Artificial intelligence in human genomics and biomedicine

The increasing availability of extensive and complex data has made human genomics and its applications in (bio)medicine an at­ tractive domain for artificial intelligence (AI) in the form of advanced machine learning (ML) methods. These methods are linked not only to the hope of improving diagnosis and drug development. Rather, they may also advance key issues in biomedicine, e. g. understanding how individual differences in the human genome may cause specific traits or diseases. We analyze the increasing convergence of AI and genom­ics, the emergence of a corresponding innovation system, and how these associative AI methods relate to the need for causal knowledge in biomedical research and development (R&D) and in medical prac­tice. Finally, we look at the opportunities and challenges for clinical practice and the implications for governance issues arising from this convergence.

Reducing the Complexity of Casual Representation in Bayesian Belief Network

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool. Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model. Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN. Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

A Vector-agent Approach to (Spatiotemporal) Movement Modelling and Reasoning

Modelling a complex system of autonomous individuals moving through space and time essentially entails understanding the (heterogeneous) spatiotemporal context, interactions with other individuals, their internal states and making any underlying causal interrelationships explicit, a task for which agents (including vector-agents) are specifically well-suited. Building on a conceptual model of agent space-time and reasoning behaviour, a design guideline for an implemented vector-agent model is presented in this article as an example. The movement of football players was chosen as it is appropriately constrained in possible space, time and individual actions. Sensitivity-variability analysis was applied to measure the performance of different configurations of system components on the emergent movement patterns. The model output varied more when the condition of the contextual actors (players’ role-areas) were manipulated. In conclusion, ABMs can contribute to our understanding of movement and how causally-relevant evidence could be produced, through a proposed agent equipped with active causal knowledge.

No guts, no glory: underestimating the benefits of providing children with mechanistic details

Previous research shows that children effectively extract and utilize causal information, yet we find that adults doubt children’s ability to understand complex mechanisms. Since adults themselves struggle to explain how everyday objects work, why expect more from children? Although remembering details may prove difficult, we argue that exposure to mechanism benefits children via the formation of abstract causal knowledge that supports epistemic evaluation. We tested 240 6–9 year-olds’ memory for concrete details and the ability to distinguish expertise before, immediately after, or a week after viewing a video about how combustion engines work. By around age 8, children who saw the video remembered mechanistic details and were better able to detect car-engine experts. Beyond detailed knowledge, the current results suggest that children also acquired an abstracted sense of how systems work that can facilitate epistemic reasoning.

Descriptive understanding and prediction in COVID-19 modelling

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding.

Causal explanations for weight influence children’s social preferences: Biological-essentialist explanations reduce, and behavioral explanations promote, preferences for thin friends

The current study experimentally investigated the impact of causal-explanatory information on weight bias over development. Participants (n = 395, children ages 4–11 years and adults) received either a biological or behavioral explanation for body size, or neither, in three between-subjects conditions. Participants then made preference judgments for characters with smaller versus larger body sizes. Results showed that both behavioral and biological explanations impacted children’s preferences. Relative to children’s baseline preferences, behavioral explanations enhanced preferences for smaller characters, and biological explanations reduced these preferences—unlike the typical facilitative impact of biological-essentialist explanations on other biases. The explanations did not affect adults’ preferences. In contrast to previous findings, we demonstrate that causal knowledge can impact weight bias early in development.

The Plausible Impossible: Chinese Adults Hold Graded Notions of Impossibility

Events that violate the laws of nature are, by definition, impossible, but recent research suggests that people view some violations as “more impossible” than others (Shtulman & Morgan, 2017). When evaluating the difficulty of magic spells, American adults are influenced by causal considerations that should be irrelevant given the spell’s primary causal violation, judging, for instance, that it would be more difficult to levitate a bowling ball than a basketball even though weight should no longer be a consideration if contact is no longer necessary for support. In the present study, we sought to test the generalizability of these effects in a non-Western context – China – where magical events are represented differently in popular fiction and where reasoning styles are often more holistic than analytic. Across several studies, Chinese adults (n = 466) showed the same tendency as American adults to honor implicit causal constraints when evaluating the plausibility of magical events. These findings suggest that graded notions of impossibility are shared across cultures, possibly because they are a byproduct of causal knowledge.