Beliefs and Algorithms: When AI Advice Goes Wrong

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Author: Denis Avetisyan

A new framework reveals how an individual’s existing beliefs can undermine even the most accurate AI-powered decision support systems.

A treatment model iteratively refines its understanding of a patient population through Bayesian updates-informed by observed patient data and prior predictions-to estimate treatment effects and ultimately determine optimal interventions, linking patient attributes and treatment choices to predicted outcomes.
A treatment model iteratively refines its understanding of a patient population through Bayesian updates-informed by observed patient data and prior predictions-to estimate treatment effects and ultimately determine optimal interventions, linking patient attributes and treatment choices to predicted outcomes.

This review introduces the 2-Step Agent framework for modeling the interaction between a decision maker’s prior beliefs and Machine Learning Decision Support systems, highlighting the risks of misalignment and the importance of understanding model uncertainty.

Despite increasing reliance on artificial intelligence for decision support, a fundamental understanding of how these systems interact with human cognition remains elusive. This paper introduces the ‘2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support’, a computational model employing Bayesian methods and causal inference to analyze the effects of AI-assisted decision-making. Simulations reveal that even optimally predictive models can lead to suboptimal outcomes if the agent’s prior beliefs are misaligned, highlighting the critical role of initial assumptions. Consequently, how can we best design and implement AI decision support systems to mitigate the risks of flawed priors and ensure genuinely beneficial outcomes?

Beyond Prediction: The Need for Causal Reasoning

A significant limitation of many current machine learning systems lies in their opacity – they excel at predicting outcomes, but offer little insight into the reasoning behind those predictions. While achieving high accuracy is often prioritized, this focus neglects a crucial element for responsible and robust artificial intelligence. Deployments frequently treat models as ‘black boxes’, accepting predictions without understanding the underlying factors driving them. This lack of interpretability poses challenges in high-stakes scenarios-such as healthcare, finance, or autonomous driving-where trust and accountability are paramount. The inability to explain why a particular prediction was made hinders debugging, exposes systems to unforeseen biases, and ultimately limits their capacity to adapt to novel or changing circumstances.

Effective decision-making in complex scenarios demands more than just accurate predictions; agents must possess the ability to understand why certain outcomes are likely. While predictive models excel at identifying patterns, they often fall short when faced with unforeseen circumstances or the need for intervention. An agent operating solely on prediction may correctly forecast a negative event but lack the insight to prevent it, or worse, initiate actions that inadvertently exacerbate the problem. Causal reasoning, conversely, allows an agent to model the underlying mechanisms driving events, enabling it to not only anticipate outcomes but also to evaluate the consequences of different actions and select interventions that achieve desired results. This capacity is particularly critical in high-stakes domains – such as healthcare, finance, and autonomous systems – where understanding the causal relationships between variables is essential for responsible and effective operation.

A fundamental challenge in artificial intelligence lies in distinguishing between correlation and causation, a misstep that can lead to demonstrably flawed reasoning and potentially adverse consequences. Many systems, when presented with data, identify patterns without understanding the underlying mechanisms driving those patterns; a system might observe that ice cream sales and crime rates rise in tandem, but incorrectly infer that one causes the other. This conflation is particularly pronounced when an agent-be it a software program or a robot-operates with pre-existing, but inaccurate, beliefs about how the world functions. Recent work highlights that such incorrect prior beliefs can systematically distort an agent’s ability to learn causal relationships, leading to persistent errors even when presented with abundant evidence, and demonstrating the critical need for systems capable of robust causal inference rather than mere pattern recognition.

Following an update with model-based learning from demonstration signals, the agent maintains a distribution over possible states, as reflected in the correlation between [latex]N_{E}[/latex] and [latex]\mu_{A}[/latex] within its posterior beliefs.
Following an update with model-based learning from demonstration signals, the agent maintains a distribution over possible states, as reflected in the correlation between [latex]N_{E}[/latex] and [latex]\mu_{A}[/latex] within its posterior beliefs.

