R Under development (unstable) (2026-11-10 r90874 ucrt) -- "Unsuffered Consequences"
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> library(testthat)
> library(AIGovernance)
> 
> test_check("AIGovernance")
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- EEOC Adverse Impact (4/5ths Rule) --

# A tibble: 5 x 8
  group        n n_selected selection_rate   AIR fourfifths_flag   p_value
  <chr>    <int>      <int>          <dbl> <dbl> <lgl>               <dbl>
1 Asian       60         21          0.35  0.739 TRUE             0.0857  
2 Black      104         29          0.279 0.588 TRUE             0.000801
3 Hispanic    86         23          0.267 0.564 TRUE             0.000926
4 Other       20          5          0.25  0.528 TRUE             0.0538  
5 White      230        109          0.474 1     FALSE           NA       
# i 1 more variable: small_n_flag <lgl>
x Result: FAIL — 4 group(s) flagged (AIR < 0.80).
! Small sample warning: groups "Other" have n < 30. 
i Disclaimer: The 4/5ths rule is a statistical rule of thumb. 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- EEOC Adverse Impact (4/5ths Rule) --

# A tibble: 5 x 8
  group        n n_selected selection_rate   AIR fourfifths_flag   p_value
  <chr>    <int>      <int>          <dbl> <dbl> <lgl>               <dbl>
1 Asian       60         21          0.35  0.739 TRUE             0.0857  
2 Black      104         29          0.279 0.588 TRUE             0.000801
3 Hispanic    86         23          0.267 0.564 TRUE             0.000926
4 Other       20          5          0.25  0.528 TRUE             0.0538  
5 White      230        109          0.474 1     FALSE           NA       
# i 1 more variable: small_n_flag <lgl>
x Result: FAIL — 4 group(s) flagged (AIR < 0.80).
! Small sample warning: groups "Other" have n < 30. 
i Disclaimer: The 4/5ths rule is a statistical rule of thumb. 
v AIGovernance object built: 200 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- EEOC Adverse Impact (4/5ths Rule) --

# A tibble: 4 x 8
  group     n n_selected selection_rate   AIR fourfifths_flag p_value
  <chr> <int>      <dbl>          <dbl> <dbl> <lgl>             <dbl>
1 A        50         25            0.5     1 FALSE                NA
2 B        50         25            0.5     1 FALSE                 1
3 C        50         25            0.5     1 FALSE                 1
4 D        50         25            0.5     1 FALSE                 1
# i 1 more variable: small_n_flag <lgl>
v Result: PASS — No group flagged for adverse impact (AIR >= 0.80 for all groups).
i Disclaimer: The 4/5ths rule is a statistical rule of thumb. 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- EEOC Adverse Impact (4/5ths Rule) --

# A tibble: 5 x 8
  group        n n_selected selection_rate   AIR fourfifths_flag   p_value
  <chr>    <int>      <int>          <dbl> <dbl> <lgl>               <dbl>
1 Asian       60         21          0.35  0.739 TRUE             0.0857  
2 Black      104         29          0.279 0.588 TRUE             0.000801
3 Hispanic    86         23          0.267 0.564 TRUE             0.000926
4 Other       20          5          0.25  0.528 TRUE             0.0538  
5 White      230        109          0.474 1     FALSE           NA       
# i 1 more variable: small_n_flag <lgl>
x Result: FAIL — 4 group(s) flagged (AIR < 0.80).
! Small sample warning: groups "Asian", "Black", "Hispanic", and "Other" have n < 200. 
i Disclaimer: The 4/5ths rule is a statistical rule of thumb. 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 
v Report written to: 'D:\temp\2026_11_11_23_40_16_6951\Rtmp23WfyI\file247ca04c177cba.html'
# A tibble: 4 x 3
  Function Score Status
  <chr>    <dbl> <chr> 
1 GOVERN    0    RED   
2 MAP       0    RED   
3 MEASURE   0.12 RED   
4 MANAGE    0    RED   
# A tibble: 4 x 3
  Function Score Status
  <chr>    <dbl> <chr> 
1 GOVERN       0 RED   
2 MAP          0 RED   
3 MEASURE      0 RED   
4 MANAGE       0 RED   
# A tibble: 9 x 4
  item_id function_area description                                       status
  <chr>   <chr>         <chr>                                             <chr> 
1 EU_9    Art. 9        Art. 9  — Risk management system is established ~ Pendi~
2 EU_10   Art. 10       Art. 10 — Training data governance and bias exam~ Pendi~
3 EU_11   Art. 11       Art. 11 — Technical documentation prepared befor~ Pendi~
4 EU_13   Art. 13       Art. 13 — Transparency information provided to d~ Pendi~
5 EU_14   Art. 14       Art. 14 — Human oversight measures are implement~ Pendi~
6 EU_15   Art. 15       Art. 15 — Accuracy, robustness, and cybersecurit~ Pendi~
7 EU_43   Art. 43       Art. 43 — Conformity assessment completed (self-~ Pendi~
8 EU_49   Art. 49       Art. 49 — System registered in EU AI database     Pendi~
9 EU_72   Art. 72       Art. 72 — Post-market monitoring system is estab~ Evide~
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- NYC Local Law 144 — Bias Audit Results --

