At a glance
This section explains the lawfulness, fairness, and transparency principles. Compliance with these principles may be challenging in an AI context.
Who is this section for?
This section is aimed at compliance-focused roles, including senior management, who are responsible for ensuring the processing using AI is lawful, fair, and transparent. There are several techniques described that would require the input of a technical specialist.
How do the principles of lawfulness, fairness and transparency apply to AI?
Firstly, the development and deployment of AI systems involve processing personal data in different ways for different purposes. You must break down and separate each distinct processing operation, and identify the purpose and an appropriate lawful basis for each one, in order to comply with the principle of lawfulness.
Second, if you use an AI system to infer data about people, in order for this processing to be fair, you need to ensure that:
- the system is sufficiently statistically accurate and avoids discrimination; and
- you consider the impact of individuals’ reasonable expectations.
For example, an AI system used to predict loan repayment rates is likely to breach the fairness principle if it:
- makes predictions which frequently turn out to be incorrect;
- leads to disparities in outcomes between groups (eg between men and women) which could not be justified as a proportionate means of achieving a legitimate aim; or
- uses personal data in ways which individuals would not reasonably expect.
Thirdly, you need to be transparent about how you process personal data in an AI system, to comply with the principle of transparency. The core issues regarding AI and the transparency principle are addressed in ‘Explaining decisions made with AI’ guidance, so are not discussed in detail here.
How do we identify our purposes and lawful basis when using AI?
What should we consider when deciding lawful bases?
Whenever you are processing personal data – whether to train a new AI system, or make predictions using an existing one – you must have an appropriate lawful basis to do so.
Different lawful bases may apply depending on your particular circumstances. However, some lawful bases may be more likely to be appropriate for the training and / or deployment of AI than others. In some cases, more than one lawful basis may be appropriate.
At the same time, you must remember that:
- it is your responsibility to decide which lawful basis applies to your processing;
- you must always choose the lawful basis that most closely reflects the true nature of your relationship with the individual and the purpose of the processing;
- you should make this determination before you start your processing;
- you should document your decision;
- you cannot swap lawful bases at a later date without good reason;
- you must include your lawful basis in your privacy notice (along with the purposes); and
- if you are processing special categories of data you need both a lawful basis and an additional condition for processing.
How should we distinguish purposes between AI development and deployment?
In many cases, when determining your purpose(s) and lawful basis, it will make sense for you to separate the research and development phase (including conceptualisation, design, training and model selection) of AI systems from the deployment phase. This is because these are distinct and separate purposes, with different circumstances and risks.
Therefore, it may sometimes be more appropriate to choose different lawful bases for your AI development and deployment. For example, you need to do this where:
- the AI system was developed for a general-purpose task, and you then deploy it in different contexts for different purposes. For example, a facial recognition system could be trained to recognise faces, but that functionality could be used for multiple purposes, such as preventing crime, authentication, and tagging friends in a social network. Each of these further applications might require a different lawful basis;
- in cases where you implement an AI system from a third party, any processing of personal data undertaken by the developer will have been for a different purpose (eg to develop the system) to what you intend to use the system for, therefore you may need to identify a different lawful basis; and
- processing of personal data for the purposes of training a model may not directly affect the individuals, but once the model is deployed, it may automatically make decisions which have legal or significant effects. This means the provisions on automated decision-making apply; as a result, a different range of available lawful bases may apply at the development and deployment stages.
The following sections outline some AI-related considerations for each of the UK GDPR’s lawful bases. They do not consider Part 3 of the DPA (law enforcement processing) at this stage.
Can we rely on consent?
Consent may be an appropriate lawful basis in cases where you have a direct relationship with the individuals whose data you want to process.
However, you must ensure that consent is freely given, specific, informed and unambiguous, and involves a clear affirmative act on the part of the individuals.
The advantage of consent is that it can lead to more trust and buy-in from individuals when they are using your service. Providing individuals with control can also be a factor in your DPIAs.
However, for consent to apply, individuals must have a genuine choice about whether you can use their data. This may have implications depending on what you intend to do with the data – it can be difficult to ensure you collect valid consent for more complicated processing operations, such as those involved in AI. For example, the more things you want to do with the data, the more difficult it is to ensure that consent is genuinely specific and informed.
