This position paper discusses the benefits of longitudinal behavioural research with customised AI tools for exploring the opportunities and risks of synthetic relationships. Synthetic relationships are defined as "continuing associations between humans and AI tools that interact with one another wherein the AI tool(s) influence(s) humans' thoughts, feelings, and/or actions." (Starke et al., 2024). These relationships can potentially improve health, education, and the workplace, but they also bring the risk of subtle manipulation and privacy and autonomy concerns. To harness the opportunities of synthetic relationships and mitigate their risks, we outline a methodological approach that complements existing findings. We propose longitudinal research designs with self-assembled AI agents that enable the integration of detailed behavioural and self-reported data.

Chronic obstructive pulmonary disease (COPD), a progressive and non-reversible lung disease causing obstructed air-flow from the lungs, often occurs with other diseases not restricted to the respiratory system. Therefore, it is important to understand interaction between genes and diseases to uncover the real causes of a disease. In this paper, we propose to automatically classify COPD diseases, using network of gene disease relationships. We simplify interaction between COPD, COPD multimorbidities, and related genes as a bi-partite network, and apply network embedding together with machine learning classifiers to classify diseases into different categories. Our experiments confirm that adding synthetic edges in a strategic way statistically enhances quality of node embedding and improve COPD disease classification performance.