* Reference category (most selected): "White"
* Reference selection rate: 0.4739
# A tibble: 5 x 6
  Category `Total Assessed` `Number Selected` `Selection Rate` `Impact Ratio`
  <chr>               <int>             <int>            <dbl>          <dbl>
1 Asian                  60                21            0.35           0.738
2 Black                 104                29            0.279          0.588
3 Hispanic               86                23            0.267          0.564
4 Other                  20                 5            0.25           0.528
5 White                 230               109            0.474          1    
# i 1 more variable: `Flagged (IR < 0.80)` <lgl>
x Statistical result: REVIEW — 4 category(ies) with IR < 0.80.

-- Procedural Checklist (NA = requires employer confirmation) 
  ✔ Annual bias audit conducted
  ? Audit performed prior to AEDT use or within 1 year
  ✔ Impact ratios computed for all categories
  ? Audit results published on employer website
  ? Publication includes audit date
  ? Publication includes distribution date of AEDT
  ? Candidate/employee notice provided (10 days prior)
  ? Notice includes AEDT purpose and data collected
  ? Accommodation process documented for candidates
  ? Audit conducted by independent auditor
i Disclaimer: This output is a statistical support tool. 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- NYC Local Law 144 — Bias Audit Results --

* Reference category (most selected): "White"
* Reference selection rate: 0.4739
# A tibble: 5 x 6
  Category `Total Assessed` `Number Selected` `Selection Rate` `Impact Ratio`
  <chr>               <int>             <int>            <dbl>          <dbl>
1 Asian                  60                21            0.35           0.738
2 Black                 104                29            0.279          0.588
3 Hispanic               86                23            0.267          0.564
4 Other                  20                 5            0.25           0.528
5 White                 230               109            0.474          1    
# i 1 more variable: `Flagged (IR < 0.80)` <lgl>
x Statistical result: REVIEW — 4 category(ies) with IR < 0.80.

-- Procedural Checklist (NA = requires employer confirmation) 
  ✔ Annual bias audit conducted
  ? Audit performed prior to AEDT use or within 1 year
  ✔ Impact ratios computed for all categories
  ? Audit results published on employer website
  ? Publication includes audit date
  ? Publication includes distribution date of AEDT
  ? Candidate/employee notice provided (10 days prior)
  ? Notice includes AEDT purpose and data collected
  ? Accommodation process documented for candidates
  ? Audit conducted by independent auditor
i Disclaimer: This output is a statistical support tool. 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- NYC Local Law 144 — Bias Audit Results --

* Reference category (most selected): "White"
* Reference selection rate: 0.4739
# A tibble: 5 x 6
  Category `Total Assessed` `Number Selected` `Selection Rate` `Impact Ratio`
  <chr>               <int>             <int>            <dbl>          <dbl>
1 Asian                  60                21            0.35           0.738
2 Black                 104                29            0.279          0.588
3 Hispanic               86                23            0.267          0.564
4 Other                  20                 5            0.25           0.528
5 White                 230               109            0.474          1    
# i 1 more variable: `Flagged (IR < 0.80)` <lgl>
x Statistical result: REVIEW — 4 category(ies) with IR < 0.80.

-- Procedural Checklist (NA = requires employer confirmation) 
  ✔ Annual bias audit conducted
  ? Audit performed prior to AEDT use or within 1 year
  ✔ Impact ratios computed for all categories
  ? Audit results published on employer website
  ? Publication includes audit date
  ? Publication includes distribution date of AEDT
  ? Candidate/employee notice provided (10 days prior)
  ? Notice includes AEDT purpose and data collected
  ? Accommodation process documented for candidates
  ? Audit conducted by independent auditor
i Disclaimer: This output is a statistical support tool. 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- NIST AI RMF 1.0 — Employment AI Audit --

