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In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively.

US intelligence analysts must weigh up relevant evidence to assess the probability of their conclusions, and express this reasoning clearly in written reports for decision-makers. Typically, they work alone with no special analytic tools, and sometimes succumb to common probabilistic and causal reasoning errors. So, the US government funded a major research program (CREATE) for four large academic teams to develop new structured, collaborative, software-based methods that might achieve better results. Our team's method (BARD) is the first to combine two key techniques: constructing causal Bayesian network models (BNs) to represent analyst knowledge, and small-group collaboration via the Delphi technique. BARD also incorporates compressed, high-quality online training allowing novices to use it, and checklist-inspired report templates with a rudimentary AI tool for generating text explanations from analysts' BNs. In two prior experiments, our team showed BARD's BN-building assists probabilistic reasoning when used by individuals, with a large effect (Glass' 0.8) (Cruz et al., 2020), and even minimal Delphi-style interactions improve the BN structures individuals produce, with medium to very large effects (Glass' 0.5-1.3) (Bolger et al., 2020). This experiment is the critical test of BARD as an integrated system and possible alternative to business-as-usual for intelligence analysis. Participants were asked to solve three probabilistic reasoning problems spread over 5 weeks, developed by our team to test both quantitative accuracy and susceptibility to tempting qualitative fallacies. Our 256 participants were randomly assigned to form 25 teams of 6-9 using BARD and 58 individuals using Google Suite and (if desired) the best pen-and-paper techniques. For each problem, BARD outperformed this control with very large to huge effects (Glass' 1.4-2.2), greatly exceeding CREATE's initial target. We conclude that, for suitable problems, BARD already offers significant advantages over both business-as-usual and existing BN software. Our effect sizes also suggest BARD's BN-building and collaboration combined beneficially and cumulatively, although implementation differences decreased performances compared to Cruz et al. (2020), so interaction may have contributed. BARD has enormous potential for further development and testing of specific components and on more complex problems, and many potential applications beyond intelligence analysis.

Bayes Nets (BNs) are extremely useful for causal and probabilistic modelling in many real-world applications, often built with information elicited from groups of domain experts. But their potential for reasoning and decision support has been limited by two major factors: the need for significant normative knowledge, and the lack of any validated methods or software supporting collaboration. Consequently, we have developed a web-based structured technique – Bayesian Argumentation via Delphi (BARD) – to enable groups of domain experts to receive minimal normative training and then collaborate effectively to produce high-quality BNs. BARD harnesses multiple perspectives on a problem, while minimising biases manifest in freely interacting groups, via a Delphi process: solutions are first produced individually, then shared, followed by an opportunity for individuals to revise their solutions. To test the hypothesis that BNs improve due to Delphi, we conducted an experiment whereby individuals with a little BN training and practice produced structural models using BARD for two Bayesian reasoning problems. Participants then received 6 other structural models for each problem, rated their quality on a 7-point scale, and revised their own models if they wished. Both top-rated and revised models were on average significantly better quality (scored against a gold-standard) than the initial models, with large and medium effect sizes. We conclude that Delphi – and BARD – improves the quality of BNs produced by groups. Further, although rating cannot create new models, rating seems quicker and easier than revision and yielded significantly better models – so, we suggest efficient BN amalgamation should include both.

Background Digital health (DH) and the benefits of related services are fairly well understood. However, it still is critical to map the digital health care landscape including the key elements that define it as an ecosystem. Particularly, knowing the perspectives of citizens on this digital transformation is an important angle to capture. In this review we aim to analyze the relevant studies to identify how DH is understood and experienced by Australian citizens and what they may require from DH platforms. Materials and methods A scoping literature review was conducted across several electronic databases (ACM Digital Library, OVID, PubMed, Scopus, IEEE, Science Direct, SAGE), as well as grey literature. Additionally, citation mining was conducted to identify further relevant studies. Identified studies were subjected to eligibility criteria and the final set of articles was independently reviewed, analyzed, discussed and interpreted by three reviewers. Results Of 3811 articles, 98 articles met the inclusion criteria with research-based articles–as opposed to review articles or white papers– comprising the largest proportion (72%) of the selected literature. The qualitative analysis of the literature revealed five key elements that capture the essence of the digital health ecosystem interventions from the viewpoint of the Australian citizens. The identified elements were “consumer/user”, “health care”, “technology”, “use and usability”, “data and information”. These elements were further found to be associated with 127 subcategories. Conclusions This study is the first of its kind to analyze and synthesize the relevant literature on DH ecosystems from the citizens’ perspective. Through the lens of two research questions, this study defines the key components that were found crucial to understanding citizens’ experiences with DH. This understanding lays a strong foundation for designing and fostering DH ecosystem. The results provide a solid ground for empirical testing.