It is now six
months since Harvey Weinstein’s activities unleashed a sea-change in the world
of workplace harassment. During that time, widespread adverse comment following
the Cambridge Analytica revelations prompted assurances by Mark Zuckerberg to
the effect that Facebook now understands that with great data comes great
responsibility. Meanwhile, concerns have been raised about diverse effects of
data automation: automated decision-making affecting humans; unaccountable
robots; excessive and intrusive surveillance; opaque, unreliable or
discriminatory algorithms; online echo chambers and fake news.
Many of these
concerns are also raised about AI. In addition, rapid developments in AI have
prompted a policy debate about whether we are skilling a workforce to work with
technology, and whether AI will deliver benefits to a few, while many citizens
are left behind. These concerns are exacerbated by a decline of faith in public
policymaking: trust of citizens in institutions and governments is at historic lows.
Can we ensure
that what have we learnt from Cambridge Analytica is applied to address these
Big data ethics
challenges in application of data analytics are nothing new.
To take one
relevant example, only three years ago Facebook data scientist Adam Kramer and
Cornell University social scientists Jamie Guillory and Jeff Hancock published
a peer reviewed report on their A/B study that experimentally modified the
Facebook feed algorithm for 689,003 people. The authors demonstrated that
changing the negative or positive emotional valence of posts on a user’s News
Feed affected the emotional valence of the posts made by the user after seeing
those posts. This supported the hypothesis that ‘emotional contagion’ –
spreading emotional states through social contact – occurs on a massive scale,
albeit with relatively small individual effects, in social networks.
Publication of the
report of this study launched a controversy about big data ethics, albeit
without the media and public attention recently focussed on Cambridge
Analytica. One criticism made of the study was that Facebook users whose feeds
were altered were not asked to consider this study and invited to provide
informed consent to participate in the study. Facebook suggested that its users
significantly more opaque than today.
questioned the ethics of platform providers experimenting with the emotional
state of their users.
suggested that the differences between this study and common online marketing
practices were that the experiment was not selling anything and that results
were published in a peer reviewed scientific journal.
What, no ethics review?
In any event, the study was not subjected to any form
of ethics review.
probably was not required to be reviewed because the US ‘Common Rule’ only
requires ethics review of ‘intervention’ in the life of human subjects by way
of ‘(human) research’.
Some critics noted
that the conduct of the study illustrated a broader concern, suggesting that
the data science community was not familiar with the ethics regulation found in
other science and technology communities. These critics suggested that data
scientists wrongly thought that many of their data projects concerned systems
and devices, not people, and therefore were human-related ethical concerns.
Of course, many
data driven systems and devices differentiate between humans having regard to
individual behaviour or inferred likely behaviour. This rightly raises issues
as to whether any adverse effects on some individuals, whether by inclusion or
exclusion, have been properly considered.
come to this new field with diverse training that often does not include any
training as to ethics, social science or psychology.
emotional contagion study illustrates the danger that data scientists and
technology developers may not see, and avoid or mitigate, ethical concerns with
projects which affect humans.
have a ‘right to know’ how information about them is used to manipulate their
Or that they are
being treated differently to other individuals?
How can social
responsibility and other ethical concerns be addressed without the slow and
complex processes for medical research ethics review?
Analytica magnified concerns already raised by the Facebook emotional contagion
study. It grabbed headlines and social media attention because the potential
impact was demonstrated through the backstory of its alleged role in delivering
the White House to a political outsider.
also raised the issue of whether Facebook users should know how their Facebook
profiles are being used.
A further issue
was whether Facebook knew, or as a data custodian should have taken active
steps to check, what Cambridge Analytica was up to.
What have we leant?
medical research and other ‘research’ conducted by public institutions through
use of public funds involving humans and animals must be subject to review and
oversight of a research ethics committee.
development and commercialisation of most products and services outside formal
and services do not involve collection, use or disclosure of personal
information about identifiable individuals and are therefore outside data
protection laws. If information is being used but it is not about
identifiable individuals, the use of that information may not be subject to any
form of privacy impact assessment. Although privacy review does not formally
include consideration of non-privacy ethical concerns, often these are picked
up when uses of personal information are reviewed. But no collection, use or
disclosure of personal information, no privacy review.
