=Paper=
{{Paper
|id=Vol-2456/paper11
|storemode=property
|title=Explaining Disclosure Decisions Over Personal Data
|pdfUrl=https://ceur-ws.org/Vol-2456/paper11.pdf
|volume=Vol-2456
|authors=Roghaiyeh Gachpaz Hamed,Harshvardhan J. Pandit,Declan O’Sullivan,Owen Conlan
|dblpUrl=https://dblp.org/rec/conf/semweb/HamedPOC19
}}
==Explaining Disclosure Decisions Over Personal Data==
https://ceur-ws.org/Vol-2456/paper11.pdf
Explaining Disclosure Decisions Over Personal Data
Roghaiyeh (Ramisa) Gachpaz Hamed, Harshvardhan J. Pandit, Declan O’Sullivan,
Owen Conlan
ADAPT Centre, Trinity College Dublin, Ireland
{ramisa.hamed,harshvardhan.pandit,declan.osullivan,owen.conlan
}@adaptcentre.ie
Abstract. The use of automated decision making systems to disclose personal
data provokes privacy concerns as it is difficult for individuals to understand how
and why these decisions are made. This research proposes an approach for
empowering individuals to understand such automated access to personal data by
utilising semantic web technologies to explain complex disclosure decisions in a
comprehensible manner. We demonstrate the feasibility of our approach through
a prototype that uses text and visual mediums to explain disclosure decisions
made in the health domain and its evaluation through a user study.
1 Introduction
While individuals understand the value of their personal data, they are largely
concerned with its potential misuse by businesses and governments [4], and bemoan
the lack of a trusted entity that affords them control or advice regarding protection of
their data [5]. Legislation such as the EU's General Data Protection Regulation
(GDPR)1 aim to preserve privacy by making data processing more transparent and
accountable. Recital 71 of the GDPR additionally states the “right to explanation” for
automated-decisions with significant effects for individuals. Providing more
information and control is beneficial to all stakeholders as individuals who perceive
themselves to be in control over the release and access of their private information (even
information that allows them to be personally identified) have greater willingness to
disclose this information [2].
This research believes that technology can be used for social good by having
automated decisions over the access and disclosure of personal data if provided with
transparent explanations of the decisions made. To this end, we present a framework
for empowering individuals to understand how and why a decision was made regarding
access to their personal data. We present the feasibility of our approach through a
prototype for a use-case in the health domain. The prototype utilises semantic web
technologies to explain complex disclosure decisions of a reasoning process through
textual and visual representations.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
1
Regulation (EU) 2016/679. Official Journal of the European Union. L119, 1–88 (2016)
2 Framework & Prototype Implementation
The aim of our framework is to enable individuals to understand automated disclosure
decisions over their data by explaining the facts and logic for how the system arrived
at a particular decision. The framework is comprised of the following components: (1)
Data Resources: includes personal data and a knowledge base, comprised of - domain
ontologies, privacy rules defined by individuals or regulation and logs that record
decision history. (2) Actors: includes data owners - individuals to whom the personal
data relates, and data requesters - entities that request access to personal data. (3)
Functional Components: these include automatic and semi-automatic decision
making units, a logger to record all decisions, and a context-discovery unit for gathering
contextual information about actors for use in the decisions. The decision maker unit
utilises a semantic reasoner over the collected knowledge-base and disclosure history
to decide the response for access requests. These decisions are intended to be automatic
with the decision explainer capable of providing an explanation, but can be semi-
automatic where the decision maker does not have sufficient information or when the
data owner needs to (manually) change a decision - in which case the confirmer obtains
confirmation to make the disclosure.
We implemented a prototype using semantic web technologies with a focus on
explaining the inference of semi-automatic disclosure decisions over personal data. It
uses RDF/OWL to define the data graph, with human-readable information using
rdfs:label for each node. The data disclosure rules are defined using SWRL2 with a
human-readable description. Requests for data access are added as triples using the
Apache Jena API3, and the Pellet reasoner4 is used to match requests with privacy rules,
where matched rules are retrieved using SPARQL5 and indicate granted access to data.
The explanation of disclosure decisions was provided using human-readable
descriptions for entailments in the form of text and visual representation as depicted in
Fig. 1. We used OWL Explanation [3] to obtain the entailment as a set of axioms
consisting of the minimal subset of the data graph sufficient for the entailment and the
corresponding SWRL rules used for making decisions. The axioms were filtered to
remove ontological declarations, type information for instances, and domain/range
information for properties. The access request was also removed as it is considered the
outcome rather than an explanation. The axioms were then reduced by substituting type
declarations with the rdfs:label of the declared concepts. Finally, text representations
were generated by translating each triple into a statement, and visual representation
were generated by using GraphViz6.