The key is that individuals understand how you are using their personal data and have consented to this use. For example, if you want to collect a wide range of features to explore different models to predict a variety of outcomes, consent may be an appropriate lawful basis, provided that you inform individuals about these activities and obtain valid consent.
Consent may also be an appropriate lawful basis for the use of an individual’s data during deployment of an AI system (eg for purposes such as personalising the service or making a prediction or recommendation).
However, you should be aware that for consent to be valid, individuals must also be able to withdraw consent as easily as they gave it. If you are relying on consent as the basis of processing data with an AI system during deployment (eg to drive personalised content), you should be ready to accommodate the withdrawal of consent for this processing.
What is the impact of Article 22 of the UK GDPR?
Data protection law applies to all automated individual decision-making and profiling. Article 22 of the UK GDPR has additional rules to protect individuals if you are carrying out solely automated decision-making that has legal or similarly significant effects on them.
This may apply in the AI context, eg where you are using an AI system to make these kinds of decisions.
However, you can only carry out this type of decision-making where the decision is:
- necessary for the entry into or performance of a contract;
- authorised by law that applies to you; or
- based on the individual’s explicit consent.
You therefore have to identify if your processing falls under Article 22 and, where it does, make sure that you:
- give individuals information about the processing;
- introduce simple ways for them to request human intervention or challenge a decision; and
- carry out regular checks to make sure your systems are working as intended.
What do we need to do about statistical accuracy?
Statistical accuracy refers to the proportion of answers that an AI system gets correct or incorrect.
This section explains the controls you can implement so that your AI systems are sufficiently statistically accurate to ensure that the processing of personal data complies with the fairness principle.
What is the difference between ‘accuracy’ in data protection law and ‘statistical accuracy’ in AI?
It is important to note that the word ‘accuracy’ has a different meaning in the contexts of data protection and AI. Accuracy in data protection is one of the fundamental principles, requiring you to ensure that personal data is accurate and, where necessary, kept up to date. It requires you to take all reasonable steps to make sure the personal data you process is not ‘incorrect or misleading as to any matter of fact’ and, where necessary, is corrected or deleted without undue delay.
Broadly, accuracy in AI (and, more generally, in statistical modelling) refers to how often an AI system guesses the correct answer, measured against correctly labelled test data. The test data is usually separated from the training data prior to training, or drawn from a different source (or both). In many contexts, the answers the AI system provides will be personal data. For example, an AI system might infer someone’s demographic information or their interests from their behaviour on a social network.
So, for clarity, in this guidance, we use the terms:
- ‘accuracy’ to refer to the accuracy principle of data protection law; and
- ‘statistical accuracy’ to refer to the accuracy of an AI system itself.
Fairness, in a data protection context, generally means that you should handle personal data in ways that people would reasonably expect and not use it in ways that have unjustified adverse effects on them. Improving the ‘statistical accuracy’ of your AI system’s outputs is one of your considerations to ensure compliance with the fairness principle.
Data protection’s accuracy principle applies to all personal data, whether it is information about an individual used as an input to an AI system, or an output of the system. However, this does not mean that an AI system needs to be 100% statistically accurate to comply with the accuracy principle.
In many cases, the outputs of an AI system are not intended to be treated as factual information about the individual. Instead, they are intended to represent a statistically informed guess as to something which may be true about the individual now or in the future. To avoid such personal data being misinterpreted as factual, you should ensure that your records indicate that they are statistically informed guesses rather than facts. Your records should also include information about the provenance of the data and the AI system used to generate the inference.
You should also record if it becomes clear that the inference was based on inaccurate data, or the AI system used to generate it is statistically flawed in a way which may have affected the quality of the inference.
Similarly, if the processing of the incorrect inference may have an impact on them, an individual may request the inclusion of additional information in their record countering the incorrect inference. This helps ensure that any decisions taken on the basis of the potentially incorrect inference are informed by any evidence that it may be wrong.
The UK GDPR mentions statistical accuracy in the context of profiling and automated decision-making at Recital 71. This states organisations should put in place ‘appropriate mathematical and statistical procedures’ for the profiling of individuals as part of their technical measures. You should ensure any factors that may result in inaccuracies in personal data are corrected and the risk of errors is minimised.
