Industry

HR & Talent Technology

Fairness, explainability, and compliance engineering in recruiting and talent management AI preventing bias and ensuring regulatory adherence across hiring.

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Neuro-Symbolic Architecture & Constraint Systems
Enterprise HR & Neurodiversity Compliance

Aon's AI scored autistic candidates low on 'liveliness.' The ACLU filed an FTC complaint. 🧠

350K
unique test items evaluating personality constructs that track autism criteria
Aon ADEPT-15 / ACLU Filing
90%
of bias removable by switching video AI to audio-only mode
ACLU / CiteHR Analysis
Interactive Technical
View details

The Algorithmic Ableism Crisis

AI personality assessments marketed as bias-free are functioning as stealth medical exams, systematically screening out neurodivergent candidates through proxy traits that mirror clinical diagnostic criteria.

STEALTH DISABILITY SCREENING

Aon's ADEPT-15 evaluates traits like 'liveliness' and 'positivity' that directly overlap with autism diagnostic criteria. When an algorithm penalizes 'reserved' responses, it screens for neurotypicality rather than job competence. Duke research found LLMs rate 'I have autism' more negatively than 'I am a bank robber.'

CAUSAL FAIRNESS ENGINEERING
  • Deploy Causal Representation Learning to isolate hidden proxy-discrimination pathways
  • Train adversarial debiasing networks penalizing predictive leakage of protected characteristics
  • Implement counterfactual fairness auditing with synthetic candidate variations
  • Design neuro-inclusive pipelines with temporal elasticity and cross-channel fusion
Causal Representation LearningAdversarial DebiasingCounterfactual FairnessNLP Bias AuditingNIST AI RMF
Read Interactive Whitepaper →Read Technical Whitepaper →
Continuous Monitoring & Audit Trails
Human Resources & Talent Acquisition

Amazon's AI recruited men for 3 years. Learned gender from 'Women's Chess Club.' Scrapped the system. Black Box = Bias Amplifier. ⚖️

3+ Years
Amazon AI recruiting duration
Reuters investigation findings
0.8
Impact ratio threshold
NYC Law 144
Interactive Technical
View details

The Glass Box Paradigm: Engineering Fairness, Explainability, and Precision in Enterprise Recruitment with Knowledge Graphs

Amazon's AI discriminated against women for 3+ years. Glass Box Knowledge Graphs separate demographics from decisions, ensuring compliance and eliminating bias structurally.

AMAZON BLACK BOX

AI trained on male-dominated hiring data optimized for gender bias. Black Box found proxy variables like women's clubs. Amazon scrapped after 3 years.

GLASS BOX GRAPHS
  • Knowledge Graphs use deterministic traversal algorithms
  • Demographic nodes excluded from inference graphs
  • Skill distance measured using graph embeddings
  • Regulatory compliance with audit trail transparency
Knowledge GraphsExplainable AINeo4jGraph EmbeddingsNode2VecGraphSAGESemantic MatchingCosine SimilarityBias MitigationNYC Local Law 144EU AI ActGDPR ComplianceDeterministic ReasoningSubgraph Filtering
Read Interactive Whitepaper →Read Technical Whitepaper →
Solutions Architecture & Reference Implementation
Human Resources & Talent Acquisition

Amazon's AI recruited men for 3 years. Learned gender from 'Women's Chess Club.' Scrapped the system. Black Box = Bias Amplifier. ⚖️

3+ Years
Amazon AI recruiting duration
Reuters investigation findings
0.8
Impact ratio threshold
NYC Law 144
Interactive Technical
View details

The Glass Box Paradigm: Engineering Fairness, Explainability, and Precision in Enterprise Recruitment with Knowledge Graphs

Amazon's AI discriminated against women for 3+ years. Glass Box Knowledge Graphs separate demographics from decisions, ensuring compliance and eliminating bias structurally.

AMAZON BLACK BOX

AI trained on male-dominated hiring data optimized for gender bias. Black Box found proxy variables like women's clubs. Amazon scrapped after 3 years.

