Machine Learning and Theory Ladenness -- A Phenomenological Account

In recent years, the dissemination of machine learning (ML) methodologies in scientific research has prompted discussions on theory ladenness. More specifically, the issue of theory ladenness has remerged as questions about whether and how ML models (MLMs) and ML modelling strategies are impacted by the domain theory of the scientific field in which ML is used and implemented (e.g., physics, chemistry, biology, etc). On the one hand, some have argued that there is no difference between traditional (pre ML) and ML assisted science. In both cases, theory plays an essential and unavoidable role in the analysis of phenomena and the construction and use of models. Others have argued instead that ML methodologies and models are theory independent and, in some cases, even theory free. In this article, we argue that both positions are overly simplistic and do not advance our understanding of the interplay between ML methods and domain theories. Specifically, we provide an analysis of theory ladenness in ML assisted science. Our analysis reveals that, while the construction of MLMs can be relatively independent of domain theory, the practical implementation and interpretation of these models within a given specific domain still relies on fundamental theoretical assumptions and background knowledge.

Causal Concept Embedding Models: Beyond Causal Opacity in Deep Learning

Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying a deep neural network's (DNN) reasoning. This leads to the inability to rely on and verify state-of-the-art DNN-based systems especially in high-stakes scenarios. For this reason, causal opacity represents a key open challenge at the intersection of deep learning, interpretability, and causality. This work addresses this gap by introducing Causal Concept Embedding Models (Causal CEMs), a class of interpretable models whose decision-making process is causally transparent by design. The results of our experiments show that Causal CEMs can: (i) match the generalization performance of causally-opaque models, (ii) support the analysis of interventional and counterfactual scenarios, thereby improving the model's causal interpretability and supporting the effective verification of its reliability and fairness, and (iii) enable human-in-the-loop corrections to mispredicted intermediate reasoning steps, boosting not just downstream accuracy after corrections but also accuracy of the explanation provided for a specific instance.

Addressing Social Misattributions of Large Language Models: An HCXAI-based Approach

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may lead to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology.