To ensure HumanCare🩵 delivers seamless, empathetic care, the HHS app will leverage agentic systems, proactive AI that senses, predicts, and acts on user needs, shifting from reactive automation ("if X, then Y") to adaptive experiences that anticipate human complexity. 

Drawing on human-centered design (HCD) principles, the app prioritizes empathy before efficiency: shadowing real patient journeys to understand frustrations like confusing portals, ensuring accessibility with clear language and multimodal interfaces (voice, text, visual), and contextual adaptation for emotional and physical realities.

Digital-first imperatives include omni-channel orchestration (e.g., starting a booking in the app and continuing via SMS without repetition), a unified data fabric integrating clinical records, wearables, and patient reports for real-time nudges (like early fatigue alerts to prevent hospitalizations), and trust through transparency, explaining every action's "why" (e.g., "This ride suggestion is based on your recent labs and location").

Imagine a patient's wearable detecting early overdose risks, cross-referencing with history, and alerting the care team before crisis; or the app adjusting appointment slots for complex cases. This agentic approach, powered by Responsible AI, ensures providers and patients thrive in a free-market system.

✅ Up to 30% fewer preventable hospitalizations

✅ 20–30% fewer overdose deaths

✅ 2–5 additional years of life expectancy for low-income groups

✅ 200,000 fewer medical bankruptcies per year

To bring agentic, human-centered AI to life in the HHS app, HumanCare🩵 relies on robust AI workflows, structured pipelines that ensure data flows securely from input to output while upholding Responsible AI principles. These workflows transform raw user data (e.g., wearable metrics or appointment history) into proactive, personalized care, minimizing errors and maximizing trust.Key AI workflow stages include:

• Data Ingestion and Preparation: Securely aggregating multimodal inputs (e.g., voice queries, lab results, geolocation) with privacy-by-design encryption, filtering for biases via automated audits to prevent disparities in care recommendations.
• Model Inference and Adaptation: Real-time processing using lightweight, edge-deployed models that predict needs (e.g., overdose risk alerts) and adapt via federated learning—updating across devices without centralizing sensitive data.
• Output Generation and Monitoring: Delivering empathetic responses (e.g., "Based on your labs, here's a ride to your doctor, why this matters for you") with explainability layers (like SHAP values for transparency), followed by continuous monitoring for drift or anomalies, triggering human oversight if thresholds are breached.
• Feedback Loop and Iteration: User interactions feed back into the system for iterative improvement, with KPIs tracking equity (e.g., 95%+ fair outcomes across demographics) and efficiency (e.g., <2-second response times).

By embedding these workflows, HumanCare🩵 not only cuts administrative waste by 25–30% but also scales nationally without compromising safety, empowering doctors, rideshare partners, and families in a truly free-market ecosystem. 

1. Adversarial Robustness Toolbox (ART) Purpose: Tests AI model robustness against adversarial attacks. Functionality: Open-source library by IBM for evaluating and improving model security. Ensures resilience for high-stakes systems like lotteries or healthcare AI. Alignment: Supports NIST MANAGE (security risks); strengthens federal compliance.
2. AI Now Institute’s Algorithmic Impact Assessment Purpose: Evaluates societal consequences of AI systems. Functionality: Framework for assessing equity, fairness, and public welfare impacts, particularly in public-sector applications. Ideal for American Dream Lottery🏠’s societal impact. Alignment: Aligns with NIST MAP/MEASURE; supports OMB equity requirements.Fairlearn Purpose: Mitigates fairness-related risks in AI models. Functionality: Open-source Python library with algorithms and metrics to assess and reduce bias, ensuring equitable outcomes. Critical for American Dream Lottery🏠 to prevent discriminatory allocations. Alignment: Supports NIST MEASURE (fairness metrics); auditable for federal use.
3. Google’s Differential  Privacy Library Purpose: Protects sensitive data in AI models. Functionality: Open-source library implementing differential privacy by adding controlled noise to datasets/outputs. Ensures privacy for HumanCare🩵’s sensitive health data. Alignment:  Aligns with NIST Privacy Framework and MANAGE function.
4. Google’s What-If Tool Purpose: Analyzes and visualizes bias in AI models. Functionality: Interactive, open-source tool integrated with TensorFlow. Enables counterfactual analysis and fairness exploration, supporting audits for both projects. Alignment: Supports NIST MAP/MEASURE; enhances transparency.
5. IBM’s AI Fairness 360 Toolkit Purpose: Detects and mitigates bias in machine learning models. Functionality: Open-source Python library with metrics, algorithms, and visualizations to ensure fairness across protected groups. Supports equitable outcomes for American Dream Lottery🏠. Alignment: Aligns with NIST MEASURE; widely adopted for bias audits.
6. LIME (Local Interpretable Model-Agnostic Explanations) Purpose: Enhances AI model transparency. Functionality: Open-source tool explaining individual predictions by approximating complex models with interpretable ones. Supports audits for stakeholder trust in both projects. Alignment: Supports NIST MEASURE (transparency); auditable for federal use.
7. Microsoft’s Presidio Purpose: Protects sensitive data in AI systems. Functionality: Open-source tool for detecting and anonymizing PII in text/datasets. Ensures compliance with privacy regulations for HumanCare🩵. Alignment: Aligns with NIST Privacy Framework and MANAGE function.
8. SHAP (SHapley Additive exPlanations) Purpose: Interprets AI model predictions. Functionality: Open-source tool using game theory to assign feature importance scores, enhancing transparency and trust. Supports model audits for both projects.  Alignment: Supports NIST MEASURE; ensures explainable outcomes.

