Teaming Up with AI: Finding Failure Points Before They Matter

Author: Denis Avetisyan

A new analysis framework proactively identifies risks in human-AI collaboration by scrutinizing the interactions within these teams.

This paper proposes a ‘left-shifting’ methodology to analyze human-autonomy team interactions for improved system safety and AI assurance.

Despite increasing reliance on artificial intelligence in high-stakes systems, proactively identifying and mitigating risks associated with human-autonomy teaming remains a significant challenge. This paper, ‘Left shifting analysis of Human-Autonomous Team interactions to analyse risks of autonomy in high-stakes AI systems’, proposes a novel framework for systematically analyzing potential failures arising from the interplay between human operators and AI, grounded in failure mode analysis and extending prior work on human-autonomy team dynamics. By shifting risk assessment earlier in the system lifecycle, our approach enables the characterization of emergent behaviours and promotes the design of more robust and reliable AI-integrated systems. Could this proactive ‘left-shifting’ methodology fundamentally reshape the development and deployment of critical AI applications?

Unveiling Systemic Fragility: Beyond Conventional Testing

The increasing complexity of modern systems, particularly those integrating autonomous agents, introduces vulnerabilities that elude conventional testing methodologies. Traditional approaches often focus on expected functionality under ideal conditions, failing to account for the nuanced interplay of components and the unpredictable nature of real-world deployment. These systems, built upon layers of software, hardware, and algorithms, can exhibit subtle failures – emergent behaviors arising from unforeseen interactions – that remain hidden during routine checks. This isn’t simply a matter of bugs; it’s a fundamental limitation of testing methods when applied to systems whose state space is too vast to explore exhaustively. Consequently, even systems passing all standard tests can demonstrate unexpected, and potentially hazardous, performance when faced with novel situations or edge cases, highlighting the critical need for more comprehensive analytical techniques.

Addressing potential vulnerabilities before deployment is paramount for ensuring the safety and dependability of complex autonomous systems. Recent research underscores the value of a systematic failure mode analysis, moving beyond reactive troubleshooting to anticipate and mitigate risks. This proactive methodology involves a detailed examination of how a system might fail – considering not just component malfunctions, but also the interplay of various factors and potential human-machine interactions. By identifying these failure modes early in the development process, designers can implement preventative measures, bolstering system resilience and minimizing the likelihood of unpredictable behavior or hazardous outcomes. This shift toward preventative analysis promises a significant advancement in the reliability of increasingly sophisticated technologies.

Truly robust failure analysis transcends purely technical assessments of a system’s components; it necessitates a holistic view encompassing the inevitable interplay between the machine and the humans who design, operate, and interact with it. Investigations must consider not only how a system malfunctions, but also why those malfunctions occur in the context of human expectations, potential misinterpretations of feedback, and the broader operational environment. A comprehensive approach probes for weaknesses in the human-machine interface, anticipating errors arising from ambiguous displays, poorly designed controls, or insufficient training. By acknowledging that failures are frequently a product of this complex relationship, researchers can move beyond simply fixing technical bugs and address the systemic vulnerabilities that contribute to unpredictable and potentially hazardous outcomes.

The absence of robust, comprehensive failure analysis in complex systems introduces a critical vulnerability to unpredictable behavior, potentially escalating into dangerous outcomes. Systems reliant on autonomous agents, while promising increased efficiency and capability, operate within environments filled with unforeseen variables and nuanced human interactions. Without a systematic approach to identifying potential failure modes – encompassing both algorithmic shortcomings and user error – these systems can exhibit emergent properties leading to erratic performance. This isn’t simply a matter of inconvenience; in critical applications like healthcare, transportation, or industrial control, such unpredictability can have severe, even catastrophic, consequences. Therefore, prioritizing proactive failure analysis isn’t merely a best practice, but a fundamental requirement for ensuring the safety and reliability of increasingly complex technologies.

