Some HCI Priorities for
GDPR-Compliant Machine Learning
Michael Veale
University College London
London, United Kingdom
m.veale@ucl.ac.uk

Reuben Binns
Max Van Kleek
University of Oxford
Oxford, United Kingdom
reuben.binns@cs.ox.ac.uk
emax@cs.ox.ac.uk

Abstract
In this short paper, we consider the roles of HCI in enabling
the better governance of consequential machine learning
systems using the rights and obligations laid out in the
recent 2016 EU General Data Protection Regulation
(GDPR)—a law which involves heavy interaction with
people and systems. Focussing on those areas that relate
to algorithmic systems in society, we propose roles for HCI
in legal contexts in relation to fairness, bias and
discrimination; data protection by design; data protection
impact assessments; transparency and explanations; the
mitigation and understanding of automation bias; and the
communication of envisaged consequences of processing.

Introduction

The General Data Protection Regulation: An Opportunity for the CHI Community?
(CHI-GDPR 2018), Workshop at ACM CHI’18, 22 April 2018, Montréal, Canada

The 2016 EU General Data Protection Regulation (GDPR)
is making waves. With all personal data relating to EU
residents or processed by EU companies within scope, it
seeks to strengthen the rights of data subjects and the
obligations of data controllers (see definitions in the box
overleaf) in an increasingly data-laden society, newly
underpinned with an overarching obligation of data
controller accountability as well as hefty maximum fines. Its
articles introduce new provisions and formalise existing
rights clarified by the European Court of Justice (the Court),
such as the “right to be forgotten”, as well as strengthening
those already present in the 1995 Data Protection Directive

arXiv:1803.06174v1 [cs.HC] 16 Mar 2018

(DPD).
Data Subjects & Controllers
EU DP law applies whenever
personal data is processed either in the Union, or outside
the Union relating to an EU
resident. Personal data is defined by how much it can render somebody identifiable—
going beyond email, phone
number, etc to include dynamic IP addresses, browser
fingerprints or smart meter
readings. The individual data
relates to is called the data
subject. The organisation(s)
who determine ‘the purposes
and means of the processing of personal data’ are
data controllers. Data subjects have rights over personal data, such as rights
of access, erasure, objection
to processing, and portability of data elsewhere. Data
controllers are subject to a
range of obligations, such as
ensuring confidentiality, notifying if data is breached,
and undertaking risk assessments. Additionally, they must
only process data where they
have a legal ground—such as
consent—to do so, for a specified and limited purpose, and
a limited period of storage.

The GDPR has been turned to by scholars and activists as
a tool for “algorithmic accountability” in a society where
machine learning (ML) seems to be increasingly important.
Machine learning models—statistical systems which use
data to improve their performance on particular tasks—are
the approach of choice to generate value from the ‘data
exhaust’ of digitised human activities. Critics, however, have
framed ML as powerful, opaque, and with potential to
endanger privacy [2], equality [10] and autonomy [20].
While the GDPR is intended to govern personal data rather
than ML, there are a range of included rights and
obligations which might be useful to exert control over
algorithmic systems [14].
Given that GDPR rights involve both individual
data-subjects and data controllers (see sidebar) interfacing
with computers in a wide variety of contexts, it strongly
implicates another abbreviation readers will likely find
familiar: Human–Computer Interaction (HCI). In this short
paper, we outline, non-exhaustively of course, some of the
crossovers between the GDPR provisions, HCI and ML that
appear most salient and pressing given current legal, social,
and technical debates. We group these in two broad
categories: those which primarily concern the building and
training of models before deployment, and those which
primarily concern the post-deployment application of
models to data subjects in particular situations.

