Evidence

Evidence Registry

“What supports this claim?”

Every empirical claim on this site is a ledger entry mapped to sources in this registry. The registry itself is a synced snapshot of the PAN reference library's master list — 230 academic references and 78 grounding sources, deduplicated, provenance-tagged, and keyed.

Snapshot provenance: synced from the PAN reference library at commit c5fd0e2 on 2026-07-04. Sources enter the registry only through the PAN library's bibliography build (documents, curated source lists, and per-component grounding blocks) followed by a provenance-trimmed re-sync — never by hand-editing this site.

The claim ledger

What this site claims, and on what basis: 20 ledgered claims (20 cited, 0 pending citation). Claim types are labeled per the legend below.

EmpiricalMichigan's MiDAS system auto-adjudicated unemployment-insurance fraud with an extremely high error rate among automated …

Michigan's MiDAS system auto-adjudicated unemployment-insurance fraud with an extremely high error rate among automated determinations, wrongly accusing tens of thousands of people; litigation and court action forced review and compensation.

Sources: michiganag2022, ieeespectruma, aiincidentdatabase, benefitstechadvocacyhubb

Appears on: /domains/cases/michigan-midas

EmpiricalThe Royal Commission into the Robodebt Scheme documented hundreds of thousands of wrongful debts raised by an unlawful i…

The Royal Commission into the Robodebt Scheme documented hundreds of thousands of wrongful debts raised by an unlawful income-averaging method, with the onus placed on recipients to disprove automated assessments.

Sources: royalcommissionintotherobode, royalcommissionintotherobode2023b, royalcommissionintotherobode2023a, lawsocietyjournal

Appears on: /domains/cases/australia-robodebt

EmpiricalIndiana's privatized eligibility modernization produced over a million denials in its early years — many procedural rath…

Indiana's privatized eligibility modernization produced over a million denials in its early years — many procedural rather than substantive — before the state canceled the contract and litigated with its vendor.

Sources: eubanks2018b, governmenttechnologyb, ieeespectrumb

Appears on: /domains/cases/indiana-ibm-eligibility

EmpiricalAn independent audit of Rotterdam's welfare-fraud risk model documented scores skewed against already-vulnerable groups,…

An independent audit of Rotterdam's welfare-fraud risk model documented scores skewed against already-vulnerable groups, and the city suspended the system's use following the scrutiny.

Sources: lighthousereports2023, wiredlighthousereports2023, followthemoney, racismandtechnologycenter2023

Appears on: /domains/cases/rotterdam-welfare-fraud, /pan-lab

EmpiricalA large share of Arkansas home-care recipients had care hours cut when algorithmic assessment replaced nurse judgment, a…

A large share of Arkansas home-care recipients had care hours cut when algorithmic assessment replaced nurse judgment, and courts found due-process violations centered on the inability to understand or contest determinations.

Sources: upturn, universityofmichiganihpi, benefitstechadvocacyhuba, centerfordemocracytechnology, aiaaic

Appears on: /domains/cases/arkansas-archoices

EmpiricalEvaluation evidence on the Allegheny Family Screening Tool found that screener overrides of the tool's recommendations r…

Evaluation evidence on the Allegheny Family Screening Tool found that screener overrides of the tool's recommendations reduced racial disparity in screen-in rates relative to the tool alone.

Sources: vaithianathanetal2019, centreforsocialdataanalytics2019, rittenhouse

Appears on: /domains/cases/allegheny-afst, /pan-lab

EmpiricalIllinois's Rapid Safety Feedback flagged thousands of children at 90-percent-or-higher risk of serious harm — beyond any…

Illinois's Rapid Safety Feedback flagged thousands of children at 90-percent-or-higher risk of serious harm — beyond any caseload's capacity to act — while children who died in known-to-system cases had not been flagged; the agency ended its use in 2017.

