Package {AIGovernance}

Type: Package
Title: Statistical Auditing and Governance Reporting for Employment AI Systems
Version: 0.1.0
Description: Provides statistical auditing, risk documentation, and reporting tools to support AI governance workflows for employment and hiring decision systems. Implements the EEOC four-fifths adverse impact rule (Equal Employment Opportunity Commission, 1978, https://www.ecfr.gov/current/title-29/subtitle-B/chapter-XIV/part-1607), NYC Local Law 144 bias audit requirements (New York City, 2023, https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page), and the AI Risk Management Framework checklist items from the National Institute of Standards and Technology (2023, <doi:10.6028/NIST.AI.100-1>). Optionally supports EU AI Act high-risk classification (European Parliament and Council, 2024, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689). The package does not provide legal advice or certify legal compliance; it is a statistical and documentation support tool.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
Depends: R (≥ 4.1.0)
Imports: cli, rlang, stats, tibble, dplyr
Suggests: ggplot2, knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Language: en-US
URL: https://github.com/causalfragility-lab/AIGovernance
BugReports: https://github.com/causalfragility-lab/AIGovernance/issues
NeedsCompilation: no
Packaged: 2026-05-19 14:37:57 UTC; Subir
Author: Subir Hait ORCID iD [aut, cre]
Maintainer: Subir Hait <haitsubi@msu.edu>
Repository: CRAN
Date/Publication: 2026-05-27 09:50:07 UTC

EEOC Adverse Impact Analysis (4/5ths Rule)

Description

Computes selection rates by group and applies the EEOC Uniform Guidelines 4/5ths (80%) rule to assess adverse impact in employment selection procedures (EEOC, 1978).

The Adverse Impact Ratio (AIR) for each group is:

AIR_g = \frac{\text{selection rate}_g}{\text{selection rate}_{\text{ref}}}

A group is flagged for adverse impact when AIR < 0.80. The function also reports the two-proportion Z-test and Fisher's exact test p-values as supplementary statistics.

Note: The 4/5ths rule is a rule of thumb, not a bright-line legal standard. Small sample sizes reduce reliability. This function does not provide legal advice.

Usage

aigov_adverse_impact(gov, min_n = 30L)

Arguments

gov

An aigov object from aigov_build.

min_n

Integer. Minimum group sample size for a reliable AIR estimate. Groups below this threshold are flagged with a warning. Default 30.

Value

The input gov object with gov$results$adverse_impact appended, a tibble containing:

group

Group label.

n

Total applicants in group.

n_selected

Number selected.

selection_rate

Proportion selected.

AIR

Adverse Impact Ratio vs reference group.

fourfifths_flag

Logical: TRUE if AIR < 0.80.

z_stat

Two-proportion Z statistic.

p_value

Two-sided p-value from Z test.

fisher_p

Fisher's exact test p-value.

small_n_flag

Logical: TRUE if n < min_n.

References

Equal Employment Opportunity Commission (1978). Uniform guidelines on employee selection procedures. Federal Register, 43(166), 38295–38309.

Examples

data(hiring_sim)
gov <- aigov_build(hiring_sim, selected, race_ethnicity, ref_group = "White")
gov <- aigov_adverse_impact(gov)
gov$results$adverse_impact

NIST AI Risk Management Framework (AI RMF 1.0) Audit

Description

Implements a structured checklist audit aligned with the NIST AI Risk Management Framework (AI RMF 1.0, NIST, 2023). The RMF organises AI risk management into four core functions: GOVERN, MAP, MEASURE, and MANAGE.

This function presents the checklist items most relevant to employment AI systems and records user-supplied responses (or defaults to NA for items that cannot be verified from data alone).

Usage

aigov_audit_nist(gov, responses = list())

Arguments

gov

An aigov object from aigov_build.

responses

An optional named list of logical values (TRUE/ FALSE) for checklist items. Use aigov_checklist(gov, "NIST_RMF") to see item names. Items not supplied default to NA.

Value

The input gov object with gov$results$nist_rmf appended, containing:

checklist

Named list with TRUE/FALSE/NA per item.

scores

Per-function completion scores (proportion of confirmed items).

overall_score

Overall confirmed proportion.

verdict

One of "GREEN" (>= 0.75), "AMBER" (0.50–0.74), or "RED" (< 0.50).