A Two-Step Framework for Rational Agents

The 2-Step Agent Framework structures decision-making as a sequential process involving Bayesian updating and causal inference. Bayesian updating, the initial phase, refines an agent’s internal beliefs regarding the state of the environment based on observed data and a predictive model. This step generates a posterior probability distribution representing the agent’s revised understanding. Subsequently, the causal inference phase leverages these updated beliefs – the posterior – to determine the optimal action to take. This decomposition contrasts with single-step approaches and allows for explicit separation of belief revision from action selection, improving interpretability and control over the agent’s behavior.

The Bayesian Update Step involves revising an agent’s prior beliefs about the world given new evidence generated by a predictive model. This process utilizes Bayes’ Theorem, where the posterior probability distribution – representing updated beliefs – is calculated based on the likelihood of observing the evidence given the prior beliefs, and the prior probability of those beliefs. Specifically, the agent compares its predictions from the predictive model with actual observations; discrepancies between these values inform the adjustment of the probability assigned to different states of the world. This iterative process allows the agent to refine its internal representation of the environment based on incoming data, effectively learning from experience and improving the accuracy of its future predictions.

The Causal Inference Step utilizes the agent’s updated beliefs, generated during the Bayesian Update Step, to determine the optimal action. This process involves evaluating potential actions based on their predicted outcomes, considering the agent’s internal model of how the world functions. The agent estimates the causal effect of each action, quantifying the likely impact on relevant variables. This evaluation isn’t simply correlational; it aims to identify actions that will directly cause desired changes, enabling the agent to select the action maximizing expected utility given its current understanding of the environment and its goals. The output of this step is a determined action to be executed.

Separating decision-making into Bayesian updating and causal inference enhances the agent’s ability to model the data generating process by explicitly distinguishing between prior beliefs and observed evidence. This decoupling is critical for robust performance under uncertainty because it allows the agent to accurately assess the impact of new information, preventing overconfidence in potentially flawed priors. Specifically, the framework mitigates harmful effects stemming from prior-data mismatch by providing a mechanism to revise beliefs based on empirical evidence, reducing the risk of perpetuating biases present in the initial training data and improving generalization to novel situations. This approach facilitates more informed action selection by grounding decisions in an updated and empirically validated understanding of the environment.

Using ML-DS (orange) improves the agent's ability to accurately estimate CATE and positively impacts downstream outcomes [latex]Y[/latex], as evidenced by the posterior belief (within 1 standard deviation of the true mean, shown in gray) remaining closely aligned with the historical SCM's parameter [latex]\mu_{Y}[/latex] even with deviations in prior beliefs.
Using ML-DS (orange) improves the agent’s ability to accurately estimate CATE and positively impacts downstream outcomes [latex]Y[/latex], as evidenced by the posterior belief (within 1 standard deviation of the true mean, shown in gray) remaining closely aligned with the historical SCM’s parameter [latex]\mu_{Y}[/latex] even with deviations in prior beliefs.

Building Predictive Capacity with Historical Models

The predictive model is a core element of the 2-Step Agent Framework, functioning as a learned representation of the data generating process. This model is specifically trained on a Historical Structural Causal Model (SCM), which encapsulates the assumed relationships between variables and their causal influences. The SCM provides the framework with a structured understanding of how data is generated, allowing the predictive model to forecast outcomes and, subsequently, inform counterfactual reasoning. Utilizing a historical SCM for training ensures the model’s predictions are grounded in observed data and reflects the established causal structure, rather than relying on purely correlational patterns.

The Plate Model is implemented to improve the efficiency and scalability of predictive modeling within the 2-Step Agent Framework. This technique achieves optimization by representing complex probabilistic relationships with a compact graphical structure, allowing for the sharing of parameters across multiple variables. Specifically, the Plate Model facilitates the efficient calculation of probabilities by factoring the joint distribution into a product of conditional distributions, thereby reducing computational demands and memory requirements. This parameter sharing is particularly beneficial when dealing with high-dimensional data or large sample sizes, as it significantly reduces the number of parameters that need to be estimated and stored. The use of shared distributions also promotes generalization and prevents overfitting, leading to more robust and scalable predictive models.

