RE:CZ

How to Address Human Desire for Control: On Controllable Trust in Human-Machine Collaboration

AI Research

👤 Software engineers, AI researchers, human-computer interaction designers, complex system managers, professionals interested in autonomous systems and trust building
This paper explores the root causes of human desire for control in human-machine collaboration, arguing that it stems from rational concerns about loss of control over outcomes. To address this, the article introduces the concept of 'controllable trust' and constructs a two-layer multiplicative model: the foundational layer is intent alignment (including expression, value, dynamic, and structural alignment), and the execution layer is the risk control triangle (predictability, intervenability, and recoverability). The article further reveals the fractal recursive structure of intent alignment and proposes a 'well-organized agents' implementation framework, making agent organizations a mirror of human intent. This framework shifts the human role from operator to architect and governor, allowing the desire for control to be exercised at a higher level, thereby liberating productivity and enabling scalable collaboration.
  • ✨ The desire for control stems from rational human concerns about loss of control over outcomes, not from a fixation on power.
  • ✨ Controllable trust is key to liberating the desire for control and achieving scalable productivity in human-machine collaboration.
  • ✨ Controllable trust consists of a two-layer multiplicative model formed by intent alignment and the risk control triangle.
  • ✨ Intent alignment has a fractal recursive structure, requiring self-similar alignment across multiple scales.
  • ✨ Proposes a 'well-organized agents' framework, making agent organizations a mirror of the fractal intent structure.
📅 2026-01-05 · 1,574 words · ~7 min read
  • Human-machine collaboration
  • Controllable trust
  • Desire for control
  • Intent alignment
  • Fractal structure
  • Agent organization
  • Risk control
  • Autonomy boundaries

Table of Contents

How to Address Human Desire for Control: On the Issue of Controllable Trust in Human-Machine CollaborationAbstract1. Problem Background1.1 The Dilemma of the Desire for Control1.2 Limitations of Existing Research1.3 The Core of the Problem2. Core Arguments and Supporting Evidence2.1 Core Argument2.2 Supporting Evidence 1: The Two-Layer Multiplicative Model of Controllable Trust2.3 Supporting Evidence 2: The Fractal Recursive Structure of Intent Alignment2.4 Supporting Evidence 3: The "Well-Organized Agent" Implementation Framework2.5 Supporting Evidence 4: The Practical Path to Liberating the Desire for Control3. Conclusion

How to Address Human Desire for Control: On the Issue of Controllable Trust in Human-Machine Collaboration

2026-01-05

Abstract

With the widespread application of agents in software engineering and other complex domains, a core contradiction in human-machine collaboration has become increasingly prominent: due to concerns about uncertainty and potential risks, humans tend to maintain excessive control over machines, which severely constrains collaboration efficiency and the scalable expansion of systems. This paper posits that the key to solving this problem lies in constructing "controllable trust"—a trust model based on systematic safeguard mechanisms that allows humans to confidently delegate authority under the premise of controlled risk. We propose, for the first time, a two-layer multiplicative model of controllable trust: the foundational layer of intent alignment ensures consistency between human and machine goals, while the execution layer's risk control triangle (Predictability × Intervenability × Recoverability) guarantees operational safety. Furthermore, we reveal the fractal recursive structure of intent alignment and propose an implementation framework for "Well-Organized Agents," making the organizational structure of agents a mirror of the fractal nature of human intent, thereby guaranteeing alignment across all scales from strategy to operation through mechanisms. This paper provides a systematic theoretical framework and engineering pathway for designing the next generation of human-machine collaborative systems.

Keywords: Human-Machine Collaboration; Controllable Trust; Desire for Control; Intent Alignment; Fractal Structure; Agent Organization; Risk Control; Autonomy Boundary

1. Problem Background

1.1 The Dilemma of the Desire for Control

In the fields of agent-driven software engineering and complex system management, human-machine collaboration is transitioning from a "tool use" paradigm to an "autonomous collaboration" paradigm. However, the human desire for control—the tendency to maintain close monitoring and intervention over decision-making and execution processes—has become a major bottleneck for scalable collaboration. This desire for control is rooted in the instinct for risk aversion in cognitive psychology: when potential consequences are uncertain, uncontrollable, or irreversible, humans instinctively tighten control, even if it means sacrificing efficiency and innovation.

