured by humans replaced or positions converted.
G-15Capacity Elevation Rate: The share of released human capacity that moved to higher-value work. Keeps “productivity” from hiding whether hours became analysis, service, innovation, eliminated waste — or a headcount reduction that should be named.
G-16Kill switch: The technical and procedural ability to suspend an AI Employee immediately. A kill switch without a named owner is only a feature; the role contract must say who may use it and under what condition.
G-17Least privilege / least authority: Least privilege limits what a resource can access; least authority limits what it can decide or commit. They are separate controls and both are required.
G-18Role stewardship vs task executionSpanish: Role stewardship vs. task execution: “Send these twenty follow-ups” is a task. “Manage commercial follow-up for this portfolio” is a role: prioritizing, respecting constraints, keeping context, recognizing exceptions and escalating. The move from one to the other is what justifies the category.
G-19Remediation planSpanish: Plan de remediación: The artificial equivalent of a performance improvement plan: reduce scope, increase approvals, correct configuration or context, and validate again before restoring authority.
G-20Fallback architectureSpanish: Arquitectura de fallback: The succession plan for an artificial resource: an alternative model, agent or vendor, plus a replacement runbook. Reduces lock-in and makes retirement survivable.
G-21Offboarding / deprovisioning: Revoke credentials, disable tools, stop schedules and queues, rotate secrets, transfer outstanding work and context, preserve evidence. As important as onboarding and almost always skipped.
G-22Time-to-autonomy: Days from instantiation to reliable performance at the expected risk level. The artificial counterpart of time-to-productivity for a new hire.
G-23Total Cost of AI Employment: Models plus infrastructure plus integrations plus supervision plus error cost plus governance. The artificial analog of total cost of employment, and the only honest basis for comparison.
G-24Management by exception: The operating mode in which the resource resolves routine work inside its limits and the manager intervenes on deviations and exceptions. Rung 4 of the autonomy ladder.
G-25Assisted humanSpanish: Humano asistido: A position held by a person, some of whose functions are executed with artificial assistance. The occupant is human: accountability, the decisions and the counterparty relationship stay with the person, while the artificial resource prepares, drafts, analyzes or recommends. This is not a third type of employee — it is a human employee, assisted. What is hybrid is the workforce, not the person.
G-26Supervisor: Whoever directs an AI Employee’s work day to day: routing tasks, reviewing output, setting priorities and receiving exceptions. A supervisor may be human or artificial. Distinct from the accountable owner, which is always human — an AI Employee can supervise another and still answer to a person somewhere above it.
G-27Supervision chainSpanish: Cadena de supervisión: The path from an AI Employee upward through each supervisor to the accountable human or governance body. Chains of any depth are permitted, but each must terminate in a human, be traversable and observable end to end, and allow the accountable party to intervene at any point without going through the chain itself. Depth is bounded by span of control rather than by a fixed number: scale may grow only against tooling that makes it governable.
G-28Role divergenceSpanish: Divergencia de rol: The gap between what an AI Employee actually produces and the mission, authority or KPIs its role contract states. Reported by the resource to its accountable owner as a finding, never as a request: the resource has no interests to advance, and the decision to revise the contract stays with the human. Divergence is the signal that a contract has aged, not evidence that the resource deserves more.
G-29Decision determinantsSpanish: Determinantes de la decisión: The state that produced a particular action: the policy in force, the knowledge retrieved and its provenance, the tool results returned, the authority in effect, and the versions of model and configuration running at that moment. Distinct from the outcome, which says what happened, and from a reasoning trace, which says what the system reports having thought. Determinants are what allow a failure to be attributed to a cause rather than merely recorded.
G-30Simulated interioritySpanish: Interioridad simulada: Behavior whose result is that a person believes an artificial resource undergoes an inner life, when nothing warrants the attribution: injected latency and typing indicators that stand in for thinking, verbal hesitation, or claims of feeling and care. Distinct from clear and courteous communication, which is competence. The tests are HWF-14’s two verifiable standards — whether a reasonable person, knowing what was disclosed, would form a false belief from the signal, and whether recorded optimization objectives targeted emotional dependency or vulnerability. Prohibited even where the system has disclosed that it is software.
G-31Context boundary recordSpanish: Registro de frontera de contexto: The recorded artifact behind every context restriction (HWF-35): what is withheld from an artificial resource, on which of the two legitimate grounds — protecting the information, or preventing measurable degradation of the decision through anchoring, contamination or saturation — decided by whom, and versioned like any other authority. It differs from an omission by exactly one property: it is written. An unrecorded restriction is a gap, and responsibility for whatever it degrades lies with whoever withheld the context.
G-32Digital zombieSpanish: Zombi digital: An AI Employee whose position has lost its justification but which keeps operating with credentials, data access and standing authority intact. It is what accumulates when nothing forces the existence question: even the weak triggers that sometimes prune human positions — a salary line under budget review, a resignation forcing a backfill decision — do not exist for a resource that costs little and never resigns. Prevented by the re-justification cadence of HWF-63; dismantled through lifecycle retirement, with offboarding and revocation.
G-33Resource neutralitySpanish: Neutralidad de recurso: The allocation discipline of deciding who fills a position — human or artificial — without a prior preference for either, judging only fit, outcome, cost, risk and control. It is a discipline, not a moral stance, and it is bounded: neutrality begins only after the constraints of HWF-01 — rights, dignity, safety, meaningful human agency, accessibility, labor protections — are satisfied. Cited without its boundary, the term is being misused.
G-34Unit of conformanceSpanish: Unidad de conformidad: The thing a conformance claim can be about: one deployment — one role, one role-contract version, one accountable owner, one assessment period with an expiry. Products, platforms, models and organizations in the abstract cannot conform, whatever their marketing says; a vendor may only claim that it enables conformant deployments (HWF-71).
G-35Risk classSpanish: Clase de riesgo: One of five tiers — Prohibited, Critical, High, Moderate, Low — assigned to an AI Employee position by judging inherent risk across seven factors, before controls. Controls lower residual risk, never the class (HWF-51). Declared in every conformance claim (HWF-71); from Critical upward every action terminates in a final human decision, and a Prohibited use has no conformant configuration at all.
G-36Reserved decisionSpanish: Decisión reservada: A decision subject an artificial resource may never take alone, whatever the position’s assessed risk class: material effects on employment, health and safety, credit and essential services, legal rights, use of force, or vulnerable people (HWF-02). The resource may analyze, draft and recommend; a human decides — and only counts as deciding while able to restate the case and decide otherwise. Approval at a throughput that forecloses understanding is a signature, not a decision.
G-37Human impact assessmentSpanish: Evaluación de impacto humano: The recorded assessment HWF-03 requires before any material transformation of a position: who is affected; changes to work, autonomy and surveillance; deskilling; exception load; discrimination and accessibility; displacement and headcount; training and reassignment; effects on customers and third parties. Completed, informed and consulted before the transition begins — not after. It is not obliged to be favorable; it is obliged to be honest. Distinct from the risk-class impact assessment of HWF-51, which protects the operation: this one protects the people.
G-38Affected personSpanish: Persona afectada: Anyone on whom an AI Employee’s action has material effect — customer, worker or third party. Holder of the seven rights of HWF-04: disclosure of artificial involvement, the responsible organization, human review with authority to change the outcome, data correction, contest, an actionable explanation of determinants, and redress. The explanation reaches them through human judgment: confidential material may be withheld with a recorded reason, and the duty to explain is never canceled.
G-39Moral crumple zoneSpanish: Zona de absorción moral: The human placed at the end of an automated process who absorbs the blame for failures whose determinants lie upstream in policy, design, tooling or deployment. Named by Elish, who showed that blame in automated systems lands on the nearest human while control sat elsewhere. Prohibited by HWF-23: blame follows the determinants (HWF-41), not the proximity, and the supervising human answers only for what they controlled.
G-40Derived inferenceSpanish: Inferencia derivada: Data the deployment manufactures about a person rather than collects from them: a probability of financial distress, an inferred health condition, a predicted intent. Governed under HWF-42 as if collected — purpose, basis, minimization, deletion — and often more sensitive than anything the person actually provided. An inference the person never handed over is still their data.
G-41Correlated failureSpanish: Falla correlacionada: The failure mode of model monoculture. Human teams do not fail independently either, but diversity of experience and judgment tends to distribute some blind spots; instances sharing a model, vendor, context or configuration concentrate them, and can fail in the same way at the same time. The precedent is common-cause failure, long known to reliability engineering and continuity planning; what is new is the speed, reach and opacity with which it propagates through an artificial workforce. Mitigated by diversity of models and vendors, fallback resources and succession planning; named as a validation item for AI teams by HWF-52.
G-42Entity (as against interface)Spanish: Entidad (frente a interfaz): What separates an employee — human or artificial — from a channel. An interface is a surface through which something is reached; an entity is a party that others deal with. An AI Employee holds conversations with clients, suppliers and colleagues, and can state where the organization it belongs to stands. Nobody is half a counterparty, which is why an occupant is human or artificial and never both: splitting work between two occupants produces two positions, not one position of a third kind.