The Plate Model incorporates specific probability distributions to effectively represent characteristics of the observed data. The Normal Product Distribution is utilized to model variables that are products of normally distributed random variables, accommodating scenarios where multiplicative effects are present. Conversely, the Chi-Squared Distribution is employed to model variables representing the sum of squared normally distributed random variables, frequently arising in contexts involving variance estimation or goodness-of-fit tests. These distributions, parameterized within the Plate Model, allow for a flexible representation of data characteristics and facilitate accurate predictive modeling by capturing the underlying statistical properties of the Historical SCM.

Ordinary Least Squares (OLS) is utilized for parameter estimation within the predictive model, offering a computationally efficient and statistically well-understood approach. Empirical results indicate the 2-Step Agent Framework can successfully correct Causal Average Treatment Effect (CATE) estimates when initial prior beliefs about the data generating process are inaccurate. However, performance is sensitive to the degree of misalignment between prior beliefs and the true data characteristics; substantial discrepancies can lead to a degradation in CATE estimation accuracy, and in some cases, produce estimates further from the true value. This suggests careful consideration of prior specification and potential sensitivity analysis are crucial for reliable results.

Using machine learning to debias agent priors (orange) improves the accuracy of causal effect estimation (CATE) and downstream outcome prediction [latex]YY[/latex] across varying degrees of prior misalignment with the historical treatment policy [latex]\mu_A[/latex] and covariate distribution [latex]\mu_X[/latex], as indicated by the reduced uncertainty (gray areas) around the true CATE compared to unbiased priors (blue).
Using machine learning to debias agent priors (orange) improves the accuracy of causal effect estimation (CATE) and downstream outcome prediction [latex]YY[/latex] across varying degrees of prior misalignment with the historical treatment policy [latex]\mu_A[/latex] and covariate distribution [latex]\mu_X[/latex], as indicated by the reduced uncertainty (gray areas) around the true CATE compared to unbiased priors (blue).

Towards Robust and Reliable Artificial Intelligence

The 2-Step Agent Framework represents a shift in AI design, moving beyond purely predictive models to prioritize interpretability and trustworthiness. This approach structures AI systems as agents with explicit, learnable beliefs, enabling a more transparent decision-making process. Unlike traditional ‘black box’ AI, this framework decomposes complex tasks into two distinct stages: first, an agent forms beliefs about the environment and potential outcomes, and second, it acts based on those beliefs. This separation allows for direct examination of the agent’s reasoning, identifying potential biases or flawed assumptions that might otherwise remain hidden. By explicitly modeling the agent’s internal state, the framework not only enhances understanding of why an AI makes a particular decision, but also provides a pathway for correcting errors and improving the reliability of future actions – crucial for applications demanding accountability and safety.

The foundation of reliable artificial intelligence increasingly relies on acknowledging and incorporating an agent’s pre-existing knowledge – its prior beliefs. Rather than operating as a blank slate, an AI system benefits from a clearly defined understanding of initial expectations regarding the world and potential outcomes. Explicitly modeling these prior beliefs allows the system to systematically update its understanding as new data becomes available, actively mitigating the influence of inherent biases present in training data or algorithmic design. This approach enhances robustness, particularly when faced with unexpected or incomplete information, as the agent doesn’t solely rely on observed correlations but leverages its established understanding to make informed decisions. Consequently, incorporating agent prior beliefs represents a critical step towards building AI systems capable of consistently delivering accurate and trustworthy results, even in complex and dynamic environments.

A key finding of this research demonstrates the 2-Step Agent Framework’s capacity to discern and refine an agent’s underlying beliefs. Analysis revealed a strong correlation between the nuanced treatment effect – the actual impact of an intervention – and the historical policy that guided prior decisions, particularly after the agent interacted with the Machine Learning-Decision System (ML-DS). This isn’t merely a statistical observation; it signifies that the framework can effectively map how an agent’s beliefs are shaped by new data and experiences. The ability to track this relationship between treatment effect and historical policy provides a crucial diagnostic tool, enabling researchers to understand why an agent makes certain decisions and to identify potential biases or inaccuracies in its belief system. This dynamic interplay between observed outcomes and pre-existing beliefs is central to building AI systems that are not only intelligent but also transparent and accountable.