If you use an AI system to make inferences about people, you need to ensure that the system is sufficiently statistically accurate for your purposes. This does not mean that every inference has to be correct, but you do need to factor in the possibility of them being incorrect and the impact this may have on any decisions that you may take on the basis of them. Failure to do this could mean that your processing is not compliant with the fairness principle. It may also impact on your compliance with the data minimisation principle, as personal data, which includes inferences, must be adequate and relevant for your purpose.
Your AI system therefore needs to be sufficiently statistically accurate to ensure that any personal data generated by it is processed lawfully and fairly.
However, overall statistical accuracy is not a particularly useful measure, and usually needs to be broken down into different measures. It is important to measure and prioritise the right ones. If you are in a compliance role and are unsure what these terms mean, you should consult colleagues in the relevant technical roles.
Further reading – European Data Protection Board
The European Data Protection Board (EDPB), which has replaced the Article 29 Working Party (WP29), includes representatives from the data protection authorities of each EU member state. It adopts guidelines for complying with the requirements of the EU version of the GDPR.
The EDPB has adopted guidelines on automated decision-making and profiling.
EDPB guidelines are no longer directly relevant to the UK regime and are not binding under the UK regime. However, they may still provide helpful guidance on certain issues.
How should we define and prioritise different statistical accuracy measures?
Statistical accuracy, as a general measure, is about how closely an AI system’s predictions match the correct labels as defined in the test data.
For example, if an AI system is used to classify emails as spam or not spam, a simple measure of statistical accuracy is the number of emails that were correctly classified as spam or not spam, as a proportion of all the emails that were analysed.
However, such a measure could be misleading. For example, if 90% of all emails received to an inbox are spam, then you could create a 90% accurate classifier by simply labelling everything as spam. But this would defeat the purpose of the classifier, as no genuine email would get through.
For this reason, you should use alternative measures of statistical accuracy to assess how good a system is. If you are in a compliance role, you should work with colleagues in technical roles to ensure that you have in place appropriate measures of statistical accuracy given your context and the purposes of processing.
These measures should reflect the balance between two different kinds of errors:
- a false positive or ‘type I’ error: these are cases that the AI system incorrectly labels as positive (eg emails classified as spam, when they are genuine); or
- a false negative or ‘type II’ error: these are cases that the AI system incorrectly labels as negative when they are actually positive (eg emails classified as genuine, when they are actually spam).
It is important to strike the right balance between these two types of errors. There are more useful measures which reflect these two types of errors, including:
- precision: the percentage of cases identified as positive that are in fact positive (also called ‘positive predictive value’). For example, if nine out of 10 emails that are classified as spam are actually spam, the precision of the AI system is 90%; or
- recall (or sensitivity): the percentage of all cases that are in fact positive that are identified as such. For example, if 10 out of 100 emails are actually spam, but the AI system only identifies seven of them, then its recall is 70%.
There are trade-offs between precision and recall, which can be assessed using statistical measures. If you place more importance on finding as many of the positive cases as possible (maximising recall), this may come at the cost of some false positives (lowering precision).
In addition, there may be important differences between the consequences of false positives and false negatives on individuals, which could affect the fairness of the processing.
If a CV filtering system being used to assist with selecting qualified candidates for an interview produces a false positive, then an unqualified candidate may be invited to interview, wasting the employer’s and the applicant’s time unnecessarily.
If it produces a false negative, a qualified candidate will miss an employment opportunity and the organisation will miss a good candidate.
You should prioritise avoiding certain kinds of error based on the severity and nature of the risks.
In general, statistical accuracy as a measure depends on how possible it is to compare the performance of a system’s outputs to some ‘ground truth’ (ie checking the results of the AI system against the real world). For example, a medical diagnostic tool designed to detect malignant tumours could be evaluated against high quality test data, containing known patient outcomes.
In some other areas, a ground truth may be unattainable. This could be because no high-quality test data exists or because what you are trying to predict or classify is subjective (eg whether a social media post is offensive). There is a risk that statistical accuracy is misconstrued in these situations, so that AI systems are seen as being highly statistically accurate even though they are reflecting the average of what a set of human labellers thought, rather than objective truth.
To avoid this, your records should indicate where AI outputs are not intended to reflect objective facts, and any decisions taken on the basis of such personal data should reflect these limitations. This is also an example of where you must take into account the accuracy principle – for more information, see our guidance on the accuracy principle, which refers to accuracy of opinions.