# A tibble: 4 x 3
  Function Score Status
  <chr>    <dbl> <chr> 
1 GOVERN       0 RED   
2 MAP          0 RED   
3 MEASURE      0 RED   
4 MANAGE       0 RED   
* Overall score: 0 — RED
x NIST RMF: RED — Significant governance gaps identified.
i Supply `responses` list to `aigov_audit_nist()` to record 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- EEOC Adverse Impact (4/5ths Rule) --

# A tibble: 5 x 8
  group        n n_selected selection_rate   AIR fourfifths_flag   p_value
  <chr>    <int>      <int>          <dbl> <dbl> <lgl>               <dbl>
1 Asian       60         21          0.35  0.739 TRUE             0.0857  
2 Black      104         29          0.279 0.588 TRUE             0.000801
3 Hispanic    86         23          0.267 0.564 TRUE             0.000926
4 Other       20          5          0.25  0.528 TRUE             0.0538  
5 White      230        109          0.474 1     FALSE           NA       
# i 1 more variable: small_n_flag <lgl>
x Result: FAIL — 4 group(s) flagged (AIR < 0.80).
! Small sample warning: groups "Other" have n < 30. 
i Disclaimer: The 4/5ths rule is a statistical rule of thumb. 

-- NIST AI RMF 1.0 — Employment AI Audit --

# A tibble: 4 x 3
  Function Score Status
  <chr>    <dbl> <chr> 
1 GOVERN    0    RED   
2 MAP       0    RED   
3 MEASURE   0.12 RED   
4 MANAGE    0    RED   
* Overall score: 0.03 — RED
x NIST RMF: RED — Significant governance gaps identified.
i Supply `responses` list to `aigov_audit_nist()` to record 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- NIST AI RMF 1.0 — Employment AI Audit --

# A tibble: 4 x 3
  Function Score Status
  <chr>    <dbl> <chr> 
1 GOVERN    0.14 RED   
2 MAP       0    RED   
3 MEASURE   0    RED   
4 MANAGE    0    RED   
* Overall score: 0.04 — RED
x NIST RMF: RED — Significant governance gaps identified.
i Supply `responses` list to `aigov_audit_nist()` to record 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- AI System Risk Classification --

* Domain : employment
* Makes final decision : TRUE

-- EU AI Act 
* Risk tier : HIGH RISK
* Annex basis : Annex III, Point 4 — Employment, workers management, and access
  to self-employment
i Key obligations:
  Art. 9 — Risk management system (documented, ongoing)
  Art. 10 — Data governance and training data requirements
  Art. 11 — Technical documentation (before market placement)
  Art. 13 — Transparency and provision of information to deployers
  Art. 14 — Human oversight measures
  Art. 15 — Accuracy, robustness, and cybersecurity
  Art. 43 — Conformity assessment
  Art. 49 — Registration in EU AI database
  Art. 72 — Post-market monitoring system (providers)

-- NIST AI RMF 
* Tier 3 — Significant impact on individuals' rights and opportunities
i Human oversight: UNKNOWN — requires clarification.
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 

-- Checklist: NYC_LL144 --

# A tibble: 10 x 4
   item_id  function_area     description                                 status
   <chr>    <chr>             <chr>                                       <chr> 
 1 LL144_1  NYC Local Law 144 Annual bias audit has been conducted        Pendi~
 2 LL144_2  NYC Local Law 144 Audit was performed before AEDT use or wit~ Pendi~
 3 LL144_3  NYC Local Law 144 Impact ratios computed for each race/ethni~ Pendi~
 4 LL144_4  NYC Local Law 144 Impact ratios computed for each sex catego~ Pendi~
 5 LL144_5  NYC Local Law 144 Audit results published on employer website Pendi~
 6 LL144_6  NYC Local Law 144 Publication includes the date of the audit  Pendi~
 7 LL144_7  NYC Local Law 144 Publication includes the distribution date~ Pendi~
 8 LL144_8  NYC Local Law 144 Candidates/employees notified at least 10 ~ Pendi~
 9 LL144_9  NYC Local Law 144 Notice describes AEDT type and data/criter~ Pendi~
10 LL144_10 NYC Local Law 144 Audit was conducted by an independent audi~ Pendi~
-- Checklist: EEOC --

# A tibble: 8 x 4
  item_id function_area           description                             status
  <chr>   <chr>                   <chr>                                   <chr> 
1 EEOC_1  EEOC Uniform Guidelines Selection rates computed for all race/~ Pendi~
2 EEOC_2  EEOC Uniform Guidelines Selection rates computed for gender gr~ Pendi~
3 EEOC_3  EEOC Uniform Guidelines Adverse Impact Ratio (AIR) calculated ~ Pendi~
4 EEOC_4  EEOC Uniform Guidelines 4/5ths (80%) rule applied to all prote~ Pendi~
5 EEOC_5  EEOC Uniform Guidelines Sample sizes sufficient for reliable A~ Pendi~
6 EEOC_6  EEOC Uniform Guidelines Differential validity analysis conduct~ Pendi~
7 EEOC_7  EEOC Uniform Guidelines Selection procedure validated for job-~ Pendi~
8 EEOC_8  EEOC Uniform Guidelines Records retained per EEOC recordkeepin~ Pendi~
-- Checklist: NIST_RMF --