Sources: chicagotribunereporting, chicagotribune2017, theimprint2017, governmenttechnologya

Appears on: /domains/cases/illinois-rapid-safety-feedback

EmpiricalOregon's child-welfare agency dropped its AFST-derived Safety at Screening tool in 2022, citing equity concerns amid nat…

Oregon's child-welfare agency dropped its AFST-derived Safety at Screening tool in 2022, citing equity concerns amid national scrutiny of racial disparity in child-welfare algorithms.

Sources: nprap2022, willametteweek2022

Appears on: /domains/cases/oregon-safety-at-screening

ScenarioIn a published PAN model run over a supervised-plus-agent scenario, adding a verifier to the autonomous agent removed ro…

In a published PAN model run over a supervised-plus-agent scenario, adding a verifier to the autonomous agent removed roughly 45.8% of steady-state harm and a coordinated governance package roughly 43.0%, while upgrading the model alone removed roughly 6.3%.

Illustrative PAN-run result — PAN Social Work User Guide (v6.2.11.6), lever-ranking walkthrough (fig03).

Appears on: /practice/verifier-on-the-agent, /practice/improve-the-model, /pan-lab

ScenarioAcross a published PAN sweep of 216 deployment topologies (432 sampled deployments), system-layer governance outperforme…

Across a published PAN sweep of 216 deployment topologies (432 sampled deployments), system-layer governance outperformed equal-effort model-layer improvement in 99.1% of cases at roughly 3.4x mean leverage - a figure the project corrected downward from an earlier 9.4x estimate.

Illustrative PAN-run result — PAN Normalized Baseline Analysis; README direction-and-shape summary.

Appears on: /practice/improve-the-model, /pan-lab

ScenarioIn published PAN runs, content-blind record removal raised the contaminated share of the record system by stripping beni…

In published PAN runs, content-blind record removal raised the contaminated share of the record system by stripping benign dilution; only content-aware decontamination reliably reduced it.

Illustrative PAN-run result — PAN Governance Lever Audit ('content-blind levers backfire').

Appears on: /practice/connection-authorization, /pan-lab, /practice/data-minimization

ScenarioIn published PAN runs, the same AI embedded in three office cultures produced steady-state error adoption of roughly 75%…

In published PAN runs, the same AI embedded in three office cultures produced steady-state error adoption of roughly 75% (agentic low-oversight), 20% (human-supervised), and 16% (high-governance professional).

Illustrative PAN-run result — PAN Social Work User Guide (v6.2.11.6), three-scenario comparison (fig02b).

Appears on: /pan-lab

EmpiricalIn a national survey of 1,179 U.S.-based social workers conducted from October 2025 to February 2026 by the University o…

In a national survey of 1,179 U.S.-based social workers conducted from October 2025 to February 2026 by the University of Texas at Austin in collaboration with NASW, 63.5% of respondents reported using AI tools or technologies in their current role.

Sources: borah2026b, borah2026a

Appears on: /pan-lab

EmpiricalIn the same national survey, concerns about data privacy and security were the most frequently reported challenge to usi…

In the same national survey, concerns about data privacy and security were the most frequently reported challenge to using AI in practice (46.5% of respondents), and an increased focus on client privacy and confidentiality was the most requested improvement to AI tools for social work (50.4%).

Sources: borah2026b

Appears on: /pan-lab, /practice/data-minimization

EmpiricalIn the same national survey, 40.8% of respondents reported ethical concerns about relying on AI for decision-making, and…

In the same national survey, 40.8% of respondents reported ethical concerns about relying on AI for decision-making, and overreliance on automated decision-making was among the most frequently cited concerns overall.

Sources: borah2026b

Appears on: /pan-lab

EmpiricalThe same national survey describes a gap between AI exposure and AI preparedness: 26.6% of respondents cited lack of tra…

The same national survey describes a gap between AI exposure and AI preparedness: 26.6% of respondents cited lack of training or understanding of AI technology as a challenge, 53.4% said training on AI tools and effective use would help, and clear guidelines on the ethical use of AI were the most-endorsed need (66.8%).

Sources: borah2026b

Appears on: /pan-lab

EmpiricalIn the same national survey, 42.1% of respondents reported having no role in decision-making about AI adoption in their …

In the same national survey, 42.1% of respondents reported having no role in decision-making about AI adoption in their workplace; the report concludes most respondents have limited or no control over how AI technologies are selected or implemented within their organizations.

