Mechanical Governance for LLM Decisions — model-agnostic governance regimes (R1/R2/R3), hard gates, entropy commit-reveal and governance metrics for high-stakes LLM decision systems.
A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.
A long-form article introducing the Twin Test: a practical standard for high-stakes machine learning where models must show nearest “twin” examples, neighborhood tightness, mixed-vs-homogeneous evidence, and “no reliable twins” abstention. Argues similarity and evidence packets beat probability scores for trust and safety.
MIDAS is an open platform for governing execution authority at decision surfaces across agents, AI systems, and enterprise workflows.
Longform article reframing abstention (reject option / selective prediction) as product design, not model weakness. Covers coverage as a KPI, calibration as a prerequisite, threshold selection under review capacity and risk, queue/UX design for human-in-the-loop workflows, and anti-patterns that break safety in production.
Personal GitHub profile README showcasing production ML, GenAI/RAG systems, LLM observability, and decision-ready AI workflows.
Defines the decision layer for AI systems where deployment outcomes are governed, recorded, and reconstructable. 5th conforming implementation of draft-farley-acta-signed-receipts (IETF).
Decision Infrastructure System is an experimental platform for evaluating the governability of AI-enabled systems. It measures whether AI workflows remain influenceable, explainable, and correctable throughout execution using a presentation-driven architecture and deterministic assessment pipeline.
Deterministic governance system for AI-driven marketing that separates diagnostics, human reasoning, and execution into strictly controlled layers.
Open-source framework for Decision Traces in complex decision systems. Providing a verifiable audit trail for observable, explainable, and humane choices in software and agentic engineering
Event-driven NLP governance architecture using FastStream, Redpanda, and PostgreSQL with auditability, human-in-the-loop control, and ethical safeguards.
Computational framework for Constraint-Driven Stability with scientific toy models and applied AI decision surfaces.
Governed AI-assisted decision workflow for regulatory authoring with validation, review orchestration, and auditability
Stock redistribution and fairness-based transfer recommendations (Excel/VBA prototype).
Control-plane architecture for AI & agentic systems: governance as admission control, decision admissibility, and audit-grade evidence.
Public evidence window for Portotify runtime decision governance across AI, LLM, ML, algorithmic and hybrid decision systems. Frozen replay, governance boundaries and benchmark evidence.
Turn-based control architectures
Defines the Selection Layer — the decision system through which AI models determine visibility, inclusion, and recommendation.
Product briefs for AI/deterministic systems that make consequential decisions on unstructured input in regulated environments — research data, health, finance. Each project states the decision, the constraints, the metric, and the human escalation path.
Multi-agent orchestration system with DAG-based reasoning, tool abstraction, and observable decision traces.
Add a description, image, and links to the decision-systems topic page so that developers can more easily learn about it.
To associate your repository with the decision-systems topic, visit your repo's landing page and select "manage topics."