Soon virtually every decision will involve AI. For every decision we make, the critical first step is to determine the level of AI delegation. And then select the specific Humans + AI architecture most appropriate to the decision.
This framework is the latest iteration of the AI decision-making delegation frameworks I’ve been developing and using with clients, made concise for communication and easy digestion.
My frameworks are always works-in-progress, so please share any thoughts or comments on LinkedIn to help shape the next version!! 🙏 See more Humans + AI frameworks.
AI delegation levels
Human only
Description: Decisions made solely by humans without any AI assistance.
Example: High human impact decisions such as personal healthcare choices or allocation of humanitarian aid.
Human with AI red-teaming
Description: AI is used to simulate adversarial scenarios to usefully challenge human decisions.
Example: Identifying potential risks or weaknesses in a proposed business strategy as a robustness check.
Humans + AI collaboration
Description: Humans and AI work together through decision-making processes to optimize complementary capabilities.
Example: Board members use AI in a multi-step process of exploring options and scenarios for a major strategic decision.
AI input: decision reasoning
Description: AI provides reasoning, logic, or explanations to support and improve human decisions.
Example: Analytics based-proposals for corporate ESG initiatives, with full rationale and underlying research for decision-makers.
AI input: decision analytics
Description: AI provides data analysis or insights that inform human decisions.
Example: Structured implications of data analytics presented to support decisions on marketing channel allocations.
AI recommendation
Description: AI proposes its preferred actions based on its analysis, with humans approving or using as input to their decision-making.
Example: Recommending a retail investment portfolio based on risk tolerance and financial goals, with final human decision.
AI with human-in-the-loop
Description: AI makes decisions, but humans are involved at one or more points in the process to provide input, refine, or modify decisions.
Example: Predictive maintenance with humans checking recommendations and providing feedback to improve models.
AI with human approval
Description: AI operates autonomously but requires human approval before execution.
Example: Recommendation to change suppliers in a supply chain with supporting reasons.
Conditional autonomy
Description: AI operates autonomously in normal conditions, routing to humans in the case of anomalies or unexpected situations.
Example: Responding to customer enquiries, with unclear situations or customer sentiment triggers directed to humans.
AI with exceptions
Description: AI makes decisions within its defined scope of capability, directing those that fall outside to humans.
Example: Managing accounts receivable, directing any overdue payments or disputes to humans.
AI with oversight
Description: AI operates autonomously in decision-making, but humans monitor the process and outcomes for compliance.
Example: AI for inventory management and automated ordering, with regular human review and adjustment.
Full AI delegation
Description: AI has complete autonomy in decision-making without human intervention, trusted in all scenarios.
Example: AI autonomously managing and optimizing traffic flow.
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- Source: https://rossdawson.com/framework-levels-ai-delegation-decision-making/