HCI, GDPR and Model Training
An increasing proportion of collected personal data1 is used
to train machine learning systems, which are in turn used to
1
Note that the GDPR defines personal data broadly—including things
like dynamic IP addresses and home energy data—as opposed to the predominantly American notion of personally identifiable information (PII) [25].

make or support decisions in a variety of fields. As model
training with personal data is considered data processing
(assuming data is not solidly ‘anonymised’), the GDPR does
govern it to a varying degree. In this section, we consider to
what extent HCI might play a role in promoting the
governance of model training under the GDPR.
Fairness, discrimination and ‘special category’ data
Interest in unfair and/or illegal data-driven discrimination
has concerned researchers, journalists, pundits and
policy-makers [17, 3], particularly as the ease of
transforming seemingly non-sensitive data into potentially
damaging, private insights has become clear [9]. Most focus
on how to govern data (both in Europe and elsewhere
broadly [19]) has been centred on data protection, which is
not an anti-discrimination law and does not feature
anti-discrimination as a core concept. Yet the GDPR does
contain provisions which concern particularly sensitive
attributes of data.
Several “special” types of data are given higher protection in
the GDPR. The 1995 Data Protection Directive (art 8)
prohibits processing of data revealing racial or ethnic
origin, political opinions, religious or philosophical
beliefs and trade-union membership, in addition to data
concerning health or sex life. The GDPR (art 9(1)) adds
genetic and biometric data (the latter for the purposes of
identification), as well as clarifying sex life includes
orientation, to create 8 ‘special categories’ of data. This list
is similar, but not identical, to the ‘protected characteristics’
in many international anti-discrimination laws. Compared to
the UK’s Equality Act 2010, the GDPR omits age, sex and
marital status but includes political opinions, trade union
membership, and health data more broadly.
The collection, inference and processing of special category
data triggers both specific provisions (e.g. arts 9, 22) and

specific responsibilities (e.g. Data Protection Impact
Assessments, art 35 and below), as well as generally
heightening the level of risk of processing and therefore the
general responsibilities of a controller (art 24). Perhaps the
most important difference is that data controllers cannot rely
on their own legitimate interests to justify the processing of
special category data, which usually will mean they will
have to seek explicit, specified consent for the type of
processing they intend—which they may not have done for
their original data, and may not be built into their legal data
collection model.
Given that inferred special category data is also
characterised as special category data [28], there are
important questions around how both controllers and
regulators recognise that such inference is or might be
happening. Naturally, if a data controller trains a supervised
model for the purpose of inferring a special category of data,
this is quite a simple task (as long as they are honest about
it). Yet when they are using latent characteristics, such as
through principal components analysis, or features that are
embedded within a machine learning model, this becomes
more challenging. In particular it has been repeatedly
demonstrated that biases connected to special category
data can appear in trained systems even where those
special categories are not present in the datasets being
used [9].
The difficulty of this task is heightened by how the controller
is unlikely to possess ‘ground truth’ special category data in
order to assess what it is they are picking up. HCI might
play an important role here in establishing what has been
described as ‘exploratory fairness analysis’ [27]. The task is
to understand potential patterns of discrimination, or to
identify certain unexpected but sensitive clusters, with only
partial additional information abut the participants. A similar

proposal (and prototype of) a visual system, albeit one
assuming full information, has been proposed by
discrimination-aware data mining researchers concerned
that the formal statistical criteria for non-discrimination
established by researchers may not connect with ideas of
fairness in practice [4, 5]. If we do indeed also know
unfairness when we see it, exploratory visual analysis may
be a useful tool. A linked set of discussions have been
occurring in the information visualisation community around
desirable characteristics of feminist data visualisation, which
connects feminist principles around marginalisation and
dominance in the production of knowledge to information
design [11]. Finally, visual tools which help identify
misleading patterns in data, such as instances of Simpson’s
paradox (e.g. [24]), may prove useful in confirming apparent
disparities between groups. Building and testing interfaces
which help identify sensitive potential correlations and ask
critical questions around bias and discrimination in the data
is an important prerequisite to rigorously meeting
requirements in the GDPR.
Upstream provisions: Data Protection by Design (DPbD) and
Data Protection Impact Assessments (DPIAs)
The GDPR contains several provisions intended to move
considerations of risks to data subjects’ rights and freedoms
upstream into the hands of designers. Data Protection by
Design (DPbD), a close cousin of privacy by design, is a
requirement under the GDPR and means that controllers
should use organisational and technical measures to imbue
their products and processes with data protection
principles [8]. Data Protection Impact Assessments (DPIAs)
have a similar motivation [6]. Whenever controllers have
reason to believe that a processing activity brings high risks,
they must undertake continuous, documented analysis of
these, as well as any measures they are taking to mitigate.

