Fig 1.
A flow chart outlining our methodology.
Fig 2.
Life-cycle of regression models research.
Flow diagram showing the life-cycle of a dynamic community pertaining to statistics research on regression models. Any papers present in the first or last realisation of the dynamic community are plotted. The nodes in the graph represent step communities and they are grouped by the time step in which they appear. The edges between nodes show the movement of papers between step communities. The dynamic community dissolves after 2010
Fig 3.
Life-cycle of neural networks research.
Flow diagram showing the life-cycle of a dynamic community of neural networks research that splits before 2005. The research area that focuses on rule extraction remains present in 2020. Any papers present in the first or last realisation of the dynamic community are plotted. The nodes in the graph represent step communities and they are grouped by the time step in which they appear. The edges between nodes show the movement of papers between step communities.
Fig 4.
Life-cycles of counterfactual and causal explanation methods.
Flow diagram comparing the life-cycle of two dynamic communities relating to two general approaches to explanation: ‘causal explanation’ and ‘counterfactual explanation’. Any papers present in the final realisation of either dynamic community are plotted. The nodes in the graph represent step communities and they are grouped by the time step in which they appear. The edges between nodes show the movement of papers between step communities.
Fig 5.
Foundation research areas life-cycles.
Dynamic community life-cycles of the communities with the highest betweenness centrality (left) and lowest betweenness centrality (right) in the period 2000–2010. Each row represents the life-cycle of a dynamic community and each cell in the row is populated if that dynamic community appears in the network as a step community in the corresponding time step. Each cell is coloured to show the most common ASJC category among the papers in the step community.
Fig 6.
Knowledge transfer between foundations of XAI and contemporary topics.
The percentage of total possible interactions (citations) between contemporary research areas in XAI literature and the foundation areas. For comparison, some recent central topics in XAI are included on the right. For readability, the interaction probabilities are scaled to percentages. For example, if 20% of papers in research area A cite 10% of papers in research area B, the resulting interactions score would be 2%. Community interaction probabilities are calculated according to Eq 2.
Fig 7.
The percentage of total possible interactions (citations) between contemporary research areas in XAI literature and the foundation areas.
For comparison, some recent central topics in XAI are included on the right. For readability, the interaction probabilities are scaled to percentages. For example, if 20% of papers in research area A cite 10% of papers in research area B, the resulting interactions score would be 2%.
Table 1.
Knowledge silos.
Table 2.
Knowledge gaps.