1. Human Rights Impact Assessments (HRIAs) Purpose: Evaluates AI impacts on fundamental human rights (e.g., privacy, non-discrimination). Functionality: Offers a systematic approach to ensure AI aligns with international human rights standards. Critical for high-stakes applications like lotteries or healthcare. Alignment: Aligns with NIST GOVERN and State Department’s AI-Human Rights Profile. 
2. IEEE's Ethically Aligned Design Purpose: Ensures AI systems prioritize human well-being and ethical principles. Functionality: Provides practical recommendations for transparency, accountability, and human-centered design. Useful for ethical governance in both projects. Alignment: Supports NIST GOVERN; referenced in federal ethical AI discussions.
3. Impact Assessment Framework for AI (IAF) Purpose: Guides ethical and societal risk assessments for AI deployments. Functionality: Provides a structured methodology to identify and mitigate risks to ethical principles (e.g., fairness, accountability) and societal values. Adaptable to U.S. contexts for client projects. Alignment: Supports NIST GOVERN/MANAGE; complements federal equity goals.
4. ISO/IEC 23894 Purpose: International standard for AI risk management. Functionality: Offers a structured approach to identify, assess, and mitigate AI safety and security risks. Ensures global compliance, supporting federal interoperability goals. Alignment: Complements NIST AI RMF MAP/MANAGE functions.
5. ISO/IEC 42001 Purpose: Establishes AI management systems for high-risk AI. Functionality: Provides guidelines for governance, safety, and ethical compliance. Frequently referenced in U.S. policy for structured risk management in client projects. Alignment: Supports NIST GOVERN; aligns with OMB M-24-10 for high-risk systems.
6. NIST AI Risk Management Framework (AI RMF 1.0) Purpose: Provides a comprehensive, voluntary framework to manage AI risks across the lifecycle. Functionality: Structured around four core functions, Govern (oversight), Map (contextual risk identification), Measure (quantitative/qualitative risk assessment), and Manage (risk mitigation). Includes supporting resources like the AI RMF Playbook, Roadmap, and Generative AI Profile (2024). Essential for federal  compliance and adaptable for clients (equity). Alignment: Core federal guidance; mandated for U.S. agency AI use under OMB M-24-10.
7. NIST Privacy Framework Purpose: Helps organizations assess and manage privacy risks in AI systems. Functionality: Offers guidelines for implementing privacy controls, ensuring compliance with regulations, and protecting user data. Integrates with AI RMF to address sensitive data risks in Safety Kaizen, LLC and clients. Alignment: Supports NIST MAP/MANAGE functions; critical for health data privacy.
8. OECD AI Principles and Framework for AI System Classification Purpose: Provides a risk-based approach to classify and assess AI systems. Functionality: Helps prioritize assessments based on risk levels (e.g., high-risk for clients, limited-risk for low-stakes systems). Internationally aligned, supporting U.S. policy interoperability. Alignment: Supports NIST MAP; enhances risk prioritization for both projects.
9. OMB Memorandum M-24-10 (2024) Purpose: Guides federal agencies in AI governance and risk management. Functionality: Requires AI use case inventories, risk assessments, and mitigation plans for high-impact systems. Provides minimum practices for safety and rights-impacting AI (e.g., lotteries, healthcare). Alignment: Mandates NIST AI RMF adoption; directly relevant to federal assessments.
10. State Department’s Risk Management Profile for AI and Human Rights (2024) Purpose: Assesses AI impacts on human rights in public and private deployments. Functionality: Builds on NIST AI RMF and HRIAs to evaluate risks to privacy, non-discrimination, and freedom of expression. Ideal for ensuring Safety Kaizen, LLC and client projects uphold equity. Alignment: Complements NIST GOVERN/MAP; U.S.-specific human rights focus.