Finally, statistical accuracy is not a static measure. While it is usually measured on static test data (held back from the training data), in real life situations AI systems are applied to new and changing populations. Just because a system is statistically accurate about an existing population’s data (eg customers in the last year), it may not continue to perform well if there is a change in the characteristics of that population or any other population who the system is applied to in future. Behaviours may change, either of their own accord, or because they are adapting in response to the system, and the AI system may become less statistically accurate with time.
This phenomenon is referred to in machine learning as ‘concept / model drift’, and various methods exist for detecting it. For example, you can measure the distance between classification errors over time; increasingly frequent errors may suggest drift.
You should regularly assess drift and retrain the model on new data where necessary. As part of your accountability responsibilities, you should decide and document appropriate thresholds for determining whether your model needs to be retrained, based on the nature, scope, context and purposes of the processing and the risks it poses. For example, if your model is scoring CVs as part of a recruitment exercise, and the kinds of skills candidates need in a particular job are likely to change every two years, you should anticipate assessing the need to re-train your fresh data at least that often.
In other application domains where the main features don’t change so often (eg recognising handwritten digits), you can anticipate less drift. You will need to assess this based on your own circumstances.
What should we do?
You should always think carefully from the start whether it is appropriate to automate any prediction or decision-making process. This should include assessing the effectiveness of the AI system in making statistically accurate predictions about the individuals whose personal data it processes.
You should assess the merits of using a particular AI system in light of consideration of its effectiveness in making statistically accurate, and therefore valuable, predictions. Not all AI systems demonstrate a sufficient level of statistical accuracy to justify their use.
If you decide to adopt an AI system, then to comply with the data protection principles, you should:
- ensure that all functions and individuals responsible for its development, testing, validation, deployment, and monitoring are adequately trained to understand the associated statistical accuracy requirements and measures;
- make sure data is clearly labelled as inferences and predictions, and is not claimed to be factual;
- ensure you have managed trade-offs and reasonable expectations; and
- adopt a common terminology that staff can use to discuss statistical accuracy performance measures, including their limitations and any adverse impact on individuals.
What else should we do?
As part of your obligation to implement data protection by design and by default, you should consider statistical accuracy and the appropriate measures to evaluate it from the design phase and test these measures throughout the AI lifecycle. After deployment, you should implement monitoring, the frequency of which should be proportional to the impact an incorrect output may have on individuals. The higher the impact the more frequently you should monitor and report on it. You should also review your statistical accuracy measures regularly to mitigate the risk of concept drift. Your change policy procedures should take this into account from the outset.
Statistical accuracy is also an important consideration if you outsource the development of an AI system to a third party (either fully or partially) or purchase an AI solution from an external vendor. In these cases, you should examine and test any claims made by third parties as part of the procurement process.
Similarly, you should agree regular updates and reviews of statistical accuracy to guard against changing population data and concept/ model drift. If you are a provider of AI services, you should ensure that they are designed in such a way as to allow organisations to fulfil their data protection obligations.
Finally, the vast quantity of personal data you may hold and process as part of your AI systems is likely to put pressure on any pre-existing non-AI processes you use to identify and, if necessary, rectify/ delete inaccurate personal data, whether it is used as input or training/ test data. Therefore, you need to review your data governance practices and systems to ensure they remain fit for purpose.
How should we address risks of bias and discrimination?
As AI systems learn from data which may be unbalanced and/or reflect discrimination, they may produce outputs which have discriminatory effects on people based on their gender, race, age, health, religion, disability, sexual orientation or other characteristics.
The fact that AI systems learn from data does not guarantee that their outputs will not lead to discriminatory effects. The data used to train and test AI systems, as well as the way they are designed, and used, might lead to AI systems which treat certain groups less favourably without objective justification.
The following sections give guidance on interpreting the discrimination-related requirements of data protection law in the context of AI, as well as making some suggestions about best practice.
The following sections do not aim to provide guidance on legal compliance with the UK’s anti-discrimination legal framework, notably the UK Equality Act 2010. This sits alongside data protection law and applies to a range of organisations. It gives individuals protection from direct and indirect discrimination, whether generated by a human or an automated decision-making system (or some combination of the two).
Demonstrating that an AI system is not unlawfully discriminatory under the EA2010 is a complex task, but it is separate and additional to your obligations relating to discrimination under data protection law. Compliance with one will not guarantee compliance with the other.
Data protection law addresses concerns about unjust discrimination in several ways.