1.2 Limitations of Existing Research

Existing research mostly focuses on improving autonomy at the technical level or optimizing interaction interfaces but fails to fundamentally address the issue of trust building. For example:

  • Transparency design only improves comprehensibility but does not address the fear of losing control;
  • Safety constraint mechanisms provide hard boundaries but often lead to excessive restriction of agent capabilities;
  • Progressive delegation alleviates psychological resistance but lacks systematic theoretical support.

These fragmented solutions fail to answer a fundamental question: Under what conditions will humans be truly willing to cede control to autonomous agents?

1.3 The Core of the Problem

The essence of the desire for control is not human obsession with power but rational concern about loss of control over consequences. Therefore, the core of solving the control desire problem is not to eliminate the human need for monitoring but to construct a set of systematic safeguard mechanisms that make potential risks predictable, intervenable, and recoverable, while ensuring that agent behavior remains consistent with human intent. This is precisely the fundamental challenge that the concept of "controllable trust" needs to address.

2. Core Arguments and Supporting Evidence

2.1 Core Argument

Controllable trust is the key to liberating the desire for control and achieving scalable productivity in human-machine collaboration. This trust can be systematically constructed through a two-layer multiplicative model: the upper layer ensures strategic consistency through intent alignment, and the lower layer guarantees operational safety through the risk control triangle. The realization of intent alignment requires a fractal recursive structure, ultimately achieved through "Well-Organized Agents," making the agent organization a mirror of human intent.

2.2 Supporting Evidence 1: The Two-Layer Multiplicative Model of Controllable Trust

We propose that controllable trust is formed by the multiplication of safeguard mechanisms at two levels:

2.2.1 Foundational Layer: Intent Alignment Intent alignment ensures that what the agent pursues aligns with what humans truly desire. It includes:

  • Expressive Alignment: Accurately parsing human instructions and constraints;
  • Value Alignment: Consistency between the internal utility function and human values;
  • Dynamic Alignment: Adapting to evolving intent and environmental changes;
  • Structural Alignment (New): Handling the fractal recursive relationships of intent, ensuring the connection and coordination of multi-scale intents.

Intent alignment is the strategic foundation of trust, determining whether collaboration is a positive-sum or negative-sum game.

2.2.2 Execution Layer: Risk Control Triangle The risk control triangle addresses trust at the operational level, containing three multiplicative factors:

  • Predictability: Reducing uncertainty through transparency, simulation, prediction, etc.;
  • Intervenability: Retaining veto power and dynamic adjustment capabilities at key nodes;
  • Recoverability: Ensuring error consequences are reversible and system states can be rolled back.

This triangle covers the complete timeline of risk management (before, during, and after an event). Any factor approaching zero will cause the collapse of overall trust.

Formal Model Expression:

Controllable Trust = Intent Alignment Index × Risk Control Index
Intent Alignment Index = Expressive Alignment Degree × Value Alignment Degree × Structural Alignment Degree × Dynamic Alignment Degree
Risk Control Index = Predictability × Intervenability × Recoverability

2.3 Supporting Evidence 2: The Fractal Recursive Structure of Intent Alignment

Human intent is inherently a complex network of multi-scale, multi-level structures, not flat instructions. Therefore, intent alignment must possess fractal recursive characteristics:

2.3.1 Fractality Intent exhibits self-similar structures at different levels of abstraction: strategic intent (e.g., "increase market share") recursively decomposes into tactical intent (e.g., "optimize user experience") and operational intent (e.g., "reduce page load time"). Alignment must hold simultaneously at each level and between levels.

2.3.2 Recursiveness

  • Downward Propagation: Value constraints from high-level intent are accurately transmitted to low-level operations;
  • Upward Aggregation: Execution status at low levels is effectively aggregated into high-level progress metrics;
  • Cross-Layer Consistency Checks: At key decision points, retrospective verification ensures service to the top-level intent.

2.3.3 Networked Coordination Multiple intents may be parallel or conflicting (e.g., "release quickly" vs. "ensure quality"). The structural alignment layer must possess:

  • Intent graph construction and conflict detection;
  • Dynamic resource allocation and trade-offs;
  • Global optimization with opportunity cost awareness.

