Tool Criticism

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Slides for my talk at the Scientific Meeting at Centrum Wiskunde & Informatica, 29/05/2015 A standard procedure in humanities research is to evaluate the aptitude of a source by performing source criticism. This includes the examination of the validity, reliability and relevance of a source in the context of a given research task. By turning the physical manifestations of the sources into digital objects, the sources are transformed by the technology applied to them. Some of the used tools, such as OCR engines, do not yield error-free results. For humanities researchers, this means that they do not only have to deal with source-inherent biases, but they also have to take into account tool-induced bias. The aim of our research is to develop methods that enable humanities researchers to perform tool criticism by revealing uncertainty in the data by tracing tool-induced bias.

Tool Criticism
Myriam C. Traub, Jacco van Ossenbruggen, Lynda Hardman!
Information Access

“Amsterdam”
1642
“Amfterdam”
1624
“?”
16xx?
2
First mention of …
1618
… in the OCRed newspaper archive of the KB?
“Amslerdam”
1673
earliest !
document

01
Digital
Humanities
✤ digital archives / large data
collections!
✤ collection bias / digitization policy!
✤ digital representation ≠ physical
object!
✤ tools are imperfect !
✤ source cannot be considered
independent from tools!
✤ need methods to detect tool-
induced bias
3

01
Source criticism
✤ well established method in the
humanities to detect bias in a
source!
✤ Who is the author?!
✤ Is the information current?!
✤ Is the information objective
and credible?…!
✤ need a similar approach for
tool-induced bias: !
! tool criticism
4

01
Methodology
✤ interviews with humanities scholars!
✤ classiﬁcation of common research
tasks!
✤ lack of trust blocks progress!
✤ use case: digital newspaper archive
of KB !
✤ no formal OCR evaluation!
✤ useful for scholars?!
✤ mismatch between two
perspectives
5

We care about
average performance
on representative
subsets for generic
cases.
I care about
actual performance
on my non-
representative subset
for my speciﬁc
query.
6
Two different perspectives of quality evaluation

No silver bullet
✤ we propose novel strategies that solve
part of the problem:!
✤ critical attitude !
(awareness and better support)!
✤ transparency !
(provenance, open source,
documentation, …)!
✤ alternative quality metrics!
(taking research context into account)
7
M. Traub, J. van Ossenbruggen, L. Hardman, !
Impact Analysis of the OCR Quality Problem in Digital Archives, TPDL2015 (under review)

8
“Amsterdam”
Context-aware quality indicators
query
data
task

01
Future work
✤ What strategies should a tool
support to help scholars discover
and dealing with bias?!
✤ What is a good way of
estimating uncertainty for a
speciﬁc task?!
✤ Can we crowdsource (part of the
data for) better estimates?!
✤ What is a good way of conveying
the estimated impact to scholars?
9
… questions?

This is the set of slides for my presentation at JCDL 2016 in Newark, USA. Bias in the retrieval of documents can directly influence the information access of a digital library. In the worst case, systematic favoritism for a certain type of document can render other parts of the collection invisible to users. This potential bias can be evaluated by measuring the retrievability for all documents in a collection. Previous evaluations have been performed on TREC collections using simulated query sets. The question remains, however, how representative this approach is of more realistic settings. To address this question, we investigate the effectiveness of the retrievability measure using a large digitized newspaper corpus, featuring two characteristics that distinguishes our experiments from previous studies: (1) compared to TREC collections, our collection contains noise originating from OCR processing, historical spelling and use of language; and (2) instead of simulated queries, the collection comes with real user query logs including click data. First, we assess the retrievability bias imposed on the newspaper collection by different IR models. We assess the retrievability measure and confirm its ability to capture the retrievability bias in our setup. Second, we show how simulated queries differ from real user queries regarding term frequency and prevalence of named entities, and how this affects the retrievability results.