The holistic nature of both DPbD and DPIAs is emphasised
in both the legal text and recent guidance. These are
creative processes mixing anticipation and foresight with
best practice and documentation. Some HCI research has
already addressed this in particular. Luger et al. [23] use
ideation cards to engage designers with regulation and
co-produce “data protection heuristics”.2 Whether DPIA
aides can be built into existing systems and software in a
user-centric way is an important area for future exploration.
Furthermore, many risks within data, such as bias, poor
representation, or the picking up of private features, may be
unknown to data controllers. Identifying these is the point of
a DPIA, but subtle issues are unlikely to leap out of the
page. In times like this, it has been suggested that a shared
knowledgebase [27] could be a useful resource, where
researchers and data controllers (or their modelling staff)
could log risks and issues in certain types of data from their
own experiences, creating a resource which might serve
useful in new situations. For example, such a system might
log found biases in public datasets (particularly when linked
to external data) or in whole genres of data, such as Wi-Fi
analytics or transport data. Such a system might be a useful
starting point for considering issues that may otherwise go
undetected, and for supporting low-capacity organisations
in their responsible use of analytics. From an HCI
perspective though, the design of such a system presents
significant challenges. How can often nuanced biases be
recorded and communicated both clearly and in such a way
that they generalise across applications? How might
individuals easily search a system for issues in their own
datasets, particularly when they might have a very large
number of variables in a combination the system has not
seen previously? Making this kind of knowledge accessible
to practitioners seems promising, but daunting.
2

The cards are downloadable at https://perma.cc/3VBQ-VVPQ.

HCI, GDPR and Model Application
The GDPR, and data protection law in general, was not
intended to significantly govern decision-making. Already a
strange law in the sense that it poses transparency
requirement that applies to the public and private sectors
alike, it is also a Frankenstein’s monster–style result
culminating from the melding of various European law and
global principles that preceded it [16].
Modes of Transparency
While transparency is generally spoken of as a virtue, the
causal link between it and better governance is rarely
simple or clear. A great deal of focus has been placed on
the so-called “right to an explanation”, where a short paper
at a machine learning conference workshop [18] gained
sudden notoriety, triggering reactions from lawyers and
technologists noting that the existence and applicability of
such a right was far from simple [29, 14]. Yet the
individualised transparency paradigm has rarely provided
much practical use for data subjects in their day-to-day lives
(consider the burden of ‘transparent’ privacy policies).
Consequently, HCI provides a useful place to start when
considering how to make the limited GDPR algorithmic
transparency provisions useful governance tools.
There are different places in which algorithmic transparency
rights can be found in the GDPR [29]. Each bring different
important HCI challenges.
Meaningful information about the logic of processing
Articles 13–14 oblige data controllers to provide information
at the time data is collected around the logics of certain
automated decision systems that might be applied to this
data. Current regulatory guidance [1] states that there is no
obligation to tailor this information to the specific situation of
a data subject (other than if they might be part of a

vulnerable group, like children, which might need further
support to make the information meaningful), although as
many provisions in data protection law, the Court may
interpret this more broadly or narrowly when challenged.
This points to an important HCI challenge in making (or
visualising) such general information, but with the potential
for specific relevance to individuals.
Right to be informed In addition, there is a so-called
‘right to be informed’ of automated decision-making [29]:
how might an interface seamlessly flag to users when a
potentially legally relevant automated decision is being
made? This is made more challenging by the potential for
adaptive interfaces or targeted advertising to meet the
criteria of a ‘decision’. In these cases, it is unclear at what
point the ‘decision’ is being made. Decisions might be seen
in the design process, or adaptive interfaces may be seen
as ‘deciding’ which information to provide or withold [14].
Exercise of data protection rights is different in further ways
in ambient environments [13], as smart cities and ambient
computing may bring significant challenges, if, for example,
they are construed as part of decision-making
environments. Existing work in HCI has focussed on the
difficulties in identifying “moments of consent” in ubiquitous
computing [22, 21]. Not only is this relevant when consent is
the legal basis for an automated decision, but additional
consideration will be needed in relation to what equivalent
“moments” of objection might look like. Given that moments
to object likely outnumber moments to consent, this might
pose challenges.
A right to an explanation? An explicit “right to an
explanation” of specific decisions, after they have happened,
sits in a non-binding recital in the GDPR [29], and thus its
applicability and enforceability depends heavily on
regulators and the Court. However, there is support for a