2.4 Supporting Evidence 3: The "Well-Organized Agent" Implementation Framework

The theoretical model requires engineering implementation. We propose the Well-Organized Agent framework, making the agent organization a natural mapping of the fractal nature of intent:

2.4.1 Fractal Organizational Architecture The agent system is organized according to intent levels into strategic agents, tactical agent groups, and operational agent groups. Agents at each level possess local intent understanding, alignment detection, and state synchronization capabilities, forming a traceable intent execution chain.

2.4.2 Core Components

  • Intent Decomposition and Assignment Engine: Recursively decomposes top-level intent into agent tasks;
  • Cross-Agent Coordination Protocol: Handles intent consistency, resource arbitration, and progress aggregation;
  • Fractal Monitoring Dashboard: Provides multi-level visualization from macro to micro.

2.4.3 Alignment Assurance Process

  • Intent Calibration Loop: Agents help humans clarify ambiguous intent, suggesting optimal interpretations through simulation and deduction;
  • Fractal Accountability System: Each layer of agents reports contributions upward, explains tasks downward, and coordinates horizontally;
  • Dynamic Rebalancing: Proposes trade-off solutions based on top-level intent when intent conflicts are detected.

2.4.4 Safety and Evolution Mechanisms

  • Intent Sandbox Verification: Simulates and verifies alignment and collaborative effects before execution;
  • Fractal Circuit Breaker Mechanism: Independent anomaly detection and local circuit breaking at each level;
  • Organizational Learning Capability: Optimizes organizational structure and collaboration patterns from historical collaboration.

2.5 Supporting Evidence 4: The Practical Path to Liberating the Desire for Control

Under the controllable trust framework, the human role undergoes a fundamental transformation:

2.5.1 From "Operator" to "Architect" Humans focus on intent setting, value definition, and strategic adjustment, rather than detailed monitoring. The mental cost shifts from "continuous vigilance" to "periodic review," freeing cognitive resources for creative work.

2.5.2 From "Point Control" to "System Governance" Through fractal monitoring and circuit breaker mechanisms, humans no longer need to intervene in every detail but govern the operating principles and boundary conditions of the entire agent system. Control shifts from micro-operations to macro-regulation.

2.5.3 Scalable Collaboration Becomes Possible One person can supervise multiple agent teams, handling parallel task flows. Agent organizations can dynamically scale with intent complexity, achieving productivity scaling while maintaining alignment and controllability.

3. Conclusion

This paper systematically explores the root causes and solutions to the desire for control in human-machine collaboration. We argue that the desire for control is not a defect to be overcome but an instinctive response to risk. Therefore, the truly effective solution is not to eliminate the human need for control but to enable confident delegation through the construction of "controllable trust."

Our proposed two-layer multiplicative model unifies intent alignment and risk control within a single theoretical framework for the first time, clarifying the components and interrelationships of controllable trust. The further revealed fractal recursive structure addresses the fundamental challenge of multi-scale intent alignment, while the Well-Organized Agent framework provides a viable engineering implementation path for the theoretical model.

The fundamental significance of this framework lies in redefining the human-machine relationship: humans are no longer direct controllers but architects of intent and governors of the system; agents are no longer passive tools but organized, aligned executors of intent. In this new paradigm, the desire for control no longer hinders collaboration but is exercised more effectively at a higher level of abstraction—through setting goals, defining values, and adjusting boundaries.

Future research directions include: formal languages for intent graphs, optimization algorithms for alignment propagation, adaptive mechanisms for fractal organizations, and application validation in more complex domains (e.g., medical decision-making, urban planning, scientific discovery). Ultimately, when controllable trust becomes the infrastructure for human-machine collaboration, we will truly move towards a new era of deep integration between human wisdom and machine intelligence.

Acknowledgments: The conceptual formation of this paper benefited from interdisciplinary research in human factors engineering, cybernetics, complex systems theory, and cognitive psychology, as well as in-depth observation of modern software engineering practices. Special thanks are extended to pioneering work on trust building in autonomous systems for providing inspiration.

See Also

Referenced By