Assessing the Local Interpretability of Machine Learning Models Article Swipe
Dylan Slack
,
Sorelle A. Friedler
,
Carlos Scheidegger
,
Chitradeep Dutta Roy
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1902.03501
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1902.03501
The increasing adoption of machine learning tools has led to calls for accountability via model interpretability. But what does it mean for a machine learning model to be interpretable by humans, and how can this be assessed? We focus on two definitions of interpretability that have been introduced in the machine learning literature: simulatability (a user's ability to run a model on a given input) and "what if" local explainability (a user's ability to correctly determine a model's prediction under local changes to the input, given knowledge of the model's original prediction). Through a user study with 1,000 participants, we test whether humans perform well on tasks that mimic the definitions of simulatability and "what if" local explainability on models that are typically considered locally interpretable. To track the relative interpretability of models, we employ a simple metric, the runtime operation count on the simulatability task. We find evidence that as the number of operations increases, participant accuracy on the local interpretability tasks decreases. In addition, this evidence is consistent with the common intuition that decision trees and logistic regression models are interpretable and are more interpretable than neural networks.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1902.03501
- https://arxiv.org/pdf/1902.03501
- OA Status
- green
- Cited By
- 48
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2930304691
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2930304691Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1902.03501Digital Object Identifier
- Title
-
Assessing the Local Interpretability of Machine Learning ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-02-09Full publication date if available
- Authors
-
Dylan Slack, Sorelle A. Friedler, Carlos Scheidegger, Chitradeep Dutta RoyList of authors in order
- Landing page
-
https://arxiv.org/abs/1902.03501Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1902.03501Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1902.03501Direct OA link when available
- Concepts
-
Interpretability, Machine learning, Artificial intelligence, Computer science, Intuition, Metric (unit), Task (project management), Logistic regression, Psychology, Management, Economics, Cognitive science, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
48Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 9, 2022: 3, 2021: 10Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2930304691 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.1902.03501 |
| ids.doi | https://doi.org/10.48550/arxiv.1902.03501 |
| ids.mag | 2930304691 |
| ids.openalex | https://openalex.org/W2930304691 |
| fwci | |
| type | preprint |
| title | Assessing the Local Interpretability of Machine Learning Models |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12026 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Explainable Artificial Intelligence (XAI) |
| topics[1].id | https://openalex.org/T11689 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9771000146865845 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Adversarial Robustness in Machine Learning |
| topics[2].id | https://openalex.org/T12535 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.965399980545044 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Machine Learning and Data Classification |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2781067378 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9891314506530762 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q17027399 |
| concepts[0].display_name | Interpretability |
| concepts[1].id | https://openalex.org/C119857082 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7430126667022705 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[1].display_name | Machine learning |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6918210983276367 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6605047583580017 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C132010649 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5342736840248108 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q189222 |
| concepts[4].display_name | Intuition |
| concepts[5].id | https://openalex.org/C176217482 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5079885125160217 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q860554 |
| concepts[5].display_name | Metric (unit) |
| concepts[6].id | https://openalex.org/C2780451532 |
| concepts[6].level | 2 |
| concepts[6].score | 0.462159663438797 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[6].display_name | Task (project management) |
| concepts[7].id | https://openalex.org/C151956035 |
| concepts[7].level | 2 |
| concepts[7].score | 0.44811680912971497 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1132755 |
| concepts[7].display_name | Logistic regression |
| concepts[8].id | https://openalex.org/C15744967 |
| concepts[8].level | 0 |
| concepts[8].score | 0.07932361960411072 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[8].display_name | Psychology |
| concepts[9].id | https://openalex.org/C187736073 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[9].display_name | Management |
| concepts[10].id | https://openalex.org/C162324750 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[10].display_name | Economics |
| concepts[11].id | https://openalex.org/C188147891 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q147638 |
| concepts[11].display_name | Cognitive science |
