Keeping Science Human in the Age of Machine Learning

by

Author: Denis Avetisyan

As machine learning tools reshape scientific practice, maintaining meaningful human oversight-and the values that guide it-becomes increasingly critical.

This review examines the conditions for achieving epistemic control and normativity in machine learning-based scientific workflows.

The increasing reliance on machine learning in scientific discovery raises concerns about diminished human agency and oversight. This paper, ‘Epistemic Control and the Normativity of Machine Learning-Based Science’, investigates whether these concerns are justified, framing the debate through the lens of ‘epistemic control’-defined by the conditions of tracking and tracing-to assess human scientists’ ability to meaningfully engage with ML systems. Arguing against a purely pessimistic view, this work demonstrates that robust epistemic control in ML-based science is achievable through careful attention to cognitive values and ongoing negotiation between researchers and their tools. But how can we best cultivate these practices to ensure responsible and effective scientific innovation in an increasingly automated landscape?

The Shifting Landscape of Scientific Truth

The landscape of scientific inquiry is undergoing a profound shift, increasingly characterized by reliance on complex computational methods, notably machine learning. Where traditional research often involved direct observation and deductive reasoning, many modern discoveries now emerge from algorithms analyzing vast datasets. This transition isn’t limited to data-rich fields like genomics or astronomy; it’s becoming pervasive across disciplines, from materials science to social sciences. Machine learning models, capable of identifying subtle patterns and making predictions beyond human capacity, are accelerating the pace of research. However, this progress introduces new methodological challenges, requiring scientists to adapt their approaches to leverage these powerful tools effectively and responsibly, while acknowledging the inherent complexities of algorithmic discovery.

The increasing reliance on machine learning within scientific inquiry introduces a significant challenge known as opacity – the inherent difficulty in discerning the reasoning behind a system’s conclusions. Unlike traditional scientific methods where each step of analysis is readily traceable and interpretable, many modern machine learning algorithms operate as ‘black boxes’. These systems, often comprised of millions or even billions of parameters, can identify complex patterns and make predictions with remarkable accuracy, yet the specific features driving those predictions remain obscured. This isn’t simply a matter of computational complexity; it’s a fundamental property of certain algorithms, particularly deep neural networks, where relationships between input data and output are distributed across numerous layers in a non-intuitive manner. Consequently, even the developers of these systems may struggle to fully explain why a particular conclusion was reached, raising concerns about the reliability and trustworthiness of the resulting scientific inferences.

The increasing reliance on complex machine learning models in scientific inquiry introduces a critical challenge known as inductive risk – the potential for erroneous conclusions stemming from the model’s internal ‘black box’ nature. Unlike traditional scientific methods where reasoning is transparent and assumptions are readily examined, these opaque systems can generate accurate predictions without revealing why those predictions are made. This lack of interpretability jeopardizes the trustworthiness of scientific inferences, as correlations identified by the model may not reflect genuine causal relationships. Consequently, researchers are actively developing new validation techniques that move beyond simply assessing predictive accuracy, and instead focus on probing model behavior, quantifying uncertainty, and establishing robustness against adversarial inputs – all essential steps to ensure the reliability of science driven by complex algorithms.

Epistemic Control: The Imperative of Understanding

Epistemic control is critical for establishing confidence in scientific results generated through machine learning (ML) applications. The increasing reliance on ML algorithms in scientific discovery necessitates a robust framework for validating and interpreting outcomes, as these systems can produce results lacking transparency or based on spurious correlations. Without epistemic control – the ability to understand how an ML system arrives at a conclusion – researchers risk accepting inaccurate or misleading findings. This is particularly important given the ‘black box’ nature of many complex ML models, where the reasoning behind predictions is opaque. Establishing epistemic control is not merely about verifying accuracy, but about ensuring the scientific process remains interpretable, reproducible, and aligned with established methodological principles.

Epistemic control, crucial for reliable machine learning-driven science, is achieved through two primary conditions. The ‘Tracing Condition’ necessitates a comprehensive understanding of the system’s decision-making process – how inputs are transformed into outputs – and requires detailed access to the model’s internal logic and data flow. Complementing this, the ‘Tracking Condition’ demands alignment of system actions with established methodological standards, ensuring that the machine learning model adheres to accepted scientific practices regarding data handling, validation, and error analysis. Both conditions are interdependent; tracing the mechanism allows for verification against standards, while tracking informs refinement of the mechanistic understanding.

Effective epistemic control in machine learning-driven science necessitates the consistent incorporation of human expertise and adherence to established scientific norms. This linkage is not merely procedural; human perspectives are crucial for validating model outputs, identifying potential biases, and ensuring alignment with domain-specific knowledge that algorithms may lack. Upholding scientific normativity – encompassing principles like reproducibility, falsifiability, and peer review – provides a framework for evaluating the reliability and validity of machine learning-derived conclusions. Deviation from these established norms compromises the integrity of the scientific process, while consistent application reinforces trust in the resulting knowledge and facilitates effective oversight of automated systems.

Cancer Genomics and the Illusion of Algorithmic Insight

The application of machine learning techniques to cancer genomics is expanding rapidly, driven by the increasing volume and complexity of genomic data generated by high-throughput sequencing technologies. These methods are being employed across a range of analyses, including identifying cancer-associated genes, classifying tumor subtypes, predicting patient response to therapy, and discovering novel drug targets. Specifically, algorithms such as support vector machines, random forests, and deep neural networks are used to analyze genomic, transcriptomic, and proteomic datasets. This computational approach offers the potential to identify patterns and relationships that would be difficult or impossible to discern through traditional methods, accelerating progress in cancer understanding and treatment development.

