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A peek inside two black boxes-an experiment with explainable artificial intelligence and IPCC leadership

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In this paper, we devise a machine-learning approach to tackle the complex task of investigating leaders in a multi-national organisation: the Intergovernmental Panel on Climate Change. The difficulty of this task lies in the impossibility to spell out the characteristics that define leadership in a complex and highly distributed organization, endowed with a hybrid mission at the interface between science and politics. To bypass this difficulty, we start from a sample of formal organisational leaders defined by the fact of having been officially nominated for the Bureau of the IPCC – among the highest positions in the organisation. A series of anomaly-detection techniques are used to identify IPCC contributors that are or might be Bureau candidates. We find that we can construct a precise albeit implicit model of IPCC leadership despite its social and political complexity. We then suggest various explainable AI methods to investigate why the model has selected members of the IPCC as Bureau candidates. Our analysis of the AI model and of its errors suggest interesting findings about asymmetries in the data and in the IPCC as well as shortcomings of the techniques we employed.

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Data availability

The data is available on request from Kari De Pryck and Tommaso Venturini.


  1. The authors who have only contributed to its Special Reports are not included.

  2. It is important to remark that, as far as the authors are concerned, our network only includes Coordinating Lead Authors, Lead Authors or Review Editors.

  3. The main role division in the IPCC corresponds to the functional separation between the scientists that review the scientific literature on climate change and write the assessment reports (i.e. ‘the authors’) and the diplomats who serve in the national delegations overseeing the work of the organisation (i.e. ‘the delegates’). Authors are then subdivided in three Working Groups with different thematic specialisations. We also include participation to the writing of the Synthesis Report, which brings together the conclusions of the three WGs. Finally, the work of the IPCC is temporarily articulated in Assessment Cycles each lasting several years.

  4. To generate a monopartite network, we have added an edge if there is a positive Pearson correlation between the list of capacities individuals have occupied.

  5. To quantify the bridging function in the IPCC, we have developed a metric called ‘bipartite-bridgeness’, which we define as the summation of the number of indirect connections created by a node weighted by the importance of such connections and by their rarity (Venturini et al., 2022).

  6. We found the nominees for AR5 and AR6 in the reports of IPCC meetings – its 29th and 42nd plenaries. Unfortunately, the candidate names for AR4 are missing.

  7. Lower average ranking is better because lower ranks are closer to the top of the ranking.

  8. As defined in the IPCC rules of procedures (IPCC, 2018b).

  9. The remaining 3 candidates and 3 potential leaders are from the so-called ‘economies in transition’.

  10. An exception is Díaz-Rodríguez and Pisoni (2020).


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Tobias Blanke was supported by the project AI4Media—A European Excellence Centre for Media, Society and Democracy (EC, H2020 n. 951911).

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Authors and Affiliations



Kari De Pryck hs conducted the qualitative research of the IPCC and together with Tommaso Venturini developed the dataset of IPCC members. Tommaso Venturini has designed the statistical analysis and together with Tobias Blanke co-developed the machine-learning experiments. Tobias Blanke has designed and run the machine-learning and explainable AI part. We have all worked together on the analysis and discussion of the machine-learning results.

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Correspondence to Tobias Blanke.

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Blanke, T., Venturini, T. & De Pryck, K. A peek inside two black boxes-an experiment with explainable artificial intelligence and IPCC leadership. Int J Digit Humanities 6, 45–69 (2024).

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