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the role of meaningful human reviews

In the first
detailed element of our AI framework blog series, Reuben Binns, our Research
Fellow in AI, and Valeria Gallo, Technology Policy Adviser, explore how
organisations can ensure ‘meaningful’ human involvement to make sure AI
decisions are not classified as solely automated by mistake.

This blog forms part of our ongoing work on developing a
framework for auditing AI. We are keen to hear your views in the comments below
or you can email us.


Artificial
Intelligence (AI) systems[1] often process personal data to either support or make a decision. For example,
AI could be used to approve or reject a financial loan automatically, or
support recruitment teams to identify interview candidates by ranking job
applications.


Article 22 of
the General Data Protection Regulation (GDPR) establishes very strict
conditions in relation to AI systems that make solely automated decisions, ie
without human input, with legal or similarly significant effects about
individuals. AI systems that only support or enhance human decision-making are
not subject to these conditions. However, a decision will not fall outside the
scope of Article 22 just because a human has ‘rubber-stamped’ it: human input
needs to be ‘meaningful’.
The degree
and quality of human review and intervention before a final decision is made
about an individual is the key factor in determining whether an AI system is
solely or non-solely automated.
Board
members, data scientists, business owners, and oversight functions, among
others, will be expected to play an active role in ensuring that AI
applications are designed, built, and used as intended.
The meaningfulness
of human review in non-solely automated AI applications and the management of
the risks associated with it are key areas of focus for our proposed AI
Auditing Framework and what we will be exploring further in this blog.


What’s already been said?

  • Human reviewers must be involved in checking
    the system’s recommendation and should not “routinely” apply the automated recommendation to an individual;
  • reviewers’ involvement must be active
    and not just a token gesture. They should have actual “meaningful” influence on
    the decision, including the “authority and competence” to go against the
    recommendation; and
  • reviewers must ‘weigh-up’ and
    ‘interpret’ the recommendation, consider all available input data,
    and also take into account other additional factors’.
The
meaningfulness of human input must be considered in any automated
decision-making systems however basic (e.g. simple decision trees). In more
complex AI systems however, we think there are two additional factors that
could potentially cause a system to be considered solely-automated. They are:
2.   Lack
of
interpretability

What do we mean by automation bias?

AI
models are based on mathematics and data, and because of this people tend to
think of them as objective and trust their output.

The
terms automation bias or automation-induced complacency describe how human users
routinely rely on the output generated by a computer decision-support system
and stop using their own judgement, or stop questioning whether the output
might be wrong. 
If this happens when
using an AI system, then there is a risk that the system may unintentionally be
classed as solely automated under the law.

What do we mean by lack of interpretability?

Some
types of AI systems, for example those using deep learning, may be difficult
for a human reviewer to interpret.

If
the inputs and outputs of AI systems are not easily interpretable, and other
explanation tools are not available or reliable, there is a risk a human will
not be able to meaningfully review the output of an AI system.

If
meaningful reviews are not possible, the reviewer may start to just agree with
the system’s recommendations without judgement or challenge,
 this
would mean the decision was ‘solely automated’.

Organisations
should take a clear view on the intended use of any AI application from the
beginning. They should specify and document clearly whether AI will be used to
enhance human decision-making or to make solely automated decisions.
The management
body should review and sign-off the intended use of any AI system, making sure
that it is in line with the organisation’s risk appetite. This means board
members need to have a solid understanding of the key risk implications associated
with each option, and be ready and equipped to provide an appropriate degree of
challenge.
The
management body is also responsible to ensure clear lines of accountability and
effective risk management policies are in place from the outset. If AI systems
are only intended to support human decisions, then such policies should specifically
address additional risk factors such as automation bias and lack of interpretability.
It is
possible organisations may not know in advance whether a partly or fully
automated AI application would meet their needs best. In such cases, their risk
management policies and Data Protection Impact Assessments (DPIAs) should
reflect this distinctly, and include the risk and controls for each option
throughout the AI system’s lifecycle.

Automation bias

You may think
automation bias can be addressed chiefly by improving the effectiveness of the
training and monitoring of human reviewers. Training is a key component of effective
AI risk management but controls to mitigate automation bias should be in place
from the start.
During the
design and build phase business owners, data scientists and oversight functions
should work together to develop design requirements that support a meaningful
human review from the outset.
They must think
about what features they would expect the AI system to consider and which additional
factors the human reviewers should  take
into account before finalising their decision. For instance, the AI system
could consider measurable properties like how many years’ experience a job
applicant has, while a human reviewer assesses the skills of applicants which
cannot be captured in application forms.
If human
reviewers can only access or use the same data used by the AI system, then
arguably they are not taking into account other additional factors. This means
that their review may not be sufficiently meaningful and the decision may end
up being considered solely automated under GDPR.
If needed, organisations
have to think about how to capture additional factors. For example, getting the
human reviewers to interact directly with the person the decision is about to
gather such information.
Those in
charge of designing the front-end interface of an AI system must understand the
needs, thought process, and behaviours of human reviewers and enable them to
effectively intervene.
It
may therefore be helpful to consult and test options with human reviewers early
on.
However, the
features the AI systems will use will also depend on the data available, the
type of model(s) selected, and other system building choices. Any assumptions
made in the design phase will need to be tested and confirmed once the AI
system has been fully trained and built.
Interpretability
should also be considered from the design phase.

