Data-Debugging Through Interactive Visual Explanations

Shazia Afzal; Arunima Chaudhary; Nitin Gupta; Hima Patel; Carolina Spina; Dakuo Wang

doi:10.1007/978-3-030-75015-2_14

Data-Debugging Through Interactive Visual Explanations

Shazia Afzal^*, Arunima Chaudhary, Nitin Gupta, Hima Patel, Carolina Spina, Dakuo Wang

^*Corresponding author for this work

IBM

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Data readiness analysis consists of methods that profile data and flag quality issues to determine the AI readiness of a given dataset. Such methods are being increasingly used to understand, inspect and correct anomalies in data such that their impact on downstream machine learning is limited. This often requires a human in the loop for validation and application of remedial actions. In this paper we describe a tool to assist data workers in this task by providing rich explanations to results obtained through data readiness analysis. The aim is to allow interactive visual inspection and debugging of data issues to enhance interpretability as well as facilitate informed remediation actions by humans in the loop.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings
Editors	Manish Gupta, Ganesh Ramakrishnan
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	133-142
Number of pages	10
ISBN (Print)	9783030750145
DOIs	https://doi.org/10.1007/978-3-030-75015-2_14
Publication status	Published - 2021
Externally published	Yes
Event	Workshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 Workshop on Machine Learning for MEasurement Informatics, MLMEIN 2021, 1st Workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st International Workshop on Data Assessment and Readiness for AI, DARAI 2021 and 1st International Workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 - Virtual, Online Duration: 11 May 2021 → 14 May 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12705 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 Workshop on Machine Learning for MEasurement Informatics, MLMEIN 2021, 1st Workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st International Workshop on Data Assessment and Readiness for AI, DARAI 2021 and 1st International Workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021
City	Virtual, Online
Period	11/05/21 → 14/05/21

Keywords

Data quality
Data readiness
Explainability
Human-in-the-loop
Interactive data debugging
Visual analytics

Access to Document

10.1007/978-3-030-75015-2_14

Cite this

Afzal, S., Chaudhary, A., Gupta, N., Patel, H., Spina, C., & Wang, D. (2021). Data-Debugging Through Interactive Visual Explanations. In M. Gupta, & G. Ramakrishnan (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings (pp. 133-142). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12705 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-75015-2_14

Afzal, Shazia ; Chaudhary, Arunima ; Gupta, Nitin et al. / Data-Debugging Through Interactive Visual Explanations. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings. editor / Manish Gupta ; Ganesh Ramakrishnan. Springer Science and Business Media Deutschland GmbH, 2021. pp. 133-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{800dba5d689340beb95ffed24baec9bf,

title = "Data-Debugging Through Interactive Visual Explanations",

abstract = "Data readiness analysis consists of methods that profile data and flag quality issues to determine the AI readiness of a given dataset. Such methods are being increasingly used to understand, inspect and correct anomalies in data such that their impact on downstream machine learning is limited. This often requires a human in the loop for validation and application of remedial actions. In this paper we describe a tool to assist data workers in this task by providing rich explanations to results obtained through data readiness analysis. The aim is to allow interactive visual inspection and debugging of data issues to enhance interpretability as well as facilitate informed remediation actions by humans in the loop.",

keywords = "Data quality, Data readiness, Explainability, Human-in-the-loop, Interactive data debugging, Visual analytics",

author = "Shazia Afzal and Arunima Chaudhary and Nitin Gupta and Hima Patel and Carolina Spina and Dakuo Wang",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; Workshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 Workshop on Machine Learning for MEasurement Informatics, MLMEIN 2021, 1st Workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st International Workshop on Data Assessment and Readiness for AI, DARAI 2021 and 1st International Workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 ; Conference date: 11-05-2021 Through 14-05-2021",

year = "2021",

doi = "10.1007/978-3-030-75015-2_14",

language = "English",

isbn = "9783030750145",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "133--142",

editor = "Manish Gupta and Ganesh Ramakrishnan",

booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings",

address = "Germany",

}

Afzal, S, Chaudhary, A, Gupta, N, Patel, H, Spina, C & Wang, D 2021, Data-Debugging Through Interactive Visual Explanations. in M Gupta & G Ramakrishnan (eds), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12705 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 133-142, Workshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 Workshop on Machine Learning for MEasurement Informatics, MLMEIN 2021, 1st Workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st International Workshop on Data Assessment and Readiness for AI, DARAI 2021 and 1st International Workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021, Virtual, Online, 11/05/21. https://doi.org/10.1007/978-3-030-75015-2_14

Data-Debugging Through Interactive Visual Explanations. / Afzal, Shazia; Chaudhary, Arunima; Gupta, Nitin et al.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings. ed. / Manish Gupta; Ganesh Ramakrishnan. Springer Science and Business Media Deutschland GmbH, 2021. p. 133-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12705 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data-Debugging Through Interactive Visual Explanations

AU - Afzal, Shazia

AU - Chaudhary, Arunima

AU - Gupta, Nitin

AU - Patel, Hima

AU - Spina, Carolina

AU - Wang, Dakuo

PY - 2021

Y1 - 2021

N2 - Data readiness analysis consists of methods that profile data and flag quality issues to determine the AI readiness of a given dataset. Such methods are being increasingly used to understand, inspect and correct anomalies in data such that their impact on downstream machine learning is limited. This often requires a human in the loop for validation and application of remedial actions. In this paper we describe a tool to assist data workers in this task by providing rich explanations to results obtained through data readiness analysis. The aim is to allow interactive visual inspection and debugging of data issues to enhance interpretability as well as facilitate informed remediation actions by humans in the loop.

AB - Data readiness analysis consists of methods that profile data and flag quality issues to determine the AI readiness of a given dataset. Such methods are being increasingly used to understand, inspect and correct anomalies in data such that their impact on downstream machine learning is limited. This often requires a human in the loop for validation and application of remedial actions. In this paper we describe a tool to assist data workers in this task by providing rich explanations to results obtained through data readiness analysis. The aim is to allow interactive visual inspection and debugging of data issues to enhance interpretability as well as facilitate informed remediation actions by humans in the loop.

KW - Data quality

KW - Data readiness

KW - Explainability

KW - Human-in-the-loop

KW - Interactive data debugging

KW - Visual analytics

UR - http://www.scopus.com/inward/record.url?scp=85106407720&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-75015-2_14

DO - 10.1007/978-3-030-75015-2_14

M3 - Conference contribution

AN - SCOPUS:85106407720

SN - 9783030750145

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 133

EP - 142

BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings

A2 - Gupta, Manish

A2 - Ramakrishnan, Ganesh

PB - Springer Science and Business Media Deutschland GmbH

T2 - Workshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 Workshop on Machine Learning for MEasurement Informatics, MLMEIN 2021, 1st Workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st International Workshop on Data Assessment and Readiness for AI, DARAI 2021 and 1st International Workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021

Y2 - 11 May 2021 through 14 May 2021

ER -

Afzal S, Chaudhary A, Gupta N, Patel H, Spina C, Wang D. Data-Debugging Through Interactive Visual Explanations. In Gupta M, Ramakrishnan G, editors, Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 133-142. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-75015-2_14

Data-Debugging Through Interactive Visual Explanations

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this