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The rapid integration of machine learning and artificial intelligence into materials science has introduced powerful capabilities for predicting, screening, and discovering new materials. Yet this integration also engenders a distinctive form of bias that operates not merely through skewed training data but through the mechanisms by which models allocate and distribute attention across chemical, structural, and property spaces. This paper conceptualizes “algorithmic attention” as a form of scientific bias that manifests in materials AI systems, shaping which phenomena receive emphasis, which regions of materials space are explored, and ultimately which knowledge claims gain epistemic legitimacy within the field. Attention is interpreted here as the patterned prioritization embedded in model architectures, loss functions, data sampling strategies, and iterative feedback loops between prediction and experiment. The analysis explores how such attention dynamics amplify existing data imbalances, create self-reinforcing discovery loops, misalign interpretive authority between model outputs and domain expertise, complicate validation of uncertain predictions, steer research trajectories through hidden optimization priorities, and pose system-level challenges for epistemic reliability and governance. Drawing on recent literature in materials informatics, bias in machine learning, and philosophy of data-driven science, the paper develops an integrative conceptual framework that treats algorithmic attention as an emergent property of socio-technical knowledge systems rather than a purely technical artifact. This framing highlights trade-offs between predictive scalability and epistemic pluralism, underscoring the need for reflective practices that render attention mechanisms more visible and contestable within materials discovery workflows.

The integration of artificial intelligence into materials design processes introduces complex dynamics where initial algorithmic choices shape subsequent trajectories, often embedding persistent dependencies that influence innovation pathways. This manuscript explores the conceptual underpinnings of path dependence, examining how data selection, model architectures, and iterative learning mechanisms interweave to form self-reinforcing structures in AI-assisted materials discovery. Through a synthesis of recent literature, it examines the interpretive implications of bias propagation, feedback loops, and epistemic constraints in computational materials science. The proposed framework conceptualizes these elements as interconnected layers, in which early decisions cascade through design cycles, shaping the exploration of material spaces and the emergence of novel properties. By focusing on systems-level insights, the analysis highlights trade-offs between efficiency and diversity in algorithmic guidance, as well as ethical considerations in steering material innovation. This interpretive approach underscores the need for reflective practices in AI-driven workflows, emphasizing how path-dependent logics can both constrain and enable creative outcomes in materials engineering. Ultimately, the discussion integrates these dynamics to reveal broader implications for sustainable and equitable advancements in the field, without positing empirical directives.