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Agency

As explained in Delivering Machine Learning Projects: From design to deployment

Agency is the capacity of a person or system to make and carry out decisions that affect an unfolding situation.

Agency is the capacity to make and carry out decisions that affect an unfolding situation. In a delegative system, human agency is meaningful only when control returns early enough for the person to prevent or repair harm. section 9.2.2

Design delegative controls to preserve agency:

  • Return control with sufficient time to respond, not after the system has made the outcome unavoidable. section 9.2.2
  • Give the human genuine authority to make decisions that can avoid disaster. section 9.2.2
  • Do not treat a delayed handoff as a failsafe for an inadequately demonstrated autonomous system. section 9.2.2

When control arrives too late

A nurse alerted only after a life-support failure causes cardiac arrest has no practical agency. Similarly, engaging an aircraft pilot after a control system has placed the aircraft into an unrecoverable stall turns delegation into failure. section 9.2.2

Figure 9.3 The control flow for a delegative remote control rover [1]

Trace the control flow of a delegative remote-control rover. section 9.2.2

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Defines agency through intervention timing and authority, while deferring detailed requirements for autonomous-system development.
Examples
Uses life-support control, aircraft stall intervention, and a remote-control rover control-flow diagram.
What you can do after
Evaluate whether a human handoff occurs early enough and grants enough authority to prevent harm.
How it connects
Connects agency with delegative systems, human oversight, failsafes, and autonomous systems.

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This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.