GLASS BOX GRAPHS
  • Knowledge Graphs use deterministic traversal algorithms
  • Demographic nodes excluded from inference graphs
  • Skill distance measured using graph embeddings
  • Regulatory compliance with audit trail transparency
Knowledge GraphsExplainable AINeo4jGraph EmbeddingsNode2VecGraphSAGESemantic MatchingCosine SimilarityBias MitigationNYC Local Law 144EU AI ActGDPR ComplianceDeterministic ReasoningSubgraph Filtering
Read Interactive Whitepaper →Read Technical Whitepaper →
Human Resources & Recruitment

'Culture fit' = hiring people like me. LLMs favor white names 85% of the time. AI automates historical bias. Counterfactual Fairness required. ⚖️

85%
LLM white name bias
University of Washington 2024
23.9%
Churn Reduction with Causal Inference
Veriprajna Whitepaper
Interactive Technical
View details

Beyond the Mirror: Engineering Fairness and Performance in the Age of Causal AI

LLMs favor white names 85% of the time, automating historical bias. Causal AI uses Structural Causal Models achieving 99% Counterfactual Fairness with 23.9% churn reduction.

CULTURE FIT BIAS

Culture fit masks hiring bias as organizational cohesion. LLMs favor white names 85%, Amazon AI penalized women's clubs. Predictive AI automates historical prejudices.

COUNTERFACTUAL FAIRNESS DESIGN
  • Pearl's Level 3 causation enables fairness
  • Structural models block discriminatory demographic proxies
  • Adversarial debiasing unlearns protected attribute connections
  • NYC Law 144 compliance ensures transparency
Causal AIStructural Causal ModelsSCMCounterfactual FairnessAdversarial DebiasingJudea Pearl's Ladder of CausationHomophily DetectionBias MitigationNYC Local Law 144EU AI ActImpact Ratio AnalysisQuality of HireAlgorithmic RecourseGlass Box AI
Read Interactive Whitepaper →Read Technical Whitepaper →
AI Recruitment Liability & Employment Law

Workday rejected one applicant from 100+ jobs within minutes. The platform processed 1.1 billion rejections. ⚖️

1.1B
applications rejected through Workday's AI during the class action period
Mobley v. Workday Court Filings
100+
qualified-role rejections for one plaintiff, often within minutes
Mobley v. Workday Complaint (N.D. Cal.)
Interactive Technical
View details

The Algorithmic Agent

The Mobley v. Workday ruling establishes that AI vendors performing core hiring functions qualify as employer 'agents' under federal anti-discrimination law.

ALGORITHMIC AGENT LIABILITY

The court distinguished Workday's AI from 'simple tools,' ruling that scoring, ranking, and rejecting candidates makes it an 'agent' under Title VII, ADA, and ADEA. Proxy variables like email domain (@aol.com) and legacy tech references create hidden pathways for age and race discrimination.

NEURO-SYMBOLIC VERIFICATION
  • Implement graph-first reasoning with Knowledge Graph ontologies for auditable hiring logic
  • Deploy adversarial debiasing during training to force removal of discriminatory patterns
  • Integrate SHAP and LIME to generate feature-attribution maps for every candidate score
  • Architect constitutional guardrails preventing proxy-variable discrimination and jailbreaks
Neuro-Symbolic AIGraphRAGSHAP / LIMEAdversarial DebiasingConstitutional Guardrails
Read Interactive Whitepaper →Read Technical Whitepaper →
Enterprise HR & Talent Technology

A Deaf Indigenous woman was told to 'practice active listening' by an AI hiring tool. The ACLU filed a complaint. 🚫

78%
Word Error Rate for Deaf speakers in standard ASR systems
arXiv ASR Feasibility Study
< 80%
Four-Fifths Rule threshold triggering disparate impact liability
EEOC Title VII Guidance
Interactive Technical
View details

The Algorithmic Accountability Mandate

AI hiring platforms built on commodity LLM wrappers systematically exclude candidates with disabilities and non-standard speech patterns, turning algorithmic bias into active discrimination.

BIASED BY DESIGN

Standard ASR systems trained on hearing-centric datasets produce catastrophic 78% error rates for Deaf speakers. When an AI hiring tool analyzes such a transcript, its 'leadership trait' scores are hallucinated from garbage data -- yet enterprises treat these outputs as objective assessments.

ENGINEERED FAIRNESS
  • Deploy adversarial debiasing networks penalizing until protected attributes become undetectable
  • Integrate early multimodal fusion with Modality Fusion Collaborative De-biasing
  • Trigger event-driven Human-in-the-Loop routing when ASR confidence drops below threshold
  • Quantify feature attribution via SHAP with continuous Four-Fifths Rule monitoring
Adversarial DebiasingMultimodal FusionSHAP ExplainabilityHuman-in-the-LoopASR Calibration
Read Interactive Whitepaper →Read Technical Whitepaper →
Knowledge Graph & Domain Ontology Engineering
Human Resources & Talent Acquisition

Amazon's AI recruited men for 3 years. Learned gender from 'Women's Chess Club.' Scrapped the system. Black Box = Bias Amplifier. ⚖️

3+ Years
Amazon AI recruiting duration
Reuters investigation findings
0.8
Impact ratio threshold
NYC Law 144
Interactive Technical
View details

The Glass Box Paradigm: Engineering Fairness, Explainability, and Precision in Enterprise Recruitment with Knowledge Graphs

Amazon's AI discriminated against women for 3+ years. Glass Box Knowledge Graphs separate demographics from decisions, ensuring compliance and eliminating bias structurally.