Machine learning applications in cancer genomics currently prioritize two primary objectives: building predictive models to classify or forecast outcomes, and discovering the underlying mechanistic explanations for observed genomic patterns. However, many commonly used machine learning algorithms, particularly deep learning models, function as “black boxes” with limited transparency. This opacity hinders interpretability, making it difficult to determine why a model arrived at a specific prediction. While high predictive accuracy is valuable, a lack of understanding regarding the algorithm’s decision-making process limits its utility for generating novel biological hypotheses and validating existing knowledge about cancer development and progression. Consequently, increasing emphasis is being placed on developing and utilizing more interpretable machine learning approaches, or methods for extracting mechanistic insights from complex models.

The utility of machine learning predictions in cancer genomics is limited by a lack of mechanistic understanding; while algorithms may accurately identify correlations between genomic features and clinical outcomes, these predictions do not, in themselves, elucidate the underlying biological processes driving cancer development or treatment response. Consequently, predictions lacking explanatory power hinder the formulation of testable hypotheses and the design of targeted interventions; robust scientific advancement requires not only predictive accuracy, but also a clear understanding of how and why specific genomic alterations contribute to disease phenotypes. This absence of mechanistic insight limits the generalizability of findings and the potential for translating predictive models into effective clinical strategies.

The Fragile Future of Scientific Authority

Achieving robust epistemic control in machine learning necessitates a deliberate and transparent approach to value specification and choice. Simply put, algorithms aren’t neutral; they embody the values of their creators through design decisions regarding data selection, feature engineering, and model optimization. Without explicitly defining these embedded values – whether concerning fairness, accuracy, or specific scientific priorities – models can inadvertently perpetuate biases or produce results misaligned with intended goals. Careful value choice, therefore, involves a rigorous assessment of potential impacts and a conscious effort to prioritize values that uphold scientific integrity and societal benefit. This process isn’t a one-time fix, but rather an ongoing commitment to monitoring and refining value alignments throughout the model’s lifecycle, ensuring responsible innovation and trustworthy outputs.

While data mining and computer simulations offer unprecedented capabilities in scientific exploration, their outputs are not inherently self-validating; ongoing human oversight remains crucial for maintaining scientific rigor. These methods, though powerful, operate based on algorithms and inputted data, potentially amplifying biases or generating spurious correlations if left unchecked. Experts emphasize that simulations, for example, are only as reliable as the assumptions coded into their design, and data mining can easily identify patterns that lack genuine causal relationships. Consequently, researchers must actively interpret results, verify findings through independent means, and ensure alignment with established scientific principles – a process demanding critical thinking and domain expertise to prevent the propagation of flawed or misleading conclusions.

Recent scholarship, notably the work of Paul Humphreys, cautions that increasingly complex scientific practices – particularly those leveraging computational methods – present a genuine risk of diminished human control over knowledge production. Humphreys argues that reliance on ‘black box’ algorithms and large datasets can obscure the underlying reasoning, making it difficult to assess the validity of results or identify potential biases. This isn’t merely a theoretical concern; the potential for unforeseen consequences demands a proactive approach to scientific oversight. Strategies emphasizing transparency, rigorous testing, and continuous monitoring are therefore crucial, not to hinder innovation, but to ensure that scientific inquiry remains firmly grounded in established standards of evidence and logical reasoning, safeguarding against the unintentional propagation of flawed or misleading information.

The pursuit of epistemic control within machine learning-based science reveals a predictable tension. The article posits that constraints are inherent in adopting these tools, yet meaningful human oversight remains possible. This echoes a fundamental principle: a system that never breaks is dead. Bertrand Russell observed, “To fear is one thing. To fear well is another.” Similarly, simply having a machine learning system isn’t sufficient; the capacity to understand its limitations, trace its reasoning, and negotiate its outputs – to ‘fear well’ its opacity – is crucial. The article’s emphasis on tracking and tracing conditions isn’t about eliminating failure, but rather about cultivating a system resilient enough to accommodate it, and learning from its inevitable imperfections.

The Looming Shadows

The insistence on ‘epistemic control’ feels, already, like a plea against inevitability. This work correctly identifies the negotiation between scientist and tool, but frames it as a problem of alignment. A subtler decay will occur: not a failure to direct the machine, but a slow erosion of the questions asked. Each carefully constructed metric, each attempt to trace conditions, adds a layer of pre-commitment, a prophecy of what will be deemed ‘knowable’. The system won’t resist control-it will redefine the domain of inquiry, subtly, over time.

Future work will undoubtedly focus on refining these tracing mechanisms, building ever-more-detailed accounts of algorithmic influence. But this is treating symptoms, not the disease. The true challenge lies in acknowledging that any successful ‘control’ is merely a temporary reprieve, a local minimum in a landscape governed by entropy. The opacity isn’t a bug; it’s the natural state of complex systems, a constant reminder that complete understanding is a comforting fiction.

The field will likely discover, within a few cycles, that the most robust form of ‘control’ isn’t about dictating outcomes, but cultivating a willingness to be surprised. To accept, not as failure, but as fundamental truth, that the machine will always know more than it can reveal, and that the most valuable discoveries will emerge from the shadows of that unknowability.

Original article: https://arxiv.org/pdf/2601.11202.pdf

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

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2026-01-19 12:39