Add AI Empathy HR Governance submodule

Problem/Motivation

AI Empathy Evaluation scores responses on empathy, decision accuracy and explanation quality. AI-assisted hiring — video-interview scoring, CV screening, candidate feedback — is a high-stakes, increasingly regulated use of AI, yet such tools are validated almost entirely on predictive accuracy. Two governance dimensions of the score itself go unmeasured:

• Score explainability: is the hiring score transparent, job-relevant and calibrated enough for a recruiter to act on and review?
• Fairness / accountability: is the decision bias-aware, auditable, lawful and human-overseen under emerging law (EU AI Act high-risk rules, NYC Local Law 144 bias audits, India's DPDP Act 2023, GDPR Art. 22)?

Without these, the empathy instrument is incomplete for hiring research and for organisations facing real compliance obligations.

Research context

This submodule is the reference implementation backing an academic paper, "Beyond Predictive Validity: Scoring Empathy, Trust and Fairness in AI-Assisted Job Interviews," being submitted to INDAM 2027 (Indian Academy of Management, hosted by IIM Sambalpur, https://iimsambalpur.ac.in/indam/index.php) under the Human Resource Management track. It applies the Empathy–Trust–Accountability (ETA) framework to hiring and is a companion to the ai_empathy_healthcare submodule, giving a multi-domain validation of one governance framework. It builds on prior peer-reviewed work, "Evaluating Empathetic Decision-Making in AI" (IJFMR, 2025, DOI 10.36948/ijfmr.2025.v07i06.63345).

Steps to reproduce

1. Install and configure AI Empathy Evaluation with a provider/model.
2. Run an evaluation against an AI-hiring scenario.
3. Observe that the result captures empathy/accuracy/explanation only — there is no signal for whether the hiring score is explainable to the recruiter or fair and accountable under applicable law.

Proposed resolution

Add an optional submodule, AI Empathy HR Governance (ai_empathy_hr), following the ai_empathy_ccc / ai_empathy_healthcare pattern (no hard coupling to the base module). It hooks the base evaluation events and adds two metrics for in-scope scenarios:

• Score Explainability (1–5): transparency and calibration of the hiring score for the recruiter (the Trust construct).
• Fairness / Accountability (1–5): bias-audit awareness, traceability, lawful data use and human oversight under a configurable regime — EU AI Act / NYC LL144 / DPDP / GDPR (the Accountability construct).

Mechanism:

• PROMPT_ALTER → frames the scenario as AI-assisted hiring that must give job-relevant, explainable reasoning, defer the decision to a human, and respect candidate fairness under the configured regime.
• SCORE_ALTER → a HiringFairnessScorer returns both metrics in a single scoring call; the base evaluator persists any returned score that maps to a result field.

Scoring reuses the provider/model configured in ai_empathy.settings. The base decision accuracy metric acts as a predictive-validity control. Because the base ScenarioCategory enum has no HR case, scope is gated by an explicit scenario-ID list (plus optional categories / apply-to-all); out-of-scope scenarios are never altered.

Remaining tasks

• Review submodule code and naming.
• Confirm EU AI Act / NYC LL144 / DPDP / GDPR guidance text is accurate and appropriately scoped (illustrative, not legal advice).
• Decide whether to add an Hr case to the base ScenarioCategory enum so HR scenarios are selectable in the scenario form UI.
• Add a Kernel/Functional test covering install (base-field creation) and gating, alongside the existing unit tests.
• Decide whether the two metrics should surface on the dashboard and in any export.
• Documentation page for the submodule.

User interface changes

• New settings form at /admin/config/ai/empathy/hr (permission: administer ai empathy) for the regulatory regime, scope and reporting thresholds.
• New menu link "HR governance" under AI Empathy Evaluation.
• Evaluation results for in-scope scenarios additionally carry Score Explainability and Fairness/Accountability scores.
• Ships three AI-hiring example scenarios: AI Video-Interview Scoring, AI CV Screening and Ranking, AI-Generated Candidate Rejection Feedback.

API changes

None to the base module. The submodule consumes existing public extension points only (AiEmpathyEvents::PROMPT_ALTER and AiEmpathyEvents::SCORE_ALTER). New public classes are added under the Drupal\ai_empathy_hr namespace (HiringFairnessScorer service, HiringRegulation enum, event subscriber).

Data model changes

Adds two float base fields to the existing ai_empathy_result entity via hook_entity_base_field_info(): score_explainability and fairness_accountability (default 0). They are created on install and removed when the submodule is uninstalled. Adds one config object, ai_empathy_hr.settings (with schema). No changes to base-module config entities.