AMAZON BLACK BOX

AI trained on male-dominated hiring data optimized for gender bias. Black Box found proxy variables like women's clubs. Amazon scrapped after 3 years.

GLASS BOX GRAPHS
  • Knowledge Graphs use deterministic traversal algorithms
  • Demographic nodes excluded from inference graphs
  • Skill distance measured using graph embeddings
  • Regulatory compliance with audit trail transparency
Knowledge GraphsExplainable AINeo4jGraph EmbeddingsNode2VecGraphSAGESemantic MatchingCosine SimilarityBias MitigationNYC Local Law 144EU AI ActGDPR ComplianceDeterministic ReasoningSubgraph Filtering
Read Interactive Whitepaper →Read Technical Whitepaper →
AI Governance & Compliance Program
Human Resources & Recruitment

'Culture fit' = hiring people like me. LLMs favor white names 85% of the time. AI automates historical bias. Counterfactual Fairness required. ⚖️

85%
LLM white name bias
University of Washington 2024
23.9%
Churn Reduction with Causal Inference
Veriprajna Whitepaper
Interactive Technical
View details

Beyond the Mirror: Engineering Fairness and Performance in the Age of Causal AI

LLMs favor white names 85% of the time, automating historical bias. Causal AI uses Structural Causal Models achieving 99% Counterfactual Fairness with 23.9% churn reduction.

CULTURE FIT BIAS

Culture fit masks hiring bias as organizational cohesion. LLMs favor white names 85%, Amazon AI penalized women's clubs. Predictive AI automates historical prejudices.

COUNTERFACTUAL FAIRNESS DESIGN
  • Pearl's Level 3 causation enables fairness
  • Structural models block discriminatory demographic proxies
  • Adversarial debiasing unlearns protected attribute connections
  • NYC Law 144 compliance ensures transparency
Causal AIStructural Causal ModelsSCMCounterfactual FairnessAdversarial DebiasingJudea Pearl's Ladder of CausationHomophily DetectionBias MitigationNYC Local Law 144EU AI ActImpact Ratio AnalysisQuality of HireAlgorithmic RecourseGlass Box AI
Read Interactive Whitepaper →Read Technical Whitepaper →
Enterprise HR & Neurodiversity Compliance

Aon's AI scored autistic candidates low on 'liveliness.' The ACLU filed an FTC complaint. 🧠

350K
unique test items evaluating personality constructs that track autism criteria
Aon ADEPT-15 / ACLU Filing
90%
of bias removable by switching video AI to audio-only mode
ACLU / CiteHR Analysis
Interactive Technical
View details

The Algorithmic Ableism Crisis

AI personality assessments marketed as bias-free are functioning as stealth medical exams, systematically screening out neurodivergent candidates through proxy traits that mirror clinical diagnostic criteria.

STEALTH DISABILITY SCREENING

Aon's ADEPT-15 evaluates traits like 'liveliness' and 'positivity' that directly overlap with autism diagnostic criteria. When an algorithm penalizes 'reserved' responses, it screens for neurotypicality rather than job competence. Duke research found LLMs rate 'I have autism' more negatively than 'I am a bank robber.'

CAUSAL FAIRNESS ENGINEERING
  • Deploy Causal Representation Learning to isolate hidden proxy-discrimination pathways
  • Train adversarial debiasing networks penalizing predictive leakage of protected characteristics
  • Implement counterfactual fairness auditing with synthetic candidate variations
  • Design neuro-inclusive pipelines with temporal elasticity and cross-channel fusion
Causal Representation LearningAdversarial DebiasingCounterfactual FairnessNLP Bias AuditingNIST AI RMF
Read Interactive Whitepaper →Read Technical Whitepaper →
AI Recruitment Liability & Employment Law

Workday rejected one applicant from 100+ jobs within minutes. The platform processed 1.1 billion rejections. ⚖️

1.1B
applications rejected through Workday's AI during the class action period
Mobley v. Workday Court Filings
100+
qualified-role rejections for one plaintiff, often within minutes
Mobley v. Workday Complaint (N.D. Cal.)
Interactive Technical
View details

The Algorithmic Agent

The Mobley v. Workday ruling establishes that AI vendors performing core hiring functions qualify as employer 'agents' under federal anti-discrimination law.