This leaves a
sizeable middle ground.
and use of personal information about Facebook users by Cambridge Analytica
probably was not in that middle ground, because those activities took place
without knowledge and active consent of Facebook users. However, it was suggested
that given narrow privacy coverage of privacy-related laws in the USA,
knowledge and active consent was not required.
In any event, it
was argued that there was no requirement for ethical or privacy review of what
Cambridge Analytica was up to – that this application was in the middle ground.
But within this middle ground – and then outside current requirements for
review – lie many applications of algorithmic decision making, and uses of AI
based products and services, both in the business sector and in government.
Concerns in this
middle ground include social equity and fairness, discrimination, lack of
transparency, lack of accountability, intrusive surveillance and failure to
properly warn or disclose biases or other limitations in reliability of outputs
will rapidly escalate in scale, complexity and variety as the range of
applications of machine learning and AI continue to expand.
So how should we
address these problems without sacrificing many of the benefits of machine
learning and AI?
Making it real
Most studies of
AI ethics rework lists of principles for ethical analysis, but do not assist
operationalisation of those principles.
requires methodologies, tools, processes and lexicons that prompt sensible
discussions within laboratories and other workplaces about social equity,
fairness and ethics.
development teams need to be empowered to have these discussions. They may need
to bring outside advocates into those discussions, or to try to synthesise
viewpoints of a broader cross-section of society.
discussions need to be sufficiently structured and formalised to reliably and
The tools used
to inform and guide these discussions should not be overly intrusive and
formulaic, or review will become be a matter of box ticking, form over
must be agile and timely enough to not slow down development and
There may also
be business benefits in pausing to frame and consider ethical questions.
didn’t learn enough from the adverse comment following the emotional contagion
study, will Facebook learn more from the far greater business impact of
Cambridge Analytica upon Facebook’s market capitalisation and through loss of
trust of Facebook users?
and government agencies endeavouring to be socially responsible should no
longer require their own #MeToo moment to spur uptake of ethical assessment of
design and development decisions. Sensible ethical framing can get buy-in by
executives and other decision-makers by demonstrably yielding value by reducing
subsequent rework when problems are later discovered.
How much has it
cost Facebook to deal with the problems exposed through the Cambridge Analytica
products and services get beta released into markets without first considering
social impact and user issues, and then require costly rework to address issues
first identified in-market?
prospective customers are never gained because accessibility issues have not
How many machine
learning and AI applications will not achieve acceptance because inadequate
transparency is engineered into those applications and they are not accepted
because humans can’t properly ‘interrogate the algorithm’ to understand biases
and other reliability issues?
trust machines that fundamentally affect their lives and security when it is
not clear which provider takes responsibility for which aspects of a system and
whether issues of over-reliance on not fully reliable products are not properly
What have we learned?
It may be that
Cambridge Analytica teaches us nothing new.
But it is
reasonable to hope that this controversy highlights the ‘gap’ between data
privacy and the ethical review of research involving humans and animals, and to
fill that gap by taking the best parts of privacy assessment and ethical
We need to
quickly move from abstract statements of high ethical principles.
We need to
empower diverse humans in research and development teams to fill that gap by
delivering to them sound methodologies, tools and lexicons for ethical decision
are now mature in building privacy by design and information security by design
into their research and development.
businesses or government agencies apply social fairness, social responsibility
or transparency by design and by default into planning of products and
Ethics by design
and default is too important to not do well.
Let’s get it
Peter Leonard is
a business lawyer and economist and principal of Data Synergies, a consultancy
to data driven business and government agencies.
Toby Walsh is
Scientia Professor of Artificial Intelligence at the UNSW Sydney.
Peter and Toby
are members of the Australian Computer Society’s AI and Ethics Technical
Committee which is endeavouring to do (quickly and well) what this article says
needs to be done.