The potential for flawed assumptions within artificial intelligence carries substantial risk across critical sectors. In healthcare, an AI system operating on incorrect beliefs about patient demographics or treatment efficacy could deliver suboptimal or even harmful care. Similarly, within financial systems, misconstrued historical data or inaccurate modeling of market responses could lead to flawed investment strategies and systemic instability. Autonomous systems, reliant on perceptions of their environment and the likely effects of actions, are particularly vulnerable; an incorrect assessment of road conditions or pedestrian behavior, for example, could have catastrophic consequences. The 2-Step Agent Framework addresses this by explicitly modeling agent beliefs, offering a pathway to mitigate these risks and foster more reliable, trustworthy AI decision-making in these high-stakes domains.

Using machine learning to debias (ML-DS, orange) improves the accuracy of posterior beliefs about causal treatment effects [latex]NEN_E[/latex], past treatment policies [latex]\mu_A[/latex], past outcomes [latex]\mu_Y[/latex], and covariate distributions [latex]\mu_X[/latex] relative to unbiased estimates (blue), as demonstrated by the concentration of beliefs around the true CATE within one standard deviation.
Using machine learning to debias (ML-DS, orange) improves the accuracy of posterior beliefs about causal treatment effects [latex]NEN_E[/latex], past treatment policies [latex]\mu_A[/latex], past outcomes [latex]\mu_Y[/latex], and covariate distributions [latex]\mu_X[/latex] relative to unbiased estimates (blue), as demonstrated by the concentration of beliefs around the true CATE within one standard deviation.

The study of agent-ML-DS interaction reveals a fundamental truth about complex systems: optimization in one area invariably introduces tension elsewhere. This echoes Marvin Minsky’s observation: “The more we learn about intelligence, the more we realize how much of it is just good bookkeeping.” The ‘2-Step Agent’ framework demonstrates this principle by showing how even perfectly accurate ML decision support can exacerbate problems stemming from misaligned prior beliefs. The system’s behavior over time isn’t simply about the quality of the ML model; it’s a consequence of the interplay between the agent’s existing framework and the information provided, highlighting that a robust system requires careful consideration of the entire belief structure, not just isolated components.

Future Pathways

The presented work highlights a fundamental, yet often overlooked, truth: even flawlessly constructed tools are constrained by the foundations upon which they rest. This framework, examining the interplay between agent priors and machine learning decision support, doesn’t merely offer a model for prediction; it provides a miniature city plan, revealing how seemingly rational components can generate systemic dysfunction. The crucial observation-that misaligned prior beliefs can negate the benefits of even perfect models-demands a shift in focus. Future research must move beyond optimizing the ‘intelligence’ of decision support systems and address the more intractable problem of belief calibration.

Currently, the framework operates with a relatively simplified notion of ‘belief.’ Expanding this to encompass more nuanced cognitive structures-belief networks, for example-will be essential. More importantly, understanding how these beliefs are formed, updated, and propagated through complex systems is paramount. One does not rebuild the entire block to fix a leaky faucet; one traces the plumbing. Similarly, interventions aimed at improving decision-making shouldn’t focus solely on the output, but on the underlying infrastructure of belief.

Ultimately, the challenge lies in creating systems that are not merely intelligent, but adaptable. A truly robust framework will incorporate mechanisms for self-assessment, belief revision, and continuous learning-not just for the machine learning component, but for the agent itself. The path forward necessitates a move from optimizing for accuracy to optimizing for resilience, recognizing that the most sophisticated model is vulnerable if built on shaky ground.

Original article: https://arxiv.org/pdf/2602.21889.pdf

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

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2026-02-26 14:53