ALGORITHMIC AGENT LIABILITY

The court distinguished Workday's AI from 'simple tools,' ruling that scoring, ranking, and rejecting candidates makes it an 'agent' under Title VII, ADA, and ADEA. Proxy variables like email domain (@aol.com) and legacy tech references create hidden pathways for age and race discrimination.

NEURO-SYMBOLIC VERIFICATION
  • Implement graph-first reasoning with Knowledge Graph ontologies for auditable hiring logic
  • Deploy adversarial debiasing during training to force removal of discriminatory patterns
  • Integrate SHAP and LIME to generate feature-attribution maps for every candidate score
  • Architect constitutional guardrails preventing proxy-variable discrimination and jailbreaks
Neuro-Symbolic AIGraphRAGSHAP / LIMEAdversarial DebiasingConstitutional Guardrails
Read Interactive Whitepaper →Read Technical Whitepaper →
Enterprise AI Governance & FCRA Compliance

Eightfold AI scraped 1.5 billion data points to build secret 'match scores.' Microsoft and PayPal are named in the lawsuit. 🔍

1.5B
data points allegedly harvested from LinkedIn, GitHub, Crunchbase without consent
Kistler v. Eightfold AI (Jan 2026)
0-5
proprietary match score range filtering candidates before any human review
Eightfold AI Platform / Court Filings
Interactive Technical
View details

The Architecture of Accountability

The Eightfold AI litigation exposes how opaque match scores derived from non-consensual data harvesting transform AI vendors into unregulated consumer reporting agencies.

SECRET DOSSIER SCORING

Eightfold AI harvests professional data to generate 'match scores' that determine candidate fate before human review. Plaintiffs with 10-20 years experience received automated rejections from PayPal and Microsoft within minutes. The lawsuit argues these scores are 'consumer reports' under the FCRA.

GOVERNED MULTI-AGENT ARCHITECTURE
  • Deploy specialized multi-agent systems with provenance, RAG, compliance, and explainability agents
  • Implement SHAP-based feature attribution replacing opaque scores with transparent summaries
  • Enforce cryptographic data provenance ensuring only declared data is used for scoring
  • Architect event-driven orchestration with prompt-as-code versioning and human-in-the-loop gates
Multi-Agent SystemsExplainable AI (XAI)Data ProvenanceFCRA ComplianceSHAP / Counterfactuals
Read Interactive Whitepaper →Read Technical Whitepaper →
Enterprise HR & Talent Technology

A Deaf Indigenous woman was told to 'practice active listening' by an AI hiring tool. The ACLU filed a complaint. 🚫

78%
Word Error Rate for Deaf speakers in standard ASR systems
arXiv ASR Feasibility Study
< 80%
Four-Fifths Rule threshold triggering disparate impact liability
EEOC Title VII Guidance
Interactive Technical
View details

The Algorithmic Accountability Mandate

AI hiring platforms built on commodity LLM wrappers systematically exclude candidates with disabilities and non-standard speech patterns, turning algorithmic bias into active discrimination.

BIASED BY DESIGN

Standard ASR systems trained on hearing-centric datasets produce catastrophic 78% error rates for Deaf speakers. When an AI hiring tool analyzes such a transcript, its 'leadership trait' scores are hallucinated from garbage data -- yet enterprises treat these outputs as objective assessments.

ENGINEERED FAIRNESS
  • Deploy adversarial debiasing networks penalizing until protected attributes become undetectable
  • Integrate early multimodal fusion with Modality Fusion Collaborative De-biasing
  • Trigger event-driven Human-in-the-Loop routing when ASR confidence drops below threshold
  • Quantify feature attribution via SHAP with continuous Four-Fifths Rule monitoring
Adversarial DebiasingMultimodal FusionSHAP ExplainabilityHuman-in-the-LoopASR Calibration
Read Interactive Whitepaper →Read Technical Whitepaper →
Sensor Fusion & Signal Intelligence
Enterprise HR & Talent Technology

A Deaf Indigenous woman was told to 'practice active listening' by an AI hiring tool. The ACLU filed a complaint. 🚫

78%
Word Error Rate for Deaf speakers in standard ASR systems
arXiv ASR Feasibility Study
< 80%
Four-Fifths Rule threshold triggering disparate impact liability
EEOC Title VII Guidance
Interactive Technical
View details

The Algorithmic Accountability Mandate

AI hiring platforms built on commodity LLM wrappers systematically exclude candidates with disabilities and non-standard speech patterns, turning algorithmic bias into active discrimination.