Sources: borah2026b

Appears on: /pan-lab

EmpiricalIn the same national survey's open-ended comments, ethical concerns — prominently including the environmental impact of …

In the same national survey's open-ended comments, ethical concerns — prominently including the environmental impact of AI infrastructure — were the most common theme, and the report's first recommendation includes environmental impact among the topics profession-wide ethical guidance should address.

Sources: borah2026b, massey2026

Appears on: /pan-lab

ConceptualAI literacy — the knowledge and skills required to understand, use, and critically evaluate AI systems — has been propos…

AI literacy — the knowledge and skills required to understand, use, and critically evaluate AI systems — has been proposed as a core competency for social work, relevant even to practitioners who never directly use AI tools.

Sources: ahn2025

Appears on: /pan-lab

EmpiricalResearch on AI sycophancy describes it as a fragmented construct — a family of distinct agreement-seeking behaviors that…

Research on AI sycophancy describes it as a fragmented construct — a family of distinct agreement-seeking behaviors that share a label but differ in form, mechanism, measurement, and required mitigation — and finds it intensifies under user pushback and across multi-turn interaction.

Sources: ye2026

Appears on: /pan-lab

Pending citations (0)

Tracked openly: statements whose known sources have not yet been ingested into the PAN reference library. Each carries its resolution path; the launch discipline drives this list toward zero.

How claims are labeled

Empirical: A statement of fact about the world — always cited to the Evidence Registry.
Conceptual: Framing or definition; a way of seeing, not a factual assertion.
Scenario: A stylized, illustrative construct (PAN Lab scenarios, case stylizations).
Hypothesis: A governance hypothesis or design rationale offered for testing.
Assumption: A modeling assumption, labeled with its evidence grade.

Grounding sources (78)

Real-world case documentation and benchmark evidence — audits, royal commissions, investigative reporting, government evaluations — organized by the PAN component each source grounds.

deployment audit: Allegheny AFST1

stapletonetal2022Peer-reviewed
Stapleton et al., Imagining new futures beyond predictive systems in child welfare (FAccT 2022) https://dl.acm.org/doi/10.1145/3531146.3533177 link

deployment audit: Allegheny Housing Assessment (AHA / MH-AHA)2

alleghenycountydhs2021aGovernment
Allegheny County DHS, Allegheny Housing Assessment methodology report (2021) https://www.alleghenycountyanalytics.us/wp-content/uploads/2021/01/20-ACDHS-24-MethodologyReport_01142021_v2.pdf link
alleghenycountydhs2021bGovernment
Allegheny County DHS, Allegheny Housing Assessment methodology report (2021) https://analytics.alleghenycounty.us/2024/12/18/improving-prioritization-of-housing-services-implementation-of-the-allegheny-housing-assessment/ link

deployment audit: Arkansas ARChoices / Idaho Medicaid2

universityofmichiganihpiAcademic
University of Michigan IHPI, What happens when an algorithm cuts your health care https://ihpi.umich.edu/news/what-happens-when-algorithm-cuts-your-health-care link
upturnAdvocacy
Upturn, Calculated Need: automated home-care hour allocation https://www.upturn.org/work/calculated-need/ link

deployment audit: Benefits-navigation chatbots1

uGovernment
U.S. Social Security Administration (agency AI use inventories) https://www.ssa.gov/ link

deployment audit: Casenotes-as-training-data (research)3

casenotesandpredictivechildwaPeer-reviewed
Casenotes and predictive child-welfare models: bias feedback loops (research + ACLU-WA) https://arxiv.org/pdf/2302.08497 link
casenotesandpredictivechildwbPeer-reviewed
Casenotes and predictive child-welfare models: bias feedback loops (research + ACLU-WA) https://arxiv.org/pdf/2403.05573 link
casenotesandpredictivechildwcPeer-reviewed
Casenotes and predictive child-welfare models: bias feedback loops (research + ACLU-WA) https://www.aclu-wa.org/news/automated-decision-systems-child-welfare-predictive-analytics-tools/ link

