1,168 research outputs found
Is That Your Final Decision? Multi-Stage Profiling, Selective Effects, and Article 22 of the GDPR
Provisions in many data protection laws require a legal basis, or at the very least safeguards, for significant, solely automated decisions; Article 22 of the GDPR is the most notable. - Little attention has been paid to Article 22 in light of decision-making processes with multiple stages, potentially both manual and automated, and which together might impact upon decision subjects in different ways. - Using stylised examples grounded in real-world systems, we raise five distinct complications relating to interpreting Article 22 in the context of such multi-stage profiling systems. - These are: the potential for selective automation on subsets of data subjects despite generally adequate human input; the ambiguity around where to locate the decision itself; whether 'significance' should be interpreted in terms of any potential effects or only selectively in terms of realised effects; the potential for upstream automation processes to foreclose downstream outcomes despite human input; and that a focus on the final step may distract from the status and importance of upstream processes. - We argue that the nature of these challenges will make it difficult for courts or regulators to distil a set of clear, fair and consistent interpretations for many realistic contexts
When Data Protection by Design and Data Subject Rights Clash
• Data Protection by Design (DPbD), a holistic approach to embedding principles
in technical and organisational measures undertaken by data controllers,
building on the notion of Privacy by Design, is now a qualified
duty in the GDPR.
• Practitioners have seen DPbD less holistically, instead framing it through
the confidentiality-focussed lens of Privacy Enhancing Technologies
(PETs).
• While focussing primarily on confidentiality risk, we show that some
DPbD strategies deployed by large data controllers result in personal data
which, despite remaining clearly reidentifiable by a capable adversary,
make it difficult for the controller to grant data subjects rights (eg access,
erasure, objection) over for the purposes of managing this risk.
• Informed by case studies of Apple’s Siri voice assistant and Transport for
London’s Wi-Fi analytics, we suggest three main ways to make deployed
DPbD more accountable and data subject–centric: building parallel systems
to fulfil rights, including dealing with volunteered data; making inevitable
trade-offs more explicit and transparent through Data Protection
Impact Assessments; and through ex ante and ex post information rights
(arts 13–15), which we argue may require the provision of information
concerning DPbD trade-offs.
• Despite steep technical hurdles, we call both for researchers in PETs to
develop rigorous techniques to balance privacy-as-control with privacyas-confidentiality,
and for DPAs to consider tailoring guidance and future
frameworks to better oversee the trade-offs being made by primarily wellintentioned
data controllers employing DPbD
The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility
This paper proposes that two significant and emerging problems facing our connected, data-driven
society may be more effectively solved by being framed as sensemaking challenges. The first is in
empowering individuals to take control of their privacy, in device-rich information environments
where personal information is fed transparently to complex networks of information brokers. Although
sensemaking is often framed as an analytical activity undertaken by experts, due to the fact that
non-specialist end-users are now being forced to make expert-like decisions in complex information
environments, we argue that it is both appropriate and important to consider sensemaking challenges
in this context. The second is in supporting human-in-the-loop algorithmic decision-making, in which
important decisions bringing direct consequences for individuals, or indirect consequences for groups,
are made with the support of data-driven algorithmic systems. In both privacy and algorithmic decision-making, framing the problems as sensemaking challenges acknowledges complex and illdefined
problem structures, and affords the opportunity to view these activities as both building up
relevant expertise schemas over time, and being driven potentially by recognition-primed decision
making
Detecting Sarcasm in Multimodal Social Platforms
Sarcasm is a peculiar form of sentiment expression, where the surface
sentiment differs from the implied sentiment. The detection of sarcasm in
social media platforms has been applied in the past mainly to textual
utterances where lexical indicators (such as interjections and intensifiers),
linguistic markers, and contextual information (such as user profiles, or past
conversations) were used to detect the sarcastic tone. However, modern social
media platforms allow to create multimodal messages where audiovisual content
is integrated with the text, making the analysis of a mode in isolation
partial. In our work, we first study the relationship between the textual and
visual aspects in multimodal posts from three major social media platforms,
i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to
quantify the extent to which images are perceived as necessary by human
annotators. Moreover, we propose two different computational frameworks to
detect sarcasm that integrate the textual and visual modalities. The first
approach exploits visual semantics trained on an external dataset, and
concatenates the semantics features with state-of-the-art textual features. The
second method adapts a visual neural network initialized with parameters
trained on ImageNet to multimodal sarcastic posts. Results show the positive
effect of combining modalities for the detection of sarcasm across platforms
and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of
ACM Multimedia 201
'It's Reducing a Human Being to a Percentage'; Perceptions of Procedural Justice in Algorithmic Decisions
Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to ‘meaningful information about the logic’ behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles—under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best’ approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions
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