References

National Institute of Standards and Technology (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1. doi:10.6028/NIST.AI.100-1

Examples

data(hiring_sim)
gov <- aigov_build(hiring_sim, selected, race_ethnicity, ref_group = "White")
gov <- aigov_audit_nist(gov, responses = list(
  GOVERN_1_1 = TRUE,
  GOVERN_1_2 = TRUE,
  MAP_1_1    = TRUE
))

NYC Local Law 144 Bias Audit Module

Description

Implements the statistical components of a NYC Local Law 144 (2023) bias audit for Automated Employment Decision Tools (AEDTs). The law requires employers using AEDTs to:

  1. Conduct an annual independent bias audit.

  2. Publish a summary of audit results on their website.

  3. Provide advance notice to candidates/employees.

This function computes the required impact ratio statistics for each race/ethnicity and sex category and generates a publication-ready summary table matching the format expected in public disclosures.

The NYC LL144 impact ratio is:

IR_g = \frac{\text{selection rate}_g}{\text{selection rate of most-selected category}}

Note: LL144 uses the most-selected category (not a user-specified reference group) as the denominator – this differs from the EEOC approach. This function implements both.

Usage

aigov_audit_nyc(gov, use_most_selected = TRUE)

Arguments

gov

An aigov object from aigov_build.

use_most_selected

Logical. If TRUE (default), the denominator is the group with the highest selection rate (NYC LL144 standard). If FALSE, uses gov$ref_group (EEOC standard).

Value

The input gov object with gov$results$nyc_ll144 appended, containing:

impact_table

Tibble with selection rates and impact ratios.

most_selected_group

The reference category used.

disclosure_table

Formatted table for public disclosure.

checklist

LL144 procedural checklist (named logical vector).

verdict

Statistical verdict: "PASS" or "REVIEW".

References

New York City Local Law 144 of 2021 (effective January 1, 2023). NYC Department of Consumer and Worker Protection (DCWP). https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page

Examples

data(hiring_sim)
gov <- aigov_build(hiring_sim, selected, race_ethnicity, ref_group = "White",
                  frameworks = c("NYC_LL144"))
gov <- aigov_audit_nyc(gov)

Build an AIGovernance audit object

Description

Constructs an aigov object from employment decision data. This is the entry point for all auditing, classification, and reporting functions in the AIGovernance package.

Disclaimer: AIGovernance provides statistical and documentation support tools only. It does not provide legal advice and does not certify compliance with any law or regulation.

Usage

aigov_build(
  data,
  outcome,
  group,
  ref_group,
  frameworks = c("EEOC", "NYC_LL144", "NIST_RMF"),
  org_name = NULL,
  system_name = NULL,
  audit_date = NULL
)

Arguments

data

A data frame containing the employment decision records.

outcome

Unquoted column name of the binary decision variable (1 = selected / hired / advanced; 0 = not selected).

group

Unquoted column name of the protected-class variable (e.g., race/ethnicity, gender).

ref_group

Character string identifying the reference group (typically the highest-selection-rate group, e.g. "White" or "Male").

frameworks

Character vector of governance frameworks to activate. One or more of "EEOC", "NYC_LL144", "NIST_RMF", "EU_AI_Act". Default is c("EEOC", "NYC_LL144", "NIST_RMF").

org_name

Optional character string — organisation name for reports.

system_name

Optional character string — name of the AI system being audited (e.g., "Resume screening tool v2.1").

audit_date

Optional Date or character string (ISO format). Defaults to Sys.Date().

Value

An object of class "aigov" containing:

data

The input data frame.

outcome

Name of the outcome column (character).

group

Name of the group column (character).

ref_group

Reference group label.

frameworks

Active frameworks.

org_name

Organisation name.

system_name

AI system name.

audit_date

Audit date.

group_levels

All observed group labels.

n_total

Total number of records.

Examples

data(hiring_sim)
gov <- aigov_build(
  data        = hiring_sim,
  outcome     = selected,
  group       = race_ethnicity,
  ref_group   = "White",
  frameworks  = c("EEOC", "NYC_LL144", "NIST_RMF"),
  org_name    = "Acme Corp",
  system_name = "Resume Screening Tool v1.0"
)
print(gov)

Display Governance Checklist for a Framework

Description

Returns all checklist item names and descriptions for a given framework. Use the returned item names as keys in the responses argument of aigov_audit_nist.

Usage

aigov_checklist(gov, framework)

Arguments

gov

An aigov object (used only for class checking).

framework

Character. One of "NYC_LL144", "EEOC", "NIST_RMF", or "EU_AI_Act".