deployment audit: DWP fraud & error ML (UK)1

theguardian2024Investigative
The Guardian, DWP algorithm bias by age, disability, marital status, nationality (2024) https://www.theguardian.com/society/2024/dec/06/dwp-algorithm-bias-disabled-people-benefits link

deployment audit: Federal ACF predictive-analytics push2

administrationforchildrenand2025aGovernment
Administration for Children and Families, predictive-analytics child-welfare pilots (2025-2026) https://acf.gov/media/press/2026/acf-announces-6-million-states-pilot-predictive-analytics-child-welfare link
administrationforchildrenand2025bGovernment
Administration for Children and Families, predictive-analytics child-welfare pilots (2025-2026) https://acf.gov/acyf/policy-guidance/modernizing-child-welfare-technology-predictive-risk-modeling link

deployment audit: Hackney / Xantura Early Help Profiling1

theguardian2019Investigative
The Guardian, Councils using algorithms to make welfare decisions (2019) https://www.theguardian.com/society/2019/oct/16/councils-using-algorithms-make-welfare-decisions-benefits link

deployment audit: Illinois Rapid Safety Feedback1

chicagotribune2017Investigative
Chicago Tribune, Can an algorithm tell when kids are in danger? (2017) https://www.chicagotribune.com/2017/12/06/can-an-algorithm-tell-when-kids-are-in-danger/ link

deployment audit: Magic Notes (Beam)1

beamVendor
Beam, Magic Notes (assessment transcription/summarization) https://www.beam.org/magic-notes link

deployment audit: Michigan MiDAS1

ieeespectrumaInvestigative
IEEE Spectrum, Michigan's MiDAS unemployment system: Algorithm alchemy that created lead, not gold https://spectrum.ieee.org/michigans-midas-unemployment-system-algorithm-alchemy-that-created-lead-not-gold link

deployment audit: Microsoft 365 Copilot1

ukgovernmentGovernment
UK Government, M365 Copilot and data protection https://www.gov.uk/government/publications/m365-copilot-and-data-protection link

deployment audit: Nevada unemployment-appeals RAG (Google/Vertex AI)3

nevadagenerativeaiunemploymeaInvestigative
Nevada generative-AI unemployment-appeals RAG (Route Fifty; GovTech; Nevada Independent) https://www.govtech.com/artificial-intelligence/nevada-harnesses-genai-for-employment-claims-evaluation link
nevadagenerativeaiunemploymebInvestigative
Nevada generative-AI unemployment-appeals RAG (Route Fifty; GovTech; Nevada Independent) https://thenevadaindependent.com/article/opinion-wrong-answers-faster-meet-nevadas-new-unemployment-ai-overlord link
nevadagenerativeaiunemployme2025Investigative
Nevada generative-AI unemployment-appeals RAG (Route Fifty; GovTech; Nevada Independent) https://www.route-fifty.com/artificial-intelligence/2025/05/nevada-turns-ai-speed-unemployment-appeals/404987/ link

deployment audit: NYC MyCity chatbot2

themarkup2024aInvestigative
The Markup, NYC's AI chatbot tells businesses to break the law (2024); OECD AI incident https://themarkup.org/artificial-intelligence/2024/03/29/nycs-ai-chatbot-tells-businesses-to-break-the-law link
themarkup2024bInvestigative
The Markup, NYC's AI chatbot tells businesses to break the law (2024); OECD AI incident https://oecd.ai/en/incidents/2024-03-29-3dce link

deployment audit: Robodebt1

royalcommissionintotherobode2023aGovernment
Royal Commission into the Robodebt Scheme (2023) https://robodebt.royalcommission.gov.au/ link

deployment audit: Rotterdam welfare-fraud algorithm1

wiredlighthousereports2023Investigative
WIRED / Lighthouse Reports, Inside the suspicion machine (2023) https://www.wired.com/story/welfare-state-algorithms/ link

