Top 5 Jobs in Government That Are Most at Risk from AI in Peru - And How to Adapt

Peru's recent move to regulate AI around Law 31814 makes this moment urgent for public servants: a risk-based regime now singles out areas such as educational assessment, hiring and credit scoring as

high-risk,

while mandatory transparency, data-governance and human-oversight rules mean many routine municipal and program-level tasks could be reshaped by automation and compliance requirements (see the OECD summary of Law 31814 AI regulation).

With the Regulations approved by Supreme Decree on 9 September 2025 the state is centralizing oversight through the PCM‑SGTD, so a clerk's or admissions officer's decision-support tool can shift from

helpful

to tightly regulated overnight - creating both legal duties and practical opportunities to streamline services (read the Lexology coverage of Peru AI regulations).

Practical adaptation starts with skills: short, work-focused programs like the 15‑week AI Essentials for Work bootcamp can teach prompt design, risk-aware workflows and human-in-the-loop controls that align day‑to‑day public work with Peru's new rules.

Selection combined a legal-first lens with job-task reality: the starting point was Peru's Law 31814 and its risk-classification (prohibited / high-risk / acceptable) so any role tied to areas like educational assessment, hiring, credit scoring or social programmes was flagged for close scrutiny (see the Nemko overview of Peru's AI Law 31814: Nemko overview of Peru AI Law 31814).

Next, practical filters narrowed the list: whether the role makes rights-affecting automated decisions, handles sensitive personal data, already uses decision-support tools, or falls under mandatory human‑oversight and transparency duties in the newly approved Regulations (published in the Official Gazette on 9 September 2025; see the Lexology summary of Peru AI Regulations published 9 September 2025: Lexology summary of Peru AI Regulations (9 September 2025)).

Jobs that rank at risk therefore combine frequent, routine decision points with high regulatory attention - meaning a single biased score or opaque admissions model could affect large numbers of applicants - so the methodology weighted legal risk, operational prevalence, data sensitivity and the feasibility of automation to pick the top five roles and shape practical adaptation advice.

Municipal administrative clerks - the faces behind permit desks, public registries and meeting minutes - are squarely in AI's path: tools that auto-fill forms, transcribe council sessions, and power site chatbots can shave hours off repetitive work but also shift oversight burdens onto already-stretched staff, who end up fact‑checking machine summaries and correcting "hallucinations" in translations and transcripts (see the CivicPlus primer on AI in local government and the rise of ClerkMinutes-style automation in municipal software).

In practice, AI can speed up routine data entry and handle many common resident queries, yet evidence shows these gains come with new risks - automated replies that misstate laws, black‑box recommendations that nudge human reviewers toward hasty decisions, and translation errors that devalue multilingual clerks' expertise.

Municipalities in Peru that choose to deploy these systems will need clear procurement rules, human‑in‑the‑loop workflows, and staff training so clerks remain the ultimate arbiters of public records rather than urgent clean‑up crews for imperfect automation (for deeper analysis, see the Roosevelt Institute report on AI and government workers).

[F]ailures in AI systems, such as wrongful benefit denials, aren't just inconveniences but can be life-and-death situations for people who rely upon government programs.

Social program eligibility officers - benefits caseworkers - are squarely in the high‑risk lane of Peru's AI regime because automated scores, profiling and decision-support tools touch fundamental rights and large caseloads: Peru's Law 31814 builds a risk‑based framework that insists on human oversight, transparency, data‑governance and proportionality for precisely these applications (see the Nemko overview: Peru AI Law 31814); with the implementing Regulations published in the Official Gazette on 9 September 2025, caseworkers must now expect documented audits, explainability requirements and stricter data‑minimization and consent rules before a tool can be used in benefits decisions (see the Dataguidance: Peru AI regulation published in the Official Gazette).

Practically, that means training to spot biased outputs, clear human‑in‑the‑loop checkpoints so a single opaque score doesn't determine outcomes for large numbers of applicants, and tighter procurement and lifecycle checks so officers remain the final arbiters of eligibility rather than after‑the‑fact correctors for an automated system.

Public HR recruitment officers and civil‑service exam screeners in Peru now sit at the sharp end of the country's AI rulebook: Law 31814 and its implementing Regulations treat employment‑selection and worker‑evaluation tools as high‑risk, which means any automated shortlist, scoring rubric or exam‑grading model must be documented, explainable and kept under meaningful human oversight (Nemko overview of Peru AI regulation (Law 31814)).

Practically, that turns routine screening into a compliance workflow - officers will need access to model rationales, retention of audit trails, stricter consent and data‑minimization steps, and the authority to pause or override outputs rather than simply forwarding algorithmic lists.

The Regulations approved by Supreme Decree also centralize enforcement under the PCM‑SGTD and set phased timelines for public entities (one to three years for full rollout), so HR teams must treat procurement, validation and training as part of everyday hiring - not an IT project left to chance (Lexology summary of Peru AI Regulations and enforcement timelines).

The memorable risk is simple: a single opaque screening score can quietly decide who never gets an interview unless processes are redesigned to keep people, not just algorithms, in charge of public sector hiring.

Educational assessment and admissions officers in public universities and ministry testing units are squarely flagged as high‑risk under Peru's Law 31814 - meaning entrance exams, automated scoring and admissions models will face strict transparency, explainability and human‑in‑the‑loop requirements rather than being treated as mere efficiency tools (see the Nemko overview of Peru's AI regulation).

With the Regulations approved by Supreme Decree on 9 September 2025 and centralized oversight under the PCM‑SGTD, schools must document model rationale, tighten data‑minimization and consent practices, keep robust audit trails, and ensure a trained human can pause or override outputs; without those controls, a single opaque admissions model can quietly reshape access for large numbers of applicants.

Practical adaptation will therefore combine procurement checks, procedural redesign so humans remain the final arbiter, and staged implementation as the public sector adopts the rules (see the Lexology summary of the Regulations and phased timelines).

Public financial assessors - state banks and social‑lending programs - now sit between two realities: Peru's large digitally active population (79.5% internet access) and a formal credit market that reaches only about 22% of people, which is why alternative‑data scoring is spreading fast; platforms that analyse 400+ digital signals from 200+ online sources (Mercado Libre, Rappi, DiDi among them) can unlock faster, real‑time decisions for thin‑file borrowers (see RiskSeal's Peru offering).

Psychometric pilots have also shown promise - scorecards with Gini coefficients of 20–40% and rejected groups whose default probabilities were up to four times higher - so nontraditional inputs can improve reach if rigorously validated (see the IDB study).

But credit scoring is treated as a high‑risk use case under emerging AI rules, meaning data governance, explainability, certification and human oversight aren't optional; without strong audits and human checkpoints, a single opaque score can quietly bar whole communities from credit rather than help them (read more on regulatory implications in Taktile's analysis).

The practical path forward for Peru is straightforward but urgent: map existing tools and data flows, run the mandated risk assessments under Law 31814, and embed human‑in‑the‑loop checkpoints, explainability docs and audit trails before any system touches hiring, admissions, benefits or credit; Peru's risk‑based regime and centralized oversight mean these are not optional steps (see Peru AI regulation overview).

Procurement practices must require lifecycle validation and vendor transparency, training for frontline staff must cover biased outputs and override authority, and rollouts should be phased with strong monitoring under the PCM‑SGTD so a single opaque score can't quietly bar people from services.

For busy public servants and managers, short, work‑focused courses - like the 15‑week AI Essentials for Work - offer practical prompt, governance and human‑oversight skills aligned to these duties (Register for AI Essentials for Work bootcamp).