| concepts[12].id | https://openalex.org/C21547014 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1423657 |
| concepts[12].display_name | Operations management |
| keywords[0].id | https://openalex.org/keywords/interpretability |
| keywords[0].score | 0.9891314506530762 |
| keywords[0].display_name | Interpretability |
| keywords[1].id | https://openalex.org/keywords/machine-learning |
| keywords[1].score | 0.7430126667022705 |
| keywords[1].display_name | Machine learning |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.6918210983276367 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6605047583580017 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/intuition |
| keywords[4].score | 0.5342736840248108 |
| keywords[4].display_name | Intuition |
| keywords[5].id | https://openalex.org/keywords/metric |
| keywords[5].score | 0.5079885125160217 |
| keywords[5].display_name | Metric (unit) |
| keywords[6].id | https://openalex.org/keywords/task |
| keywords[6].score | 0.462159663438797 |
| keywords[6].display_name | Task (project management) |
| keywords[7].id | https://openalex.org/keywords/logistic-regression |
| keywords[7].score | 0.44811680912971497 |
| keywords[7].display_name | Logistic regression |
| keywords[8].id | https://openalex.org/keywords/psychology |
| keywords[8].score | 0.07932361960411072 |
| keywords[8].display_name | Psychology |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:1902.03501 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/1902.03501 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/1902.03501 |
| locations[1].id | doi:10.48550/arxiv.1902.03501 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.1902.03501 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5012515310 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4186-2937 |
| authorships[0].author.display_name | Dylan Slack |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I155707491 |
| authorships[0].affiliations[0].raw_affiliation_string | Haverford Coll. |
| authorships[0].institutions[0].id | https://openalex.org/I155707491 |
| authorships[0].institutions[0].ror | https://ror.org/04fnrxr62 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I155707491 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Haverford College |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Dylan Slack |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Haverford Coll. |
| authorships[1].author.id | https://openalex.org/A5086384189 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6023-1597 |
| authorships[1].author.display_name | Sorelle A. Friedler |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I155707491 |
| authorships[1].affiliations[0].raw_affiliation_string | Haverford Coll. |
| authorships[1].institutions[0].id | https://openalex.org/I155707491 |
| authorships[1].institutions[0].ror | https://ror.org/04fnrxr62 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I155707491 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Haverford College |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sorelle A. Friedler |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Haverford Coll. |
| authorships[2].author.id | https://openalex.org/A5035997603 |
| authorships[2].author.orcid | https://orcid.org/0009-0006-9163-6136 |
| authorships[2].author.display_name | Carlos Scheidegger |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Carlos Scheidegger |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5006069871 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Chitradeep Dutta Roy |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I223532165 |
| authorships[3].affiliations[0].raw_affiliation_string | University of Utah |
| authorships[3].institutions[0].id | https://openalex.org/I223532165 |
| authorships[3].institutions[0].ror | https://ror.org/03r0ha626 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I223532165 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of Utah |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Chitradeep Dutta Roy |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | University of Utah |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/1902.03501 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Assessing the Local Interpretability of Machine Learning Models |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12026 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Explainable Artificial Intelligence (XAI) |
| related_works | https://openalex.org/W2905433371, https://openalex.org/W2888392564, https://openalex.org/W4310278675, https://openalex.org/W4388422664, https://openalex.org/W4390569940, https://openalex.org/W4361193272, https://openalex.org/W2963326959, https://openalex.org/W4388685194, https://openalex.org/W4312407344, https://openalex.org/W2894289927 |
| cited_by_count | 48 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 5 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 9 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 3 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 10 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 13 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 6 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:1902.03501 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/1902.03501 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/1902.03501 |
| primary_location.id | pmh:oai:arXiv.org:1902.03501 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/1902.03501 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/1902.03501 |