# A tibble: 28 x 4
   item_id     function_area description                                  status
   <chr>       <chr>         <chr>                                        <chr> 
 1 GOVERN_1_1  GOVERN        Organisational AI risk policies are establi~ Pendi~
 2 GOVERN_1_2  GOVERN        Roles and responsibilities for AI risk mana~ Pendi~
 3 GOVERN_1_3  GOVERN        Senior leadership oversight of AI systems i~ Pendi~
 4 GOVERN_2_1  GOVERN        Internal accountability mechanisms exist fo~ Pendi~
 5 GOVERN_2_2  GOVERN        Processes exist for escalating AI concerns ~ Pendi~
 6 GOVERN_3_1  GOVERN        AI system lifecycle is documented            Pendi~
 7 GOVERN_4_1  GOVERN        Diversity, equity, and inclusion are consid~ Pendi~
 8 MAP_1_1     MAP           AI system context and purpose are documented Pendi~
 9 MAP_1_2     MAP           Intended users and affected populations are~ Pendi~
10 MAP_1_3     MAP           Legal and regulatory requirements are ident~ Pendi~
11 MAP_2_1     MAP           Potential harms to individuals from AI deci~ Pendi~
12 MAP_2_2     MAP           Protected-group impacts are assessed prior ~ Pendi~
13 MAP_3_1     MAP           Third-party or vendor AI components are inv~ Pendi~
14 MAP_5_1     MAP           Residual risks after mitigation are acknowl~ Pendi~
15 MEASURE_1_1 MEASURE       Bias and fairness metrics are defined for t~ Pendi~
16 MEASURE_1_2 MEASURE       Adverse impact analysis has been conducted   Pendi~
17 MEASURE_1_3 MEASURE       Model performance is evaluated across demog~ Pendi~
18 MEASURE_2_1 MEASURE       Evaluation methods are appropriate and docu~ Pendi~
19 MEASURE_2_2 MEASURE       Uncertainty in AI outputs is communicated    Pendi~
20 MEASURE_2_3 MEASURE       AI system is tested with representative dep~ Pendi~
21 MEASURE_3_1 MEASURE       Ongoing monitoring plan for bias drift is e~ Pendi~
22 MEASURE_4_1 MEASURE       Feedback mechanisms exist for affected indi~ Pendi~
23 MANAGE_1_1  MANAGE        Risk response plans are documented           Pendi~
24 MANAGE_1_2  MANAGE        Identified bias risks have defined mitigati~ Pendi~
25 MANAGE_2_1  MANAGE        Human review process is in place for high-s~ Pendi~
26 MANAGE_2_2  MANAGE        Override or appeal mechanism exists for aff~ Pendi~
27 MANAGE_3_1  MANAGE        AI incident response plan is documented      Pendi~
28 MANAGE_4_1  MANAGE        Post-deployment monitoring results are revi~ Pendi~
-- Checklist: EU_AI_Act --

# A tibble: 9 x 4
  item_id function_area description                                       status
  <chr>   <chr>         <chr>                                             <chr> 
1 EU_9    Art. 9        Art. 9  — Risk management system is established ~ Pendi~
2 EU_10   Art. 10       Art. 10 — Training data governance and bias exam~ Pendi~
3 EU_11   Art. 11       Art. 11 — Technical documentation prepared befor~ Pendi~
4 EU_13   Art. 13       Art. 13 — Transparency information provided to d~ Pendi~
5 EU_14   Art. 14       Art. 14 — Human oversight measures are implement~ Pendi~
6 EU_15   Art. 15       Art. 15 — Accuracy, robustness, and cybersecurit~ Pendi~
7 EU_43   Art. 43       Art. 43 — Conformity assessment completed (self-~ Pendi~
8 EU_49   Art. 49       Art. 49 — System registered in EU AI database     Pendi~
9 EU_72   Art. 72       Art. 72 — Post-market monitoring system is estab~ Pendi~
v AIGovernance object built: 500 records, 
i Disclaimer: AIGovernance is a statistical support tool. 
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 85 ]
> 
> proc.time()
   user  system elapsed 
  11.60    1.98   13.57