Human-AI Command Interface Design Guidelines

Interactive Tutorial with Mini-Case Studies

Introduction

Welcome to the interactive tutorial on designing effective human-AI command and control interfaces! In this tutorial, we'll take a hands-on approach to exploring key design guidelines synthesized from real-world case studies and best practices. You'll have the chance to see the guidelines in action through interactive prototypes and mini-case studies that illustrate core concepts.

As you go through each guideline, think about how you might apply these principles in your own C2 interface designs for AI-enabled military systems. Consider the unique challenges and opportunities of the specific operational context you are designing for. The key is to create interfaces that harness the power of AI while ensuring meaningful human control and situational understanding.

Interactive Design Guidelines

1
Calibrate Automation to Context

Effective interfaces enable dynamic delegation of tasks to the AI based on the complexity, time pressure, risk level, and human workload of the situation. Explore this concept in a mini-case study on an adaptive AI tasking system for a UAV ground control station:

UAV Flexible Autonomy Manager

The UAV control interface features an "Autonomy Dial" that allows the operator to quickly adjust the AI's level of control based on the phase of flight and anticipated challenges. During complex urban operations, the operator keeps the autonomy level lower to maintain closer oversight. But when the UAV is in transit over empty terrain, they can dial up autonomy to reduce monitoring workload and focus on mission planning.

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2
Express Intent, Not Just Commands

Enable operators to communicate higher-level goals, priorities and constraints to the AI rather than micromanaging every action. The AI can then reason about how best to achieve the human's intent within specified boundaries. See an example of an intent-based interface for controlling an autonomous ground vehicle:

AGV Commander's Intent Panel

Rather than plotting every waypoint, the interface allows the operator to designate target locations and assign intents like "Reach Objective Alpha by 0900, minimize exposure, and avoid civilian areas." The AI plans an optimized route that satisfies the commander's intent. The operator can refine the intents or set keep-out zones as needed.

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3
Provide Focused Situational Awareness

Keep the human in the loop with clear, timely updates on autonomous agent status and actions without overwhelming them with raw data. This example shows an event-based notification system in an underwater unmanned vehicle (UUV) control interface:

Intelligent UUV Status Panel

The AI monitors the UUV's sensor feeds and internal states, looking for operationally significant events like detection of an enemy submarine or critical battery level. It pushes concise notifications to the status panel, with additional details available on demand. This attention management approach ensures the operator has necessary situation awareness without constant vigilance.

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4
Make AI Behaviors Transparent & Predictable

Help cultivate appropriate operator trust by providing visibility into the AI's capabilities, limitations, and decision rationale. Users should be able to anticipate how the AI will behave in different situations. This mini-case looks at predictability mechanisms in a missile defense C2 interface:

Interceptor Allocation Proposal Display

When the AI recommends an interceptor allocation against incoming threats, it displays a concise summary of the reasoning behind its proposal, citing factors like intercept geometry, probability of kill, and potential blast radius. It also visualizes projected engagement outcomes to help the operator quickly understand and validate the AI's decision logic before approval.

Expand Diagram
Flow diagram showing how the missile defense AI's interceptor allocation proposal UI takes in threat data and constraint parameters, generates an optimized engagement plan with supporting rationale, presents the proposal to the human operator for approval or adjustment, and only then sends the commands to the interceptors, closing the loop by displaying kill assessment data back to the human.
5
Enable Seamless Human Intervention

Humans must be able to quickly and easily redirect or halt AI actions that threaten mission success or violate rules of engagement. Careful interface design can ensure human operators remain in control while minimizing friction. Here's an intervention scenario using a directed energy weapon (DEW) system:

AI DEW Human Override

The DEW AI nominates targets and firing solutions to maximize counter-UAS effectiveness. The human operator monitors these recommendations and can intervene at any time through intuitive interactions like designating restricted fire areas on the map or tapping a "Reject" button on specific firing proposals. If communications are lost, the AI automatically switches to a fail-safe mode.

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Putting It All Together

The key to applying these guidelines effectively is tailoring them thoughtfully to your specific use case and military context. There is no universal one-size-fits-all solution. By putting the human at the center of your design process, analyzing real-world workflow and decision making, and rapidly prototyping and testing concepts, you can create C2 interfaces that empower operators and AI to collaboratively achieve mission objectives.

Designing the next generation of human-AI command interfaces is a complex, multidisciplinary challenge at the intersection of HCI, AI, human factors, and military innovation. It requires grappling with not only tough interaction design problems but also the legal, ethical, and societal implications of increased machine autonomy in warfare.

"We must pursue AI-enabled capabilities that strengthen human-machine collaboration and decision making in warfighting... We will design human-machine interfaces that improve human performance and are resilient to surprise."
- U.S. Dept. of Defense AI Strategy, 2019

To dive deeper into this critical domain, explore the additional case study reports and academic papers in the MCOAI resource library. You can also share your own case studies, prototypes, and lessons learned with the broader military AI community of interest to advance the state of the art. Together, we can design the human-AI teaming approaches needed to maintain strategic and operational advantage on the battlefields of the future.