| publication_date | 2019-02-09 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2493343568, https://openalex.org/W1964962870, https://openalex.org/W2164878629, https://openalex.org/W2806259446, https://openalex.org/W3128452405, https://openalex.org/W3123686114, https://openalex.org/W3124443940, https://openalex.org/W2792641098, https://openalex.org/W2367397349, https://openalex.org/W2510508396, https://openalex.org/W2914029945, https://openalex.org/W2911964244, https://openalex.org/W3125203539, https://openalex.org/W2962772482, https://openalex.org/W2962790223, https://openalex.org/W2282821441, https://openalex.org/W2795530988, https://openalex.org/W2788362053, https://openalex.org/W1728842521, https://openalex.org/W1957375299, https://openalex.org/W3125947019, https://openalex.org/W2594475271 |
| referenced_works_count | 22 |
| abstract_inverted_index.a | 22, 59, 62, 76, 93, 135 |
| abstract_inverted_index.(a | 54, 70 |
| abstract_inverted_index.In | 164 |
| abstract_inverted_index.To | 126 |
| abstract_inverted_index.We | 37, 146 |
| abstract_inverted_index.as | 150 |
| abstract_inverted_index.be | 27, 35 |
| abstract_inverted_index.by | 29 |
| abstract_inverted_index.in | 48 |
| abstract_inverted_index.is | 168 |
| abstract_inverted_index.it | 19 |
| abstract_inverted_index.of | 3, 42, 87, 111, 131, 153 |
| abstract_inverted_index.on | 39, 61, 105, 118, 142, 158 |
| abstract_inverted_index.to | 9, 26, 57, 73, 82 |
| abstract_inverted_index.we | 99, 133 |
| abstract_inverted_index.But | 16 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 31, 65, 113, 177, 183 |
| abstract_inverted_index.are | 121, 181, 184 |
| abstract_inverted_index.can | 33 |
| abstract_inverted_index.for | 11, 21 |
| abstract_inverted_index.has | 7 |
| abstract_inverted_index.how | 32 |
| abstract_inverted_index.if" | 67, 115 |
| abstract_inverted_index.led | 8 |
| abstract_inverted_index.run | 58 |
| abstract_inverted_index.the | 49, 83, 88, 109, 128, 138, 143, 151, 159, 171 |
| abstract_inverted_index.two | 40 |
| abstract_inverted_index.via | 13 |
| abstract_inverted_index.been | 46 |
| abstract_inverted_index.does | 18 |
| abstract_inverted_index.find | 147 |
| abstract_inverted_index.have | 45 |
| abstract_inverted_index.mean | 20 |
| abstract_inverted_index.more | 185 |
| abstract_inverted_index.test | 100 |
| abstract_inverted_index.than | 187 |
| abstract_inverted_index.that | 44, 107, 120, 149, 174 |
| abstract_inverted_index.this | 34, 166 |
| abstract_inverted_index.user | 94 |
| abstract_inverted_index.well | 104 |
| abstract_inverted_index.what | 17 |
| abstract_inverted_index.with | 96, 170 |
| abstract_inverted_index."what | 66, 114 |
| abstract_inverted_index.1,000 | 97 |
| abstract_inverted_index.calls | 10 |
| abstract_inverted_index.count | 141 |
| abstract_inverted_index.focus | 38 |
| abstract_inverted_index.given | 63, 85 |
| abstract_inverted_index.local | 68, 80, 116, 160 |
| abstract_inverted_index.mimic | 108 |
| abstract_inverted_index.model | 14, 25, 60 |
| abstract_inverted_index.study | 95 |
| abstract_inverted_index.task. | 145 |
| abstract_inverted_index.tasks | 106, 162 |
| abstract_inverted_index.tools | 6 |
| abstract_inverted_index.track | 127 |
| abstract_inverted_index.trees | 176 |
| abstract_inverted_index.under | 79 |
| abstract_inverted_index.common | 172 |
| abstract_inverted_index.employ | 134 |
| abstract_inverted_index.humans | 102 |
| abstract_inverted_index.input) | 64 |
| abstract_inverted_index.input, | 84 |
| abstract_inverted_index.models | 119, 180 |
| abstract_inverted_index.neural | 188 |
| abstract_inverted_index.number | 152 |
| abstract_inverted_index.simple | 136 |
| abstract_inverted_index.user's | 55, 71 |
| abstract_inverted_index.Through | 92 |
| abstract_inverted_index.ability | 56, 72 |
| abstract_inverted_index.changes | 81 |
| abstract_inverted_index.humans, | 30 |
| abstract_inverted_index.locally | 124 |
| abstract_inverted_index.machine | 4, 23, 50 |
| abstract_inverted_index.metric, | 137 |
| abstract_inverted_index.model's | 77, 89 |
| abstract_inverted_index.models, | 132 |
| abstract_inverted_index.perform | 103 |
| abstract_inverted_index.runtime | 139 |
| abstract_inverted_index.whether | 101 |
| abstract_inverted_index.accuracy | 157 |
| abstract_inverted_index.adoption | 2 |
| abstract_inverted_index.decision | 175 |
| abstract_inverted_index.evidence | 148, 167 |
| abstract_inverted_index.learning | 5, 24, 51 |
| abstract_inverted_index.logistic | 178 |
| abstract_inverted_index.original | 90 |
| abstract_inverted_index.relative | 129 |
| abstract_inverted_index.addition, | 165 |
| abstract_inverted_index.assessed? | 36 |
| abstract_inverted_index.correctly | 74 |
| abstract_inverted_index.determine | 75 |
| abstract_inverted_index.intuition | 173 |
| abstract_inverted_index.knowledge | 86 |
| abstract_inverted_index.networks. | 189 |
| abstract_inverted_index.operation | 140 |
| abstract_inverted_index.typically | 122 |
| abstract_inverted_index.considered | 123 |
| abstract_inverted_index.consistent | 169 |
| abstract_inverted_index.decreases. | 163 |
| abstract_inverted_index.increases, | 155 |
| abstract_inverted_index.increasing | 1 |
| abstract_inverted_index.introduced | 47 |
| abstract_inverted_index.operations | 154 |
| abstract_inverted_index.prediction | 78 |
| abstract_inverted_index.regression | 179 |
| abstract_inverted_index.definitions | 41, 110 |
| abstract_inverted_index.literature: | 52 |
| abstract_inverted_index.participant | 156 |
| abstract_inverted_index.prediction). | 91 |
| abstract_inverted_index.interpretable | 28, 182, 186 |
| abstract_inverted_index.participants, | 98 |
| abstract_inverted_index.accountability | 12 |
| abstract_inverted_index.explainability | 69, 117 |
| abstract_inverted_index.interpretable. | 125 |
| abstract_inverted_index.simulatability | 53, 112, 144 |
| abstract_inverted_index.interpretability | 43, 130, 161 |
| abstract_inverted_index.interpretability. | 15 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |