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Paper Citation Record · LEDGER

Evaluating Actionability in Explainable AI

As of 18 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 1 inbound Pith citation observation for arXiv:2601.20086.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2601.20086 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T07:35:25.604743Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T14:59:12.966608Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T11:21:04.851795Z

Reference resolution

66 of 66 outbound references displayed

  • verified exact13
  • verified fuzzy0
  • unresolved46
  • parse uncertain0
  • malformed identifier3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 23ffc90e-ec3b-4dc2-9583-7f76788eeee2 · outbound

This paper cites ICO & Turing Institute (2022), https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp- themes/explaining-decisions-made-with-artificial-intelligence/ 20 G.

Evaluating Actionability in Explainable AI ICO & Turing Institute (2022), https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp- themes/explaining-decisions-made-with-artificial-intelligence/ 20 G

Reference 1

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Observation 347b75e2-5da5-4cf6-8e48-49538c627921 · outbound

This paper cites Hum.-Comput.

Evaluating Actionability in Explainable AI Hum.-Comput

Reference 2

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This paper cites https://doi.org/10.1109/ACCESS.2018.2870052, conference Name: IEEE Ac- cess.

Evaluating Actionability in Explainable AI https://doi.org/10.1109/ACCESS.2018.2870052, conference Name: IEEE Ac- cess

Reference 3

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Observation 28c0719b-dbe1-4c8d-af02-d1c9ccda6d1a · outbound

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 4

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Observation 70094c8f-ecc7-4b34-a0b9-3e32d1a518f2 · outbound

This paper cites https://doi.org/10.1007/s00146-021-01326-6, https://doi.org/10.1007/s00146-021-01326-6.

Evaluating Actionability in Explainable AI https://doi.org/10.1007/s00146-021-01326-6, https://doi.org/10.1007/s00146-021-01326-6

Reference 5

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Observation 8d1dfb4d-4cf9-477a-9d89-3722c5532569 · outbound

This paper cites Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

Evaluating Actionability in Explainable AI Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Reference 6

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Observation c1e68094-800e-40e4-93a3-460895986f0c · outbound

This paper cites One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.

Evaluating Actionability in Explainable AI One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

Reference 7

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 8

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This paper cites (ed.): Scenario-based design: envisioning work and technology in sys- tem development.

Evaluating Actionability in Explainable AI (ed.): Scenario-based design: envisioning work and technology in sys- tem development

Reference 9

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 11

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 12

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Observation 9f270fe1-9fa9-4b80-adad-e0b7dbcac848 · outbound

This paper cites In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems.

Evaluating Actionability in Explainable AI In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Reference 13

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Observation 43893dfb-2095-468f-845e-c96ed472bfa3 · outbound

This paper cites In: 2018 IEEE 20th Interna- tional Conference on e-Health Networking, Applications and Services (Healthcom).

Evaluating Actionability in Explainable AI In: 2018 IEEE 20th Interna- tional Conference on e-Health Networking, Applications and Services (Healthcom)

Reference 14

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This paper cites In: Proceedings of the 42nd In- ternational ACM SIGIR Conference on Research and Development in Infor- mation Retrieval.

Evaluating Actionability in Explainable AI In: Proceedings of the 42nd In- ternational ACM SIGIR Conference on Research and Development in Infor- mation Retrieval

Reference 15

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 16

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Observation 159cc69c-41b5-46be-ae7f-e6c31a97ece3 · outbound

This paper cites In: Guarda, T., Portela, F., Augusto, M.F.

Evaluating Actionability in Explainable AI In: Guarda, T., Portela, F., Augusto, M.F

Reference 17

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Evaluating Actionability in Explainable AI it is a moving process

Reference 18

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 19

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 20

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Observation d0a8699d-e0a9-4953-91ed-ada5d8092a8e · outbound

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Evaluating Actionability in Explainable AI In: Dohn, N.B., Jandrić, P., Ryberg, T., de Laat, M

Reference 21

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Observation cf115508-13d6-4bb2-bddb-a538cf85e23c · outbound

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Evaluating Actionability in Explainable AI Towards A Rigorous Science of Interpretable Machine Learning

Reference 22

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Evaluating Actionability in Explainable AI https://doi.org/10.2139/ssrn.2972855, https://papers.ssrn.com/abstract=2972855

Reference 23

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Evaluating Actionability in Explainable AI In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Reference 24

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Evaluating Actionability in Explainable AI Unresolved cited work

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Evaluating Actionability in Explainable AI In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

Reference 26

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Evaluating Actionability in Explainable AI https://doi.org/10.1007/s00146-022-01454-7, https://doi.org/10.1007/s00146- 022-01454-7

Reference 27

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Evaluating Actionability in Explainable AI Learning Actionable Representations with Goal-Conditioned Policies

Reference 28

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Evaluating Actionability in Explainable AI Human-Machine Communication2, 153–171 (01 2021)

Reference 29

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Evaluating Actionability in Explainable AI Academy of Management An- nals14(2), 627–660 (2020)

Reference 30

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Evaluating Actionability in Explainable AI ACM Computing Surveys (CSUR)51, 1 – 42 (2018)

Reference 31

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Evaluating Actionability in Explainable AI AAAI Spring Symposium (2009)

Reference 33

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Evaluating Actionability in Explainable AI Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Reference 34

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Evaluating Actionability in Explainable AI Unresolved cited work

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Evaluating Actionability in Explainable AI Unresolved cited work

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Evaluating Actionability in Explainable AI Unresolved cited work

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Reference 39

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Evaluating Actionability in Explainable AI In: Trattner, C., Parra, D., Riche, N

Reference 40

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This paper cites Communications of the ACM 61, 36 – 43 (2018).

Evaluating Actionability in Explainable AI Communications of the ACM 61, 36 – 43 (2018)

Reference 41

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 42

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This paper cites Journal of Computer-Mediated Com- munication26(6), 384–402 (09 2021).

Evaluating Actionability in Explainable AI Journal of Computer-Mediated Com- munication26(6), 384–402 (09 2021)

Reference 43

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Evaluating Actionability in Explainable AI In: Nørskov, M., Seibt, J., Quick, O

Reference 44

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This paper cites Extracting Actionability from Machine Learning Models by Sub-optimal Deterministic Planning.

Evaluating Actionability in Explainable AI Extracting Actionability from Machine Learning Models by Sub-optimal Deterministic Planning

Reference 45

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Evaluating Actionability in Explainable AI A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

Reference 46

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Evaluating Actionability in Explainable AI In: Proceed- ings of the 2020 Conference on Fairness, Accountability, and Trans- parency

Reference 47

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This paper cites asser lecture - asser.nl (2021), https://www.asser.nl/asserpress/books/?rId=13986.

Evaluating Actionability in Explainable AI asser lecture - asser.nl (2021), https://www.asser.nl/asserpress/books/?rId=13986

Reference 48

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 49

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Evaluating Actionability in Explainable AI In: Lim, C.T., Leo, H.L., Yeow, R

Reference 50

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Evaluating Actionability in Explainable AI Computers and Education: Artificial Intelligence2, 100020 (2021)

Reference 51

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Evaluating Actionability in Explainable AI American Medical Informatics Association Annual Symposium2012, 779–88 (2012)

Reference 52

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This paper cites British Journal of Educational Technology50(6), 2943–2958 (2019).

Evaluating Actionability in Explainable AI British Journal of Educational Technology50(6), 2943–2958 (2019)

Reference 53

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Evaluating Actionability in Explainable AI https://doi.org/10.1007/s43681-022-00158-4, https://doi.org/10.1007/s43681- 022-00158-4

Reference 54

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Reference 55

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Evaluating Actionability in Explainable AI Unresolved cited work

Reference 56

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Evaluating Actionability in Explainable AI Directive Explanations for Actionable Explainability in Machine Learning Applications

Reference 57

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Evaluating Actionability in Explainable AI Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions"

Reference 58

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Evaluating Actionability in Explainable AI https://doi.org/10.1186/s41239-021-00313-7, https://doi.org/10.1186/s41239-021- 00313-7

Reference 59

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Evaluating Actionability in Explainable AI (eds.) The Mind-Technology Problem : Investigating Minds, Selves and 21st Century Artefacts, pp

Reference 60

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Evaluating Actionability in Explainable AI Defining and Conceptualizing Actionable Insight: A Conceptual Framework for Decision-centric Analytics

Reference 61

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Reference 62

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Reference 63

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Evaluating Actionability in Explainable AI In: ICCBR Workshops (2021)

Reference 64

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Reference 65

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Evaluating Actionability in Explainable AI In: Proceedings of the 2019 CHI Conference on Human Fac- tors in Computing Systems

Reference 66

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Evaluating Actionability in Explainable AI Explanatory Pluralism in Explainable AI

Reference 67

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Pith citing papers

Observation b6049a40-cf84-408d-9135-8ae4c240ee74 · inbound

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From Attribution to Action: A Human-Centered Application of Activation Steering Evaluating Actionability in Explainable AI

Reference 35

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