deployment audit: SyRI / childcare-benefits (toeslagenaffaire)1

amnestyinternational2021Advocacy
Amnesty International, Xenophobic machines: Discrimination through unregulated use of algorithms in the Dutch childcare benefits scandal (2021) https://www.amnesty.org/en/documents/eur35/4686/2021/en/ link

empirical cap: catch_at_generation (max)5

theillusionofprogressPeer-reviewed
'The Illusion of Progress' (arXiv:2508.08285) — LLM-as-Judge Precision 0.736 / Recall 0.957 / F1 0.832 vs human consensus on QA.
datadogllmasajudge2025Industry
Datadog LLM-as-a-judge (2025) — detection F1 drops substantially from HaluBench to the harder RAGTruth; harder hallucinations are harder to catch.
faithfulragleaderboardPeer-reviewed
Faithful RAG leaderboard (arXiv:2505.04847) — FaithJudge with o3-mini-high reaches ~84% balanced accuracy / ~82% F1 on FaithBench (optimistic ceiling).
mentalhealthchatbotdetectionPeer-reviewed
Mental-health chatbot detection (arXiv:2604.06216) — GPT judges 54.6% accuracy, 9.3% recall (miss 90.7% of hallucinations); traditional methods F1<0.30 on subjective content.
samedetectionaccuracyliteratPeer-reviewed
Same detection-accuracy literature as catch_at_generation (FaithBench arXiv:2410.13210; arXiv:2508.08285); audit-time detection is bounded by the same hallucination-detection ceiling.

empirical cap: decontaminate (max)2

samedetectionaccuracyliteratPeer-reviewed
Same detection-accuracy literature as catch_at_generation (FaithBench arXiv:2410.13210; arXiv:2508.08285); audit-time detection is bounded by the same hallucination-detection ceiling.
halludetectlegaldomainPeer-reviewed
HalluDetect legal-domain (arXiv:2509.11619) — best mitigation architecture reaches ~96% token accuracy in a FAVORABLE, retrieval-grounded legal setting (optimistic end).

empirical cap: frac_verifiable (max)1

ragevaluationsurveyPeer-reviewed
RAG evaluation survey (arXiv:2405.07437) — factuality evaluation is bounded by knowledge-base coverage and retrieval accuracy; what is checkable depends on what is documented.

empirical cap: groundtruth_reliability (max)3

faithfulragwithsparseautoencPeer-reviewed
Faithful RAG with Sparse Autoencoders (arXiv:2512.08892) — even with relevant passages retrieved, models contradict evidence / invent details; faithfulness is not guaranteed.
faithfulragPeer-reviewed
FaithfulRAG (arXiv:2506.08938) — RAG systems struggle in knowledge-conflict scenarios even when relevant passages are retrieved (pessimistic end).
retrievalaugmentedcovidfactcPeer-reviewed
Retrieval-augmented COVID-19 fact-checking (PMC12079058) — CRAG/Self-RAG reach 0.972-0.978 accuracy against a curated 130k peer-reviewed corpus (optimistic ceiling).

empirical cap: model_error_base (min)6

halogenPeer-reviewed
HALoGEN (arXiv:2501.08292) — best models hallucinate 4%-86% of generated facts depending on domain.
karpowicz2025Peer-reviewed
Karpowicz (2025) — three independent mathematical frameworks (auction theory, proper scoring, log-sum-exp) all conclude no LLM inference mechanism can be simultaneously truthful, etc.
llmstats2026Industry evaluation
llm-stats.com failure-focused eval (2026) — FactsGrounding 89.1% accuracy => ~10.9% failure on a relatively easy grounded benchmark.
openai2025Frontier lab
OpenAI (2025), 'Why Language Models Hallucinate' — next-token training plus IDK-penalizing benchmarks push models to bluff; explains the persistent nonzero floor.
suprmindbenchmarkdigest2026Industry evaluation
Suprmind benchmark digest (2026) — production ChatGPT ~4.8% major-incorrect with reasoning vs ~11.6% without; HealthBench 3.6%->1.6% with GPT-5 thinking.
xuetal2024Peer-reviewed
Xu et al. (2024), 'Hallucination is Inevitable: An Innate Limitation of LLMs' — formal proof that hallucination cannot be eliminated.