Querylog-based Assessment of Retrievability Bias in Delpher

Myriam Traub

Search engines are not “objective" pieces of technology, and bias in Delpher's search engine may or may not harm user access to certain type of documents in the collection. In the worst case, systematic favoritism for a certain type can render other parts of the collection invisible to users. This potential bias can be evaluated by measuring the “retrievability" for all documents in a collection. We explain the ideas underlying the retrievability metric, and how we measured it on the KB Newspaper collection. We describe and quantify the retrievability bias imposed on the newspaper collection by three different commonly used Information Retrieval models. For this, we investigated how document features such as length, type, or date of publishing influence the retrievability. We also investigate the effectiveness of the retrievability measure, featuring two characteristics that set our experiments apart from previous studies: (1) the newspaper collection contains noise originating from OCR processing, and historical spelling and use of language; and (2) rather than the simulated queries used in other studies, we use real user query logs including click data. We show how simulated queries differ from real user queries regarding term frequency and prevalence of named entities, and how this affects the results of a retrieval task.

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Humanities scholars increasingly rely on digital archives for their research instead of time-consuming visits to physical archives. This shift in research method has the hidden cost of working with digitally processed historical documents: how much trust can a scholar place in noisy representations of source texts? In a series of interviews with historians about their use of digital archives, we found that scholars are aware that optical character recognition (OCR) errors may bias their results. They were, however, unable to quantify this bias or to indicate what information they would need to estimate it. This, however, would be important to assess whether the results are publishable. Based on the interviews and a literature study, we provide a classification of scholarly research tasks that gives account of their susceptibility to specific OCR- induced biases and the data required for uncertainty estimations. We conducted a use case study on a national newspaper archive with example research tasks. From this we learned what data is typically available in digital archives and how it could be used to reduce and/or assess the uncertainty in result sets. We conclude that the current knowledge situation on the users’ side as well as on the tool makers’ and data providers’ side is insufficient and needs to be improved.

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Tool Criticism

1. Tool Criticism Myriam C. Traub, Jacco van Ossenbruggen, Lynda Hardman! Information Access

2. “Amsterdam” 1642 “Amfterdam” 1624 “?” 16xx? 2 First mention of … 1618 … in the OCRed newspaper archive of the KB? “Amslerdam” 1673 earliest ! document

3. 01 Digital Humanities ✤ digital archives / large data collections! ✤ collection bias / digitization policy! ✤ digital representation ≠ physical object! ✤ tools are imperfect ! ✤ source cannot be considered independent from tools! ✤ need methods to detect tool- induced bias 3

4. 01 Source criticism ✤ well established method in the humanities to detect bias in a source! ✤ Who is the author?! ✤ Is the information current?! ✤ Is the information objective and credible?…! ✤ need a similar approach for tool-induced bias: ! ! tool criticism 4

5. 01 Methodology ✤ interviews with humanities scholars! ✤ classiﬁcation of common research tasks! ✤ lack of trust blocks progress! ✤ use case: digital newspaper archive of KB ! ✤ no formal OCR evaluation! ✤ useful for scholars?! ✤ mismatch between two perspectives 5

6. We care about average performance on representative subsets for generic cases. I care about actual performance on my non- representative subset for my speciﬁc query. 6 Two different perspectives of quality evaluation

7. No silver bullet ✤ we propose novel strategies that solve part of the problem:! ✤ critical attitude ! (awareness and better support)! ✤ transparency ! (provenance, open source, documentation, …)! ✤ alternative quality metrics! (taking research context into account) 7 M. Traub, J. van Ossenbruggen, L. Hardman, ! Impact Analysis of the OCR Quality Problem in Digital Archives, TPDL2015 (under review)

8. 8 “Amsterdam” Context-aware quality indicators query data task

9. 01 Future work ✤ What strategies should a tool support to help scholars discover and dealing with bias?! ✤ What is a good way of estimating uncertainty for a speciﬁc task?! ✤ Can we crowdsource (part of the data for) better estimates?! ✤ What is a good way of conveying the estimated impact to scholars? 9 … questions?

Tool Criticism

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Tool Criticism