Dissecting Machine Performance: Beyond Simple Accuracy

System performance evaluation extends beyond simply measuring accuracy; comprehensive assessment requires quantifying variability, stability, and robustness. Variability refers to the degree of dispersion in the system’s outputs for a given input, often expressed as standard deviation or interquartile range. Stability indicates the system’s consistency over time, reflecting its ability to maintain performance levels under consistent conditions. Robustness defines the system’s resilience to changes in input data or operating conditions; a robust system exhibits minimal performance degradation when faced with noisy or unexpected inputs. These characteristics are crucial for determining the reliability and trustworthiness of a machine learning model or automated system, as high accuracy alone does not guarantee consistent and predictable performance in real-world applications.

Quantifying uncertainty in machine learning outputs is crucial because most models do not produce deterministic results; rather, they provide probability distributions or confidence intervals reflecting the inherent ambiguity in predictions. Methods for quantifying this uncertainty include calculating prediction intervals, using Monte Carlo dropout, or employing Bayesian neural networks. Simultaneously, detecting bias-systematic errors stemming from flawed training data or algorithmic design-is equally vital. Bias manifests as consistent and unfair deviations in performance across different subgroups or inputs. Techniques for bias detection involve analyzing performance metrics disaggregated by relevant demographic groups, examining feature importance to identify potentially discriminatory variables, and employing adversarial debiasing methods to mitigate unfairness. Accurate quantification of uncertainty and diligent bias detection are not merely academic exercises; they are fundamental requirements for deploying trustworthy and reliable machine learning systems in real-world applications.

The Machine Behaviour Lens is a structured approach to evaluating machine learning system performance, moving beyond single accuracy metrics to encompass variability, stability, robustness, uncertainty, and bias. This framework utilizes a multi-dimensional assessment, employing quantitative measures for each characteristic and analyzing their interdependencies. Specifically, it advocates for defining key performance indicators (KPIs) for each quality, establishing acceptable thresholds, and implementing automated monitoring to detect deviations. The resulting profile provides a comprehensive, actionable view of system behaviour, facilitating targeted improvements and ensuring reliable outputs across diverse operational conditions.

Performance characteristics such as accuracy, variability, stability, and robustness are not independent indicators; their interrelationships define a system’s overall reliability. For example, a system exhibiting high accuracy on average may still be unreliable if its variability is also high, leading to unpredictable outputs. Similarly, stability – consistent performance over time – can mask underlying biases that affect certain subsets of inputs. A comprehensive assessment, therefore, requires analyzing these metrics in conjunction; improving one characteristic in isolation may not translate to overall performance gains and can even negatively impact others. Considering the interplay between these factors provides a more nuanced and accurate evaluation of machine behaviour than isolated metric analysis.

Decoding Human Interaction: Anticipating Use and Misuse

The Human Intent Lens is a critical analytical tool used in systems engineering to evaluate discrepancies between a system’s designed purpose and its actual implementation within a user context. This lens explicitly considers the possibility of unintended use, misuse, or even malicious abuse of functionality, moving beyond a simple assessment of correct operation. By systematically identifying potential user actions that deviate from intended parameters – including those resulting from error, circumvention, or exploitation – developers can proactively address vulnerabilities and incorporate safeguards. This approach focuses on understanding how a system can be used, not just how it was designed to be used, thereby improving overall system resilience and safety.

Comparative analysis of intended system use versus observed user behavior is a critical component of vulnerability identification. Discrepancies between design specifications and real-world interaction patterns reveal potential failure points stemming from user error, unintended consequences, or circumvention of safety mechanisms. This process involves collecting data on how users actually interact with a system-including mistakes, workarounds, and unexpected inputs-and comparing it to the documented, expected usage scenarios. The resulting insights inform iterative design improvements, allowing developers to proactively address weaknesses and build more robust interfaces that minimize the risk of misuse and enhance system resilience.

Integrating analysis of human interaction – including both intended and unintended use – with a concurrent assessment of machine behavior provides a comprehensive understanding of system performance. This combined approach allows for the identification of potential failure points stemming from either technical limitations or user-induced errors. Specifically, discrepancies between predicted machine responses and observed user actions can highlight vulnerabilities in the human-machine interface. The Machine Behaviour Lens provides data on system capabilities and limitations, while the Human Intent Lens details how users actually interact with the system; combined, these lenses facilitate a holistic risk assessment and inform the development of more resilient and user-centered designs.

Effective human-machine interface (HMI) design is critical for both system reliability and user safety. A poorly designed HMI can lead to operator error, even with technically sound system functionality, increasing the risk of accidents or failures. Prioritizing user-friendliness-through intuitive controls, clear feedback mechanisms, and minimized cognitive load-directly impacts operational efficiency and reduces the likelihood of misuse. Consequently, investment in HMI design, including usability testing and iterative refinement, is essential for creating systems that are not only capable but also consistently perform as intended in real-world applications, minimizing risks and maximizing positive outcomes.

Orchestrating Human-Autonomy Teams: The Synergy of Intelligence

Human-Autonomy Teaming (HAT) fundamentally depends on the seamless integration of human cognitive abilities with the precision and speed of automated systems. This isn’t merely about combining tasks; it’s about creating a synergistic partnership where each entity compensates for the other’s limitations. Effective coordination requires a shared understanding of goals, clear communication pathways, and a robust mechanism for resolving conflicts or ambiguities. The success of HAT hinges on designing interfaces and protocols that minimize cognitive load for human operators, allowing them to focus on higher-level decision-making and strategic oversight, while the autonomous system efficiently executes pre-defined tasks and provides timely, relevant information. Ultimately, a well-functioning HAT achieves performance levels exceeding those attainable by either humans or machines acting independently, paving the way for innovation across numerous fields.

Visualizing the interplay between humans and autonomous systems is paramount to effective team performance, and tools like Activity and OODA 2 diagrams provide a crucial means of achieving this. Activity diagrams map out the sequential flow of tasks, revealing where a human operator might be waiting for an autonomous system’s input, or vice versa – potential bottlenecks that hinder overall efficiency. Complementing this, OODA 2 diagrams – derived from the Observe-Orient-Decide-Act loop – model the dynamic decision-making process, highlighting how quickly and effectively each agent can react to changing circumstances. By mapping these interactions, researchers and designers can proactively identify miscommunications, anticipate errors, and refine the coordination between human and machine, ultimately fostering a more seamless and robust collaborative environment.

The Operational Design Domain, or ODD, represents the specific conditions under which an autonomous system is designed to function correctly. This isn’t simply a geographical area, but a comprehensive definition encompassing environmental factors – like weather, lighting, and traffic density – as well as infrastructural constraints and even the types of actors the system might encounter. A clearly defined ODD is paramount; operating outside these pre-defined parameters can lead to unpredictable behavior and system failure. Consequently, rigorous testing and validation are essential to ensure the autonomous system consistently performs as expected within its ODD, and to proactively identify the limits of its capabilities, safeguarding against hazardous scenarios and maximizing reliable operation in real-world applications.

The integration of human-autonomy teaming (HAT) promises substantial gains in both efficiency and safety, especially within the demanding landscape of air traffic control systems. By strategically allocating tasks between human controllers and autonomous agents, these systems can navigate increasing air traffic volume and complexity with greater precision. Autonomous systems excel at routine monitoring and data analysis, freeing human controllers to focus on exception handling, strategic decision-making, and unpredictable events. This collaborative approach minimizes the potential for human error, reduces controller workload, and ultimately enhances the overall safety and resilience of the airspace. The benefits extend beyond safety, with optimized flight paths and reduced delays contributing to significant economic and environmental improvements within the aviation sector.

The Future of Flight: Intelligent Landing Sequences in Action

Artificial intelligence is poised to revolutionize air travel through the implementation of landing sequence recommendation systems. These systems analyze a multitude of real-time data points – including aircraft position, velocity, weather patterns, and airspace congestion – to propose optimized landing sequences for air traffic controllers. By intelligently coordinating arrivals, these AI-driven tools promise to significantly reduce delays, minimize fuel consumption, and enhance overall airport efficiency. Moreover, the predictive capabilities of these systems can proactively mitigate potential conflicts, contributing to a substantial improvement in flight safety and a reduction in the workload for human controllers. The potential benefits extend beyond mere logistical improvements; optimized landing sequences represent a critical step towards more sustainable and resilient aviation practices.

