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# AI Accountability Design Patterns
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A practical pattern library for designing human accountability into AI-enabled systems.
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[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
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[![Last Commit](https://img.shields.io/github/last-commit/simaba/ai-accountability-design-patterns)](https://github.com/simaba/ai-accountability-design-patterns/commits/main)
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## Why this repository exists
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A practical pattern library for designing human accountability into AI-enabled systems — covering escalation logic, ownership models, and intervention paths.
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AI systems often fail operationally not only because of model behavior, but because:
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- escalation logic is vague,
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- ownership is fragmented,
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- humans are nominally "in the loop" but lack authority,
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- override paths are under-specified.
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---
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This repository collects reusable accountability design patterns for regulated, enterprise, and safety-adjacent environments.
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## Why this exists
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## Contents
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AI systems often fail operationally not only because of model behaviour, but because:
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- `patterns/human-override.md`
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- `patterns/escalation-thresholds.md`
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- `patterns/ownership-models.md`
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- `patterns/decision-context.md`
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- `patterns/incident-accountability.md`
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- `diagrams/accountability-flow.mmd`
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- `examples/customer-support-agent.md`
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- `examples/ivi-assistant.md`
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- `templates/accountability-review-checklist.md`
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- escalation logic is vague or missing
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- ownership is fragmented across teams
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- humans are nominally "in the loop" but lack real authority
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- override paths are under-specified or untested
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## Intended audience
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---
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- AI product managers
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- platform and systems engineers
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- governance and risk leaders
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- operations and quality teams
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## Core design principle
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## Design principle
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```mermaid
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flowchart TD
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A[AI system output] --> B{Intervention
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conditions met?}
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B -->|No| C[Output delivered]
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B -->|Yes| D[Human notified
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with context]
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D --> E{Authority to
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intervene?}
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E -->|Yes| F[Human overrides
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or confirms]
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E -->|No| G[Escalate to
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authorised party]
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F & G --> H[Decision logged
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and reviewable]
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```
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Human oversight is only meaningful when:
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- intervention conditions are explicit,
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- authority is real,
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- context is sufficient,
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- decisions are logged and reviewable.
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> Human oversight is only meaningful when: intervention conditions are explicit, authority is real, context is sufficient, and decisions are logged and reviewable.
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---
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## Patterns included
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| Pattern | What it addresses |
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|---------|-----------------|
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| `patterns/human-override.md` | When and how humans can override AI decisions |
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| `patterns/escalation-thresholds.md` | Defining triggers for human escalation |
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| `patterns/ownership-models.md` | Assigning clear operational ownership |
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| `patterns/decision-context.md` | Ensuring humans have sufficient context to act |
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| `patterns/incident-accountability.md` | Post-incident ownership and review |
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---
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## Worked examples
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| Example | Industry context |
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|---------|----------------|
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| `examples/customer-support-agent.md` | AI-assisted customer service with override path |
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| `examples/ivi-assistant.md` | In-vehicle AI assistant with safety escalation |
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---
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## Templates
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- `templates/accountability-review-checklist.md` — review checklist for new AI deployments
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---
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## Who this is for
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- AI product managers designing human-in-the-loop systems
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- Platform and systems engineers implementing escalation logic
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- Governance and risk leaders in regulated industries
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- Operations and quality teams accountable for AI outcomes
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---
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## Related repositories
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This repository is part of a connected toolkit for responsible AI operations:
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| Repository | Purpose |
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|-----------|---------|
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| [Enterprise AI Governance Playbook](https://github.com/simaba/enterprise-ai-governance-playbook) | End-to-end AI operating model from intake to improvement |
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| [AI Release Governance Framework](https://github.com/simaba/ai-release-governance-framework) | Risk-based release gates for AI systems |
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| [AI Release Readiness Checklist](https://github.com/simaba/ai-release-readiness-checklist) | Risk-tiered pre-release checklists with CLI tool |
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| [AI Accountability Design Patterns](https://github.com/simaba/ai-accountability-design-patterns) | Patterns for human oversight and escalation |
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| [Multi-Agent Governance Framework](https://github.com/simaba/multi-agent-governance-framework) | Roles, authority, and escalation for agent systems |
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| [Multi-Agent Orchestration Patterns](https://github.com/simaba/multi-agent-orchestration-patterns) | Sequential, parallel, and feedback-loop patterns |
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| [AI Agent Evaluation Framework](https://github.com/simaba/ai-agent-evaluation-framework) | System-level evaluation across 5 dimensions |
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| [Agent System Simulator](https://github.com/simaba/agent-system-simulator) | Runnable multi-agent simulator with governance controls |
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| [LLM-powered Lean Six Sigma](https://github.com/simaba/LLM-powered-Lean-Six-Sigma) | AI copilot for structured process improvement |
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---
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*Shared in a personal capacity. Open to collaborations and feedback — connect on [LinkedIn](https://linkedin.com/in/simaba) or [Medium](https://medium.com/@bagheri.sima).*

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