model org: allegheny_afst4

centreforsocialdataanalytics2019Academic
Centre for Social Data Analytics (AUT), AFST evaluation summary https://csda.aut.ac.nz/news-and-events/2019/allegheny-family-screening-tool-evaluation-improved-decision-accuracy,-reduced-disparities link
eubanks2018aInvestigative
Eubanks, Automating Inequality (2018); AP investigation (Ho & Burke, 2022) https://www.pbs.org/newshour/nation/ap-report-doj-examining-ai-screening-tool-used-by-pa-child-welfare-agency link
rittenhouseAcademic
Rittenhouse, Algorithms, Humans and Racial Disparities in Child Protective Services https://krittenh.github.io/katherine-rittenhouse.com/Rittenhouse_Algorithms.pdf link
vaithianathanetal2019Government evaluation
Vaithianathan et al., AFST Impact Evaluation (Allegheny County DHS, 2019) https://www.alleghenycountyanalytics.us/wp-content/uploads/2019/05/Impact-Evaluation-Summary-from-16-ACDHS-26_PredictiveRisk_Package_050119_FINAL-5.pdf link

model org: arkansas_archoices_aria3

aiaaicReference
AIAAIC, Arkansas DHS ARChoices RUGs algorithm https://www.aiaaic.org/aiaaic-repository/ai-algorithmic-and-automation-incidents/arkansas-dhs-archoices-rugs-algorithm link
benefitstechadvocacyhubaAdvocacy
Benefits Tech Advocacy Hub, Arkansas Medicaid HCBS Hours Cuts https://www.btah.org/case-study/arkansas-medicaid-home-and-community-based-services-hours-cuts.html link
centerfordemocracytechnologyAdvocacy
Center for Democracy & Technology, When computer programs cut benefits https://cdt.org/insights/what-happens-when-computer-programs-automatically-cut-benefits-that-disabled-people-rely-on-to-survive/ link

model org: australia_robodebt3

lawsocietyjournalInvestigative
Law Society Journal, Crude, cruel and unlawful: Robodebt findings https://lsj.com.au/articles/crude-cruel-and-unlawful-robodebt-royal-commission-findings/ link
royalcommissionintotherobodeReference
Royal Commission into the Robodebt Scheme (Wikipedia overview) https://en.wikipedia.org/wiki/Royal_Commission_into_the_Robodebt_Scheme link
royalcommissionintotherobode2023bGovernment
Royal Commission into the Robodebt Scheme, Report (2023) https://robodebt.royalcommission.gov.au/publications/report link

model org: beam_magic_notes3

magicnotesVendor
Magic Notes (Beam) product / methodology https://magicnotes.ai/ link
socialcare2024Trade press
SocialCare.Today, Magic Notes AI saves social workers time (human-in-the-loop) https://socialcare.today/2024/09/26/magic-notes-ai-tool-saves-social-workers-time-on-admin/ link
somersetcouncilGovernment
Somerset Council, social workers save time with Magic Notes https://www.somerset.gov.uk/news/somerset-social-workers-save-time-on-admin-thanks-to-ai-tool-magic-notes/ link

model org: hackney_early_help1

reportingonhackneyehpsxantur2020Investigative
Reporting on Hackney EHPS / Xantura predictive profiling (discontinued) https://www.theguardian.com/society/2020/sep/24/councils-scrapping-algorithms-benefit-welfare-decisions-concerns-bias link

model org: illinois_rapid_safety_feedback3

chicagotribunereportingInvestigative
Chicago Tribune reporting (4,100 children at >=90% risk; missed actual fatalities) https://www.governing.com/archive/tns-chicago-data-mining.html link
governmenttechnologyaInvestigative
Government Technology, Illinois Ends Child Abuse Prediction Program https://govtech.com/health/Illinois-Ends-Child-Abuse-Prediction-Program.html link
theimprint2017Investigative
The Imprint, Illinois Drops Rapid Safety Feedback (2017) https://imprintnews.org/politics/stateline-illinois-drops-rapid-safety-feedback-predictive-analytics-tool/28913 link

model org: indiana_ibm_eligibility3

eubanks2018bInvestigative
Eubanks, Automating Inequality (2018); The Nation, Want to Cut Welfare? There's an App for That https://www.thenation.com/article/archive/want-cut-welfare-theres-app/ link
governmenttechnologybInvestigative
Government Technology, IBM and Indiana Suing Each Other https://www.govtech.com/health/ibm-and-indiana-suing-each-other.html link
ieeespectrumbInvestigative
IEEE Spectrum, Indiana and IBM Sue Each Other Over Failed Outsourcing Contract https://spectrum.ieee.org/indiana-and-ibm-sue-each-other-over-failed-outsourcing-contract link

model org: michigan_midas3

aiincidentdatabaseInvestigative
AI Incident Database, Incident 373 (MiDAS false fraud claims) https://incidentdatabase.ai/cite/373/ link
benefitstechadvocacyhubbAdvocacy
Benefits Tech Advocacy Hub, Michigan UI False Fraud Determinations https://www.btah.org/case-study/michigan-unemployment-insurance-false-fraud-determinations.html link
michiganag2022Government
Michigan AG, settlement of civil-rights class action (Bauserman, 2022) https://www.michigan.gov/ag/news/press-releases/2022/10/20/som-settlement-of-civil-rights-class-action-alleging-false-accusations-of-unemployment-fraud link

model org: nava_assistive_chatbot3

navaaVendor
Nava, Introducing our pilot with Imagine LA: testing an AI chatbot https://www.navapbc.com/news/pilot-ai-chatbot-benefits link
navabVendor
Nava, Evaluating a GenAI-powered assistive chatbot for caseworkers https://www.navapbc.com/case-studies/evaluating-ai-assistive-chatbot-caseworkers link
navabenefitsdatatrustVendor
Nava + Benefits Data Trust, AI to support public benefit navigators https://www.navapbc.com/news/nava-ai-public-benefits link

model org: oregon_safety_at_screening2

nprap2022Investigative
NPR/AP, Oregon is dropping an AI tool used in child welfare system (2022) https://www.npr.org/2022/06/02/1102661376/oregon-drops-artificial-intelligence-child-abuse-cases link
willametteweek2022Investigative
Willamette Week, Oregon DHS to End Its Use of Child Abuse Risk Algorithm (2022) https://www.wweek.com/news/state/2022/06/04/oregon-department-of-human-services-ends-its-use-of-child-abuse-risk-algorithm/ link

model org: rotterdam_welfare_fraud3

followthemoneyInvestigative
Follow the Money, How a fraud algorithm learned to suspect vulnerable groups https://www.ftm.eu/articles/algorithm-rotterdam-dissected link
lighthousereports2023Investigative
Lighthouse Reports, Suspicion Machines (2023) https://www.lighthousereports.com/investigation/suspicion-machines/ link
racismandtechnologycenter2023Advocacy
Racism and Technology Center, Rotterdam welfare fraud algorithm was biased https://racismandtechnology.center/2023/03/17/racist-technology-in-action-rotterdams-welfare-fraud-prediction-algorithm-was-biased/ link

regulatory context: EU AI Act1

euaiacthighriskclassificatioRegulatory
EU AI Act — high-risk classification for eligibility to essential public benefits https://artificialintelligenceact.eu/ link

regulatory context: professional bodies2

britishassociationofsocialwo2025Regulatory
British Association of Social Workers (BASW) — 2025 AI guidance https://www.basw.co.uk/ link
nationalassociationofsocialwRegulatory
National Association of Social Workers (NASW) — ethics & technology guidance https://www.socialworkers.org/ link

workforce data: caseload/workload3

centerfornewyorkcityaffairsData
Center for New York City Affairs, Long hours, high caseloads https://www.centernyc.org/long-hours-high-caseloads link
childwelfareleagueofamericaData
Child Welfare League of America (CWLA), caseload/workload standards https://www.cwla.org/our-work/practice-excellence-center/workforce-2/caseload-workload/ link
nycadministrationforchildrenGovernment
NYC Administration for Children's Services (ACS), becoming a CPS specialist https://www.nyc.gov/site/acs/about/becoming-cps.page link

Academic references (230)

The peer-reviewed and professional literature behind the PAN project, deduplicated with stable citation keys. Topic index first; the full alphabetical list follows.

Topic index (14 topics)

ai-alignment: afonin2026, anwar2024, betley2026, lu2025, panpatil2025
ai-governance: jegham2025, khan2025, li2023, mcdermott2024b, nel2024a, officeofmanagementandbudget2024, officeofmanagementandbudget2025, tabassi2023
ai-policy: europeanparliamentcounciloft2024, europeanparliamentandcouncil2024, executiveorderno2025
ai-safety: akbulut2026, anwar2024, chandra2026, greenstein2025, jin2024a, jin2025, korbak2025, lu2025, williams2024, ye2026
algorithmic-fairness: chouldechova2017, elliott2017, eubanks2018, henly2022, keddell2019, saxena2024, stapleton2022, veinot2018
antifragility: jin2024a, jin2024b, jin2025, taleb2007, taleb2012, taleb2023
child-welfare: alleghenycounty2019, alleghenycountydepartmentofh2024, centerforadvancedstudiesinch2025, chouldechova2017, keddell2019, saxena2024, stapleton2022
co-design: costanzachock2020, israel2005, minkler2008, sanders2008
complexity-science: afonin2026, barabasi2016, barabasi1999, barzel2013, braithwaite2018, byrne2014, cairney2012, chandler2016, ebrahim2019, gerrits2013, geyer2010, green2010, greenhalgh2018a, greenhalgh2018b, harvey2023, harvey2022, head2022, holland2014, krakauer2025, lieberman2010, long2018b, maillet2025, mcdaniel2009, mcdermott2024a, mcdermott2024b, moore2019a, moore2019b, morcol2013, nel2024a, nel2024b, nikolaou2025, paley2007, paley2010a, paley2010b, panpatil2025, plsek2001, rosenberg2025, sage2021, sterman2006, sundlevander2020b, vanewijk2018, watts2003, watts1998, yakovchenko2021, zhang2026
evaluation: patton2016, reynolds2014
implementation-science: braithwaite2018, moullin2020, moullin2019a, moullin2019b, yakovchenko2021
privacy-security: hipaajournal2026a, hipaajournal2026b, kiteworks2026, u2025a
professional-ethics: minkler2008, nasw2017, nationalassociationofsocialw2017, nationalassociationofsocialw2021a, nationalassociationofsocialw2021b
social-work: ahn2025, akesson2017, americanacademyofsocialworka2021, associationofsocialworkboardnd, badillodiaz2025, baez2026, barth2022, berringer2019, berzin2015, boduroglu2026, borah2026a, borah2026b, britishassociationofsocialwo2025a, britishassociationofsocialwo2025b, centerforadvancedstudiesinch2025, communitycare2025a, communitycare2025b, coulton2015, dey2023, flaherty2026, garkisch2024, goldkind2021, goldkind2019, goldkind2023, goldkind2018, green2010, harvey2022, henly2022, hiltz2025, hitchcock2024, hitchcock2026, hothersall2019, houston2021, huang2022, jacobi2023, jones2024a, jones2024b, karatas2026, liedgren2016, lucio2026, massey2026, mcdermott2024a, mcdermott2024b, minkler2008, mishna2021, nasw2017, nationalassociationofsocialw2017, nationalassociationofsocialw2021a, nationalassociationofsocialw2021b, nationalassociationofsocialwnd, nuwasiima2024, ogbanga2025, pandya2026, patton2020, patton2023, reamer2019, reamer2023, ricciardelli2026, rubin2024, sage2021, singer2015, singer2022, theaisocialworker2026, vanewijk2018, williams2016a, williams2016b, wykman2023, yeshivauniversitywurzweilers2025a, yeshivauniversitywurzweilers2025b

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