Operational KPIs

• HHS App Uptime and Processing Speed: Percentage of time the app is available and average time to book an appointment or process a claim. 
• Benchmark/Target: 99.5% uptime(based on Uber-like apps); <30 seconds for bookings (CMS app benchmarks).  
• Monitoring Method: Automated dashboards with real-time logs; integrate ClearML for model monitoring. 
• Alignment: Ensures efficient service delivery, supporting fraud reduction and weekly provider payments.
• Fraud Detection Accuracy: Rate at which AI identifies fraudulent claims (e.g., using ensemble models like XGBoost and SHAP for explanations). 
• Benchmark/Target: F1-score  >0.85 (from the CMS competition entry); reduce fraud/waste ($140B annually from $2.7T in inefficiencies). 
• Monitoring Method: Monthly audits via OmniXAI reports; compare against OIG estimates. Alignment: Directly cuts $140 billion in waste/abuse, generating $1.2 trillion surplus.
• Administrative Efficiency Rate: Percentage reduction in administrative labor costs through AI automation (e.g., direct HHS billing). 
• Benchmark/Target: 70% reduction; offset 227,600-446,500 job losses with retraining programs. 
• Monitoring Method: Track via HRSA/CMS data integrations; use LIME for model transparency in automation decisions. 
• Alignment: Streamlines operations, freeing funds for care and saving families $1,667/month.

Outcome KPIs

• Uninsured Rate Reduction: Percentage of U.S. citizens with healthcare coverage. Benchmark/Target: From 10% uninsured (32M people) to 0% by end of Year 1. 
• Monitoring Method:  Annual Census Bureau surveys integrated into HHS app analytics.
• Alignment: Achieves universal access, eliminating 200,000 medical bankruptcies/year.
• Health Outcome Improvements: Composite score including reductions in preventable hospitalizations, overdose deaths, and life expectancy gains. 
• Benchmark/Target: 30% fewer hospitalizations; 20-30% fewer overdose deaths (from 81,000/year); +2-5 years for low-income groups (based on Framingham Study data).
• Monitoring Method: Track via anonymized HHS app data; use predictive analytics for trends. 
• Alignment: Delivers measurable health benefits, supporting preventive care and equity.
• Family Savings and Economic Impact: Average monthly savings per family and surplus generated. 
• Benchmark/Target: $1,667/month savings (from premium elimination); $1.2T annual surplus. 
• Monitoring Method: IRS/FICA data cross-referenced with HHS reports. 
• Alignment: Boosts consumer spending (68% of GDP) and offsets FICA increase.

Responsibility KPIs

• Bias and Equity in AI Matching: Disparity rate in appointment access or care recommendations across demographics (e.g., income, race, rural/urban).
• Benchmark/Target: <5%  disparity (using Fairlearn metrics); audited for compliance with HRIAs. 
• Monitoring Method: Regular audits with IBM AI Fairness 360; public reports on demographic outcomes. 
• Alignment: Ensures non-discrimination, aligning with human rights and equity goals.
• Transparency in AI Decisions: Percentage of AI outputs (e.g., fraud flags, recommendations) with explainable attributions. 
• Benchmark/Target: 100% explainability (via SHAP/LIME); address risks per NIST AI Risk Management  Framework. 
• Monitoring Method: Integrated into HHS app; annual IEEE Ethically Aligned Design reviews. 
• Alignment: Builds trust supports HIPAA compliance, and mitigates biases in personalized care.
• User Satisfaction and Privacy Compliance: Net Promoter Score (NPS) for app users and rate of privacy incidents. 
• Benchmark/Target: NPS >70 (public sector benchmark); 0 major breaches (using Google's Differential Privacy Library). 
• Monitoring Method: User feedback surveys; NIST Privacy Framework audits. 
• Alignment: Enhances public impact, protecting sensitive data in telemedicine and  prescriptions.