Value

A tibble with columns item_id, function_area, and description.

Examples

data(hiring_sim)
gov <- aigov_build(hiring_sim, selected, race_ethnicity, ref_group = "White")
aigov_checklist(gov, "NYC_LL144")
aigov_checklist(gov, "NIST_RMF")

Classify AI System Risk Level

Description

Classifies the AI system under two frameworks:

  1. EU AI Act (2024) – Assigns one of four risk tiers: Unacceptable, High, Limited, or Minimal risk. Employment/worker-management AI is listed in Annex III as High Risk.

  2. NIST AI RMF – Assigns a risk tier (1–4) based on impact on individuals' rights and opportunities.

Usage

aigov_classify(
  gov,
  domain = "employment",
  makes_final_decision = TRUE,
  human_oversight = NA
)

Arguments

gov

An aigov object from aigov_build.

domain

Character. Application domain. One of "employment", "education", "credit", "housing", "healthcare", "law_enforcement", "other". Default "employment".

makes_final_decision

Logical. Does the AI system make or substantially influence a final employment decision? Default TRUE.

human_oversight

Logical. Is meaningful human review in place before the AI decision takes effect? Default NA (unknown).

Value

The input gov object with gov$results$risk_class appended, containing EU AI Act and NIST risk classifications with explanatory text.

References

European Parliament and Council (2024). Regulation (EU) 2024/1689 (EU AI Act). https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

NIST (2023). AI RMF 1.0. doi:10.6028/NIST.AI.100-1

Examples

data(hiring_sim)
gov <- aigov_build(hiring_sim, selected, race_ethnicity, ref_group = "White")
gov <- aigov_classify(gov, domain = "employment",
                       makes_final_decision = TRUE,
                       human_oversight = FALSE)

Generate an AI Governance Audit Report

Description

Produces a self-contained HTML (or plain-text) governance audit report from a completed aigov object. The report is suitable for internal documentation, legal review, or public disclosure (e.g., NYC LL144 website posting requirement).

Usage

aigov_report(gov, format = "html", output_file = NULL, open = TRUE)

Arguments

gov

A completed aigov object (at least one audit module run).

format

Character. Output format: "html" (default) or "text".

output_file

Optional character. File path for the output. If NULL, a temporary file is used and opened in the browser (HTML) or printed to console (text).

open

Logical. If TRUE (default) and format = "html", attempt to open the report in the default browser.

Value

Invisibly returns the path to the generated file.

Examples


data(hiring_sim)
gov <- aigov_build(hiring_sim, selected, race_ethnicity, ref_group = "White")
gov <- aigov_adverse_impact(gov)
gov <- aigov_audit_nyc(gov)
gov <- aigov_audit_nist(gov)
aigov_report(gov, format = "html")

Determine Applicable Governance Frameworks

Description

Returns a summary of which governance frameworks apply to the AI system described in the aigov object, based on domain and jurisdiction.

Usage

aigov_scope(gov, domain = "employment", us_state = NULL)

Arguments

gov

An aigov object from aigov_build.

domain

Character. Application domain (default "employment").

us_state

Optional character. US state, e.g. "NY", "CO", "IL" for state-specific law flags.

Value

A tibble with columns framework, applies, jurisdiction, and note.

Examples

data(hiring_sim)
gov <- aigov_build(hiring_sim, selected, race_ethnicity, ref_group = "White")
aigov_scope(gov, domain = "employment", us_state = "NY")

Simulated Employment Screening Dataset

Description

A synthetic dataset of 500 job applicants processed by a hypothetical automated resume-screening tool. Generated for illustrative and testing purposes only. All individuals are fictional.

Usage

hiring_sim

Format

A data frame with 500 rows and 6 variables:

applicant_id

Integer applicant identifier.

race_ethnicity

Character. One of "White", "Black", "Hispanic", "Asian", "Other".

gender

Character. "Male" or "Female".

years_experience

Numeric. Years of relevant experience.

score

Numeric. AI screening score (0–100).

selected

Integer (0/1). Whether the applicant was advanced to the next stage (1 = selected, 0 = not selected).

Details

Selection probabilities were set to produce a realistic adverse impact pattern across race/ethnicity groups, consistent with published empirical ranges. The data are purely synthetic and do not represent any real organisation or hiring process.

Source

Simulated by the package authors for illustration purposes.

Examples

data(hiring_sim)
head(hiring_sim)
table(hiring_sim$race_ethnicity, hiring_sim$selected)