Interpretability
is challenging to define in absolute terms and can be measured in different
ways. For example:

  •  Can
    the human reviewer predict how the system’s outputs will change if given
    different inputs?
  •  Can
    the human identify the most important inputs contributing to a particular
    output?
  • Can
    the human identify when the output might be wrong?
This is why
it is important for organisations to define and document what interpretability
means, and how to measure it, in the specific context of each AI system they wish
to use.
Some AI systems
are more interpretable than others. For instance, models that use a small
number of human-interpretable features (e.g. age and weight), are likely to be easier
to interpret than models that use a large number of features, or involve heavy ‘pre-processing’2.
The relationship
between the input features and the model’s output can also be simple or
complicated. Simple “if-then” rules, which can describe decision trees, will be
easier to interpret. Similarly, linear relationships (where the value of the
output increases proportional to the input) may be easier to interpret than
relationships that are non-monotonic (where the output value is not proportional
to the input) or non-linear (where the output value may increase or decrease as
the input increases).
One approach
to address low interpretability is the use of Local Interpretable
Model-agnostic Explanations (LIMEs), which provide an explanation of the output
after it has been generated. LIMEs use a simpler surrogate model to summarise
the relationships between input and output pairs that are similar to those in
the system you are trying to interpret. In addition to summaries of individual
predictions, LIMEs can sometimes help detect errors (e.g. to see what part of
an image classifier has mistakenly been classified as a certain object). However,
they do not represent the actual logic underlying the AI system and can be
misleading if misused.
Many statistical
models can also be designed to provide a confidence score alongside each output,
which could help a human reviewer in their own decision-making. A lower
confidence score would indicate that the human reviewer needs to have more
input into the final decision.
Assessing the
interpretability requirements should be part of the design phase, allowing
explanation tools to be developed as part of the system if required.
Organisations
should try to maximise the interpretability of AI systems, but as we will
explore in future blogs there will often be difficult trade-offs to make (eg interpretability
vs. accuracy).

This is why
risk management policies should establish a robust, risk-based, and independent
approval process for each AI system. They should also set out clearly who is
responsible for the testing and final validation of the system before it is
deployed. Those individuals should be accountable for any negative impact on
interpretability and the effectiveness of human reviews and only provide sign-off
if AI systems are in line with the adopted risk management policy.

Training

Training is
pivotal ensuring an AI system is considered non-solely automated.

As a starting
point, human reviewers should be trained:
 

  • to
    understand how an AI system works and its limitations;
    to
    anticipate when the system may be misleading or wrong and why;
     
  •  to
    have a healthy level of scepticism in the AI system’s output and given a sense
    of how often the system could be  wrong;
      
  • to
    understand how their own expertise is meant to compliment the system, and be provided
    with a list of factors to take into account; 
  • and to
    provide meaningful explanations for either rejecting or accepting the AI
    system’s output – a decision they should be responsible for. A clear escalation
    policy should also be in place.
     
In order for the training to be
effective, it is important that human reviewers have the authority to override
the output generated by the AI system and they and are confident that they will
not be penalised for so doing. This authority and confidence cannot be created
by policies and training alone: a supportive organisational culture is also
crucial.
We have
focussed here on the training of human reviewers, however it is worth noting
that organisations should also consider whether any other function, eg risk or
internal audit, require additional training to provide effective oversight.
The analysis
of why, and how many times, a human reviewer accepted or rejected the AI
system’s output will be a key part in an effective risk monitoring system.
If risk
monitoring reports flag that human reviewers are routinely agreeing with the AI
system’s outputs, and cannot demonstrate they have genuinely assessed them, then
their decisions may effectively be classed as solely automated under GDPR.
Organisations
need to have controls in place to keep risk within target levels, including, if
necessary, stopping the processing of personal data by the AI system, either
temporarily or permanently.
We are keen
to hear your thoughts on this topic and welcome any feedback on our current
thinking. In particular, we would appreciate your views on the following two
questions:
1)  
What
other technical and organisational controls do you think organisations should
put in place to reduce the risk of AI systems falling within the scope of GDPR
Article 22 by mistake?
2)  
Are
there any additional risk factors, in addition to interpretability and
automation bias, which we should address in this part of our AI Auditing
Framework?

Dr Reuben Binns, a researcher working on AI and data protection, joined the ICO on a fixed term fellowship in December 2018. During his two-year term, Dr Binns will research and investigate a framework for auditing algorithms and conduct further in-depth research activities in AI and machine learning.

Valeria Gallo is currently seconded to the ICO as a Technology Policy Adviser. She works with Reuben Binns, our Artificial Intelligence (AI) Research Fellow, on the development of the ICO Auditing Framework for AI. Prior to her secondment, Valeria was responsible for analysing and developing thought leadership on the impact of technological innovation on regulation and supervision of financial services firms.

Footnotes

‘AI
system’ refers to Artificial Intelligence software which generates ‘outputs’ or
‘recommendations’ relating to a decision, for instance, whether or not to grant
a customer a loan or invite an applicant to an interview (elsewhere, these may
be referred to as ‘decision support systems’). Such recommendations will often
be based on the outputs of a ‘machine learning model’ trained on data to
generate predictions or classifications.
[2] Pre-processing is a practice in machine learning that
involves modifying the training data so that it is more useful and effective in
the learning process.

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