Effective landing sequence recommendation systems are not envisioned as replacements for air traffic controllers or pilots, but rather as sophisticated support tools operating within a Human-Autonomy Team framework. These systems are designed to analyze complex data – weather patterns, aircraft performance, and airspace congestion – to suggest optimal landing orders, but critical decision-making authority remains with human operators. The architecture prioritizes transparency and explainability, allowing controllers to understand the rationale behind each recommendation and intervene when necessary, particularly in unforeseen circumstances or when exercising professional judgment. This collaborative approach ensures that automation enhances, rather than diminishes, safety and efficiency, fostering trust in the technology and maximizing its positive impact on air travel operations.

Building confidence in automated landing sequence recommendation systems necessitates a proactive approach to potential failures and a design philosophy centered on human-machine collaboration. Recent work demonstrates that integrating robust failure analysis – meticulously identifying and modeling potential system shortcomings – with collaborative modeling techniques fosters trust. This involves not simply predicting failures, but also allowing human controllers to understand how the system anticipates and reacts to them, and to seamlessly intervene when necessary. By visualizing the system’s reasoning and enabling shared decision-making, these techniques move beyond simple automation, creating a synergistic partnership where human expertise complements algorithmic precision, ultimately enhancing both safety and efficiency in increasingly complex air traffic scenarios.

The progression of aviation increasingly depends on a carefully balanced collaboration between human expertise and automated systems, a partnership designed to not only elevate performance benchmarks but also fundamentally reduce inherent risks. This isn’t about replacing pilots with algorithms; rather, it’s about augmenting their capabilities with the precision and data-handling capacity of artificial intelligence. Successful implementation necessitates a shift in focus, prioritizing systems that offer transparent reasoning and actionable insights, enabling human operators to maintain crucial oversight and intervene when necessary. Ultimately, the most effective future for air travel will be defined by this synergy, where the strengths of both humans – adaptability and critical thinking – and machines – speed and accuracy – are seamlessly integrated to achieve unprecedented levels of safety and efficiency.

The pursuit of robust human-autonomy teaming, as detailed in the analysis of interaction risks, demands a rigorous approach to understanding potential failure points. It’s not enough to simply build a system and hope for the best; one must actively probe its boundaries. This echoes Barbara Liskov’s sentiment: “Programs must be right first before they are fast.” The article’s focus on ‘left-shifting’ – identifying risks before they manifest – directly embodies this principle. By meticulously examining the OODA loop interactions and potential divergences, the framework strives for correctness in system design, ensuring that speed and efficiency don’t come at the cost of safety and reliability. The goal isn’t merely to create a functional team, but one demonstrably resistant to unforeseen errors.

Beyond the Horizon

The framework detailed herein isn’t about preventing failure-that’s a comforting illusion. It’s about locating the inevitable points of stress before they propagate through a human-autonomy system. The focus on interaction, on the ‘left shift,’ acknowledges a fundamental truth: the code isn’t in the algorithm, it’s in the interface. Reality is open source – one simply hasn’t read the code yet. Future work must therefore move beyond purely technical metrics and grapple with the messiness of human cognition, anticipating not just what an AI might do, but how a human will interpret that action, and react.

A crucial, largely unaddressed limitation lies in scaling this analysis. The OODA loop, while elegant, becomes computationally intractable when modeling complex, multi-agent systems. The next iteration requires moving beyond qualitative assessment and developing automated methods for identifying and prioritizing potential failure modes, ideally leveraging machine learning to predict emergent behaviors. However, this presents a paradox: using an AI to assess the safety of another AI risks simply embedding the same vulnerabilities at a higher level of abstraction.

Ultimately, the goal isn’t to build ‘safe’ AI, but understandable AI. A system’s failure isn’t a bug; it’s a revelation. Each incident is a chance to reverse-engineer a small piece of reality’s source code, and refine the model. The true metric of success won’t be the absence of errors, but the speed with which those errors can be diagnosed and corrected, turning failures into opportunities for genuine progress.

Original article: https://arxiv.org/pdf/2512.03519.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/