parallel right in varying forms in certain other laws, such as
French administrative law or the Council of Europe
Convention 108 [15], and HCI researchers have already
been testing different explanation facilities proposed by
machine learning researchers in qualitative and quantitative
settings to see how they compare in relation to different
notions of procedural justice [7]. Further research on
explanation facilities in-the-wild would be strongly welcome,
given that most explanation facilities to date have focussed
on the user of a decision-support system rather than an
individual subject to an automated decision.
Mitigating Automation Bias
A key trigger condition for the automated decision-making
provisions in the GDPR (art 22) [14] centres on the degree
of automation of the process. Significant decisions “based
solely on automated processing” require at least consent, a
contract or a basis in member state law. Recent regulatory
guidance indicates that there must be “meaningful” human
input undertaken by somebody with “authority and
competence” who does not simply “routinely apply” the
outputs of the model in order to be able to avoid
contestation or challenge [28]. Automation bias has long
been of interest to scholars of human factors in
computing [26, 12] and the GDPR provides two core
questions for HCI in this vein.
Firstly, this setup implies that systems that are expected to
outperform humans must always be considered “solely”
automated [28]. If a decision-making system is expected to
legitimately outperform humans it makes meaningful input
very difficult. Any routine disagreement would be at best
arbitrary and at worst, harmful. This serves as yet another
(legal) motivating factor to create systems where human
users can augment machine results. Even if this proves
difficult, when users contest an automated decision under

the GDPR, they have a right to human review. Interfaces
need to ensure that even where models may be complex
and high-dimensional, decision review systems are rigorous
and themselves have “meaningful” human input—or else
these reviewed decisions are equally open to contestation.
Secondly, how might a data controller or a regulator
understand whether systems have “meaningful” human
input or not, in order to either obey or enforce the law? How
might this input be justified and documented in a useful and
user-friendly way which could potentially be provided to the
subject of the decision? Recent French law does oblige this
in some cases: in the case of algorithmically-derived
administrative decisions, information should be provided to
decision-subjects on the “the degree and the mode of
contribution of the algorithmic processing to the
decision-making” [15]. Purpose-built interfaces and
increased knowledge from user studies both seem needed
for the aim of promoting meaningful, accountable input.
Communicating Envisaged Consequences
Where significant, automated decision-making using
machine learning is expected, the information rights in the
GDPR (arts 13–15) provide that a data subject should be
provided with the “envisaged consequences” of such
decision for her. What this means is far from clear. Recent
regulatory guidance provides only the example of giving
data subject applying for insurance premiums an app to
demonstrate the consequences of dangerous driving [1].
Where users are consenting to complex online
personalisation which could potentially bring significant
effects to their life, such as content delivery which might
lead to echo chambers or “filter bubbles”, it is unclear how
complex “envisaged consequences” might be best
displayed in order to promote user autonomy and choice.

Concluding remarks
HCI is well-placed to help enable the regulatory
effectiveness of the GDPR in relation to algorithmic fairness
and accountability. Here we have touched on different
points where governance might come into play—model
training and model application—but also different modes of
governance. Firstly, HCI might play a role in enabling
creative, rigorous, problem solving practices within
organisations. Many mechanisms in the GDPR, such as
data protection by design and data protection impact
assessments, will depend heavily on the communities,
practices and technologies that develop around them in
different contexts. Secondly, HCI might play a role in
enabling controllers do particular tasks better. Here, we
discussed the potential for exploratory data analysis tools,
such as detecting special category data even when it was
not explicitly collected. Finally, it might help data subjects
exercise their rights better. It appears especially important
to develop new modes and standards for transparency,
documentation of human input, and communication of tricky
notions such as “envisaged consequences”.
As the GDPR often defines data controllers’ obligations as a
function of “available technologies” and “technological
developments”, it is explicitly enabled and strengthened by
computational systems and practices designed with its
varied provisions in mind. Many parts of the HCI community
have already been building highly relevant technologies and
practices that could be applied in this way. Further
developing these with a regulatory focus might be
transformative in and of itself—and it is something we
believe should be promoted in this field and beyond.

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