2
I. Horrocks et al., “SWRL : A Semantic Web Rule Language Combining OWL and RuleML,” W3C Member
submission 21.79 (2004).
3
http://jena.apache.org/documentation/ontology/
4
E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz,“Pellet: A practical OWL-DL reasoner”Web
Semantics: science, services & agents on the World Wide Web 5.2 (2007)
5
https://www.w3.org/TR/rdf-sparql-query/
6
https://www.graphviz.org/
Fig. 1. Overview of textual and visual representation for explanation
3 User-Study
The user-study consisted of participants being shown two scenarios with disclosure
decisions and their explanation for issues in the health-domain [6]. Each user was
shown a disclosure decision and its explanation with textual representation for one
scenario and a visual representation for the other which allowed us to compare the
understandability of textual and visual mediums for explanations. The users first had to
provide explicit informed consent for the study, after which a questionnaire solicited
users’ perceptions about disclosure decisions and access to data. The users were then
shown two scenarios with their disclosure decision explained using a random
permutation of textual and visual mediums. Each scenario was followed by a SUS [1]
questionnaire assessing usability, three comprehension questions and plus one attention
question, followed by an ASQ7 questionnaire assessing satisfiability.
Three comprehension questions enquired user’s understanding of explanation for
disclosure decision, where two were multiple choice (MCQ) and one was multiple
choice with multiple answers (MA). Scoring was from 0 to +3 where higher indicated
better comprehension, and was based on awarding +1 for correct choice in MCQ and
+1/n for MA where n was total number of options.
We used Prolific8 to recruit 21 participants which consisted of paying £2.50 for
25mins required to complete the tasks. One participant was rejected for failing to
answer the attention question, and results were analysed for remaining 20 participants,
as shown in Table 1. The results for SUS indicate good usability being above 71.4 [1],
and those for ASQ (lower is better)7 similarly indicate acceptable satisfaction regarding
7
After-Scenario Questionnaire - Lewis, J. R. (1991): Psychometric evaluation of an after-scenario
questionnaire for computer usability studies: the ASQ
8
https://prolific.ac/
provided explanations in both mediums. The scores for comprehension similarly
represent sufficient understanding of the explanations, though values for the
comparatively simpler Scenario 1 reflect better understanding as compared with the
more complex Scenario 2. The lack of sufficient difference between scores for text and
visual mediums indicates similar comprehension and understanding for both scenarios,
and show that the values are not conclusive to decide their comparative effectiveness.
Table. 1. Overview of textual and visual representation for explanation
S1 Text S1 Visual S2 Text S2 Visual
Mean SD Mean SD Mean SD Mean SD
SUS 77.25 23.44 72.75 15.61 71.5 10.55 74.25 17.25
Comprehension 2.43 0.49 2.23 0.78 1.93 0.8 1.98 0.98
ASQ 2.53 1.19 2.17 0.97 2.67 1.2 2.2 0.61
4 Conclusion and Future Work
This paper addressed privacy concerns regarding automated disclosure decisions over
personal data using a framework that provides explanations to empower users to
understand the reason of access to their personal data. The paper showed feasibility of
the framework through a prototype implemented using semantic web technologies to
explain disclosure decisions using textual and visual mediums. A user-study evaluation
of showed acceptable comprehension of explanations based on the prototype.
For future work, we plan to undertake further user-studies involving greater number
of participants and complex scenarios in order to compare the effectiveness of text and
visual mediums on the ability of participants to comprehend disclosure decisions. We
also plan to incorporate graph-summarisation techniques and queries to simplify
complex scenarios and improve explanations of decisions.
Acknowledgement. This research is supported by the ADAPT Centre for Digital
Content Technology, is funded under the SFI Research Centres Programme (Grant
13/RC/2106) and is co-funded under the European Regional Development Fund.
References
1. Bangor, A., Staff, T., Kortum, P., Miller, J., & Staff, T. Determining What Individual
SUS Scores Mean : Adding an Adjective Rating Scale, 4(3), 114–123. (2009).
2. Brandimarte, L., Acquisti, A., & Loewenstein, G. . Misplaced Confidences: Privacy and
the Control Paradox. Social Psychological and Personality Science, 4(3), (2013)
3. Horridge, M., Parsia, B., &x Sattler, U.. The OWL Explanation Workbench : A toolkit
for working with justifications for entailments in OWL ontologies, 1, 1–5. (2009)
4. Morey, T., Forbath, T., & Schoop, A. Customer Data : Designing for Transparency and
Trust. (2016).
5. Web Foundation. PERSONAL DATA: An overview of low and middle-income
countries. (2017)
6. Yüksel, B., Küpçü, A., & Özkasap, Ö. (2017). Research issues for privacy and security
of electronic health services. Future Generation Computer Systems, 68, 1–13. (2017)