BIASED BY DESIGN

Standard ASR systems trained on hearing-centric datasets produce catastrophic 78% error rates for Deaf speakers. When an AI hiring tool analyzes such a transcript, its 'leadership trait' scores are hallucinated from garbage data -- yet enterprises treat these outputs as objective assessments.

ENGINEERED FAIRNESS
  • Deploy adversarial debiasing networks penalizing until protected attributes become undetectable
  • Integrate early multimodal fusion with Modality Fusion Collaborative De-biasing
  • Trigger event-driven Human-in-the-Loop routing when ASR confidence drops below threshold
  • Quantify feature attribution via SHAP with continuous Four-Fifths Rule monitoring
Adversarial DebiasingMultimodal FusionSHAP ExplainabilityHuman-in-the-LoopASR Calibration
Read Interactive Whitepaper →Read Technical Whitepaper →
Data Provenance & Traceability
Enterprise AI Governance & FCRA Compliance

Eightfold AI scraped 1.5 billion data points to build secret 'match scores.' Microsoft and PayPal are named in the lawsuit. 🔍

1.5B
data points allegedly harvested from LinkedIn, GitHub, Crunchbase without consent
Kistler v. Eightfold AI (Jan 2026)
0-5
proprietary match score range filtering candidates before any human review
Eightfold AI Platform / Court Filings
Interactive Technical
View details

The Architecture of Accountability

The Eightfold AI litigation exposes how opaque match scores derived from non-consensual data harvesting transform AI vendors into unregulated consumer reporting agencies.

SECRET DOSSIER SCORING

Eightfold AI harvests professional data to generate 'match scores' that determine candidate fate before human review. Plaintiffs with 10-20 years experience received automated rejections from PayPal and Microsoft within minutes. The lawsuit argues these scores are 'consumer reports' under the FCRA.

GOVERNED MULTI-AGENT ARCHITECTURE
  • Deploy specialized multi-agent systems with provenance, RAG, compliance, and explainability agents
  • Implement SHAP-based feature attribution replacing opaque scores with transparent summaries
  • Enforce cryptographic data provenance ensuring only declared data is used for scoring
  • Architect event-driven orchestration with prompt-as-code versioning and human-in-the-loop gates
Multi-Agent SystemsExplainable AI (XAI)Data ProvenanceFCRA ComplianceSHAP / Counterfactuals
Read Interactive Whitepaper →Read Technical Whitepaper →
Fairness Audit & Bias Mitigation
Human Resources & Recruitment

'Culture fit' = hiring people like me. LLMs favor white names 85% of the time. AI automates historical bias. Counterfactual Fairness required. ⚖️

85%
LLM white name bias
University of Washington 2024
23.9%
Churn Reduction with Causal Inference
Veriprajna Whitepaper
Interactive Technical
View details

Beyond the Mirror: Engineering Fairness and Performance in the Age of Causal AI

LLMs favor white names 85% of the time, automating historical bias. Causal AI uses Structural Causal Models achieving 99% Counterfactual Fairness with 23.9% churn reduction.

CULTURE FIT BIAS

Culture fit masks hiring bias as organizational cohesion. LLMs favor white names 85%, Amazon AI penalized women's clubs. Predictive AI automates historical prejudices.

COUNTERFACTUAL FAIRNESS DESIGN
  • Pearl's Level 3 causation enables fairness
  • Structural models block discriminatory demographic proxies
  • Adversarial debiasing unlearns protected attribute connections
  • NYC Law 144 compliance ensures transparency
Causal AIStructural Causal ModelsSCMCounterfactual FairnessAdversarial DebiasingJudea Pearl's Ladder of CausationHomophily DetectionBias MitigationNYC Local Law 144EU AI ActImpact Ratio AnalysisQuality of HireAlgorithmic RecourseGlass Box AI
Read Interactive Whitepaper →Read Technical Whitepaper →
Enterprise HR & Neurodiversity Compliance

Aon's AI scored autistic candidates low on 'liveliness.' The ACLU filed an FTC complaint. 🧠

350K
unique test items evaluating personality constructs that track autism criteria
Aon ADEPT-15 / ACLU Filing
90%
of bias removable by switching video AI to audio-only mode
ACLU / CiteHR Analysis
Interactive Technical
View details

The Algorithmic Ableism Crisis

AI personality assessments marketed as bias-free are functioning as stealth medical exams, systematically screening out neurodivergent candidates through proxy traits that mirror clinical diagnostic criteria.

STEALTH DISABILITY SCREENING

Aon's ADEPT-15 evaluates traits like 'liveliness' and 'positivity' that directly overlap with autism diagnostic criteria. When an algorithm penalizes 'reserved' responses, it screens for neurotypicality rather than job competence. Duke research found LLMs rate 'I have autism' more negatively than 'I am a bank robber.'

CAUSAL FAIRNESS ENGINEERING
  • Deploy Causal Representation Learning to isolate hidden proxy-discrimination pathways
  • Train adversarial debiasing networks penalizing predictive leakage of protected characteristics
  • Implement counterfactual fairness auditing with synthetic candidate variations
  • Design neuro-inclusive pipelines with temporal elasticity and cross-channel fusion
Causal Representation LearningAdversarial DebiasingCounterfactual FairnessNLP Bias AuditingNIST AI RMF
Read Interactive Whitepaper →Read Technical Whitepaper →
AI Recruitment Liability & Employment Law

Workday rejected one applicant from 100+ jobs within minutes. The platform processed 1.1 billion rejections. ⚖️

1.1B
applications rejected through Workday's AI during the class action period
Mobley v. Workday Court Filings
100+
qualified-role rejections for one plaintiff, often within minutes
Mobley v. Workday Complaint (N.D. Cal.)
Interactive Technical
View details

The Algorithmic Agent

The Mobley v. Workday ruling establishes that AI vendors performing core hiring functions qualify as employer 'agents' under federal anti-discrimination law.

ALGORITHMIC AGENT LIABILITY

The court distinguished Workday's AI from 'simple tools,' ruling that scoring, ranking, and rejecting candidates makes it an 'agent' under Title VII, ADA, and ADEA. Proxy variables like email domain (@aol.com) and legacy tech references create hidden pathways for age and race discrimination.

NEURO-SYMBOLIC VERIFICATION
  • Implement graph-first reasoning with Knowledge Graph ontologies for auditable hiring logic
  • Deploy adversarial debiasing during training to force removal of discriminatory patterns
  • Integrate SHAP and LIME to generate feature-attribution maps for every candidate score
  • Architect constitutional guardrails preventing proxy-variable discrimination and jailbreaks
Neuro-Symbolic AIGraphRAGSHAP / LIMEAdversarial DebiasingConstitutional Guardrails
Read Interactive Whitepaper →Read Technical Whitepaper →
Explainability & Decision Transparency
Enterprise AI Governance & FCRA Compliance

Eightfold AI scraped 1.5 billion data points to build secret 'match scores.' Microsoft and PayPal are named in the lawsuit. 🔍

1.5B
data points allegedly harvested from LinkedIn, GitHub, Crunchbase without consent
Kistler v. Eightfold AI (Jan 2026)
0-5
proprietary match score range filtering candidates before any human review
Eightfold AI Platform / Court Filings
Interactive Technical
View details

The Architecture of Accountability

The Eightfold AI litigation exposes how opaque match scores derived from non-consensual data harvesting transform AI vendors into unregulated consumer reporting agencies.

SECRET DOSSIER SCORING

Eightfold AI harvests professional data to generate 'match scores' that determine candidate fate before human review. Plaintiffs with 10-20 years experience received automated rejections from PayPal and Microsoft within minutes. The lawsuit argues these scores are 'consumer reports' under the FCRA.

GOVERNED MULTI-AGENT ARCHITECTURE
  • Deploy specialized multi-agent systems with provenance, RAG, compliance, and explainability agents
  • Implement SHAP-based feature attribution replacing opaque scores with transparent summaries
  • Enforce cryptographic data provenance ensuring only declared data is used for scoring
  • Architect event-driven orchestration with prompt-as-code versioning and human-in-the-loop gates
Multi-Agent SystemsExplainable AI (XAI)Data ProvenanceFCRA ComplianceSHAP / Counterfactuals
Read Interactive Whitepaper →Read Technical Whitepaper →

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Veriprajna Deep Tech Consultancy specializes in building safety-critical AI systems for healthcare, finance, and regulatory domains. Our architectures are validated against established protocols with comprehensive compliance documentation.

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