First, processing of personal data must be ‘fair’. Fairness means you should handle personal data in ways people reasonably expect and not use it in ways that have unjustified adverse effects on them. Any processing of personal data using AI that leads to unjust discrimination between people, will violate the fairness principle.
Second, data protection aims to protect individuals’ rights and freedoms– with regard to the processing of their personal data. This includes the right to privacy but also the right to non-discrimination. Specifically, the requirements of data protection by design and by default mean you have to implement appropriate technical and organisational measures to take into account the risks to the rights and freedoms of data subjects and implement the data protection principles effectively. Similarly, a data protection impact assessment should contain measures to address and mitigate those risks, which include the risk of discrimination.
Third, the UK GDPR specifically notes that processing personal data for profiling and automated decision-making may give rise to discrimination, and that you should use appropriate technical and organisational measures to prevent this.
Why might an AI system lead to discrimination?
Before addressing what data protection law requires you to do about the risk of AI and discrimination, and suggesting best practices for compliance, it is helpful to understand how these risks might arise. The following content contains some technical details, so understanding how it may apply to your organisation may require attention of staff in both compliance and technical roles.
A bank develops an AI system to calculate the credit risk of potential customers. The bank will use the AI system to approve or reject loan applications.
The system is trained on a large dataset containing a range of information about previous borrowers, such as their occupation, income, age, and whether or not they repaid their loan.
During testing, the bank wants to check against any possible gender bias and finds the AI system tends to give women lower credit scores.
In this case, the AI system puts members of a certain group (women) at a disadvantage, and so would appear to be discriminatory. Note that this may not constitute unlawful discrimination under equalities law, if the deployment of the AI system can be shown to be a proportionate means of achieving a legitimate aim.
There are many different reasons why the system may be giving women lower credit scores.
One is imbalanced training data. The proportion of different genders in the training data may not be balanced. For example, the training data may include a greater proportion of male borrowers because in the past fewer women applied for loans and therefore the bank doesn’t have enough data about women.
Machine learning algorithms used to create an AI system are designed to be the best fit for the data it is trained and tested on. If the men are over-represented in the training data, the model will pay more attention to the statistical relationships that predict repayment rates for men, and less to statistical patterns that predict repayment rates for women, which might be different.
Put another way, because they are statistically ‘less important’, the model may systematically predict lower loan repayment rates for women, even if women in the training dataset were on average more likely to repay their loans than men.
These issues will apply to any population under-represented in the training data. For example, if a facial recognition model is trained on a disproportionate number of faces belonging to a particular ethnicity and gender (eg white men), it will perform better when recognising individuals in that group and worse on others.
Another reason is that the training data may reflect past discrimination. For example, if in the past, loan applications from women were rejected more frequently than those from men due to prejudice, then any model based on such training data is likely to reproduce the same pattern of discrimination.
Certain domains where discrimination has historically been a significant problem are more likely to experience this problem more acutely, such as police stop-and-search of young black men, or recruitment for traditionally male roles.
These issues can occur even if the training data does not contain any protected characteristics like gender or race. A variety of features in the training data are often closely correlated with protected characteristics, eg occupation. These ‘proxy variables’ enable the model to reproduce patterns of discrimination associated with those characteristics, even if its designers did not intend this.
These problems can occur in any statistical model, so the following considerations may apply to you even if you don’t consider your statistical models to be ‘AI’. However, they are more likely to occur in AI systems because they can include a greater number of features and may identify complex combinations of features which are proxies for protected characteristics. Many modern ML methods are more powerful than traditional statistical approaches because they are better at uncovering non-linear patterns in high dimensional data. However, these may also include patterns that reflect discrimination.
Other causes of potentially discriminatory AI systems include:
- prejudices or bias in the way variables are measured, labelled or aggregated;
- biased cultural assumptions of developers;
- inappropriately defined objectives (eg where the ‘best candidate’ for a job embeds assumptions about gender, race or other characteristics); or
- the way the model is deployed (eg via a user interface which doesn’t meet accessibility requirements).
What are the technical approaches to mitigate discrimination risk in ML models?
While discrimination is a broader problem that cannot realistically be ‘fixed’ through technology, various approaches exist which aim to mitigate AI-driven discrimination.
Computer scientists and others have been developing different mathematical techniques to measure how ML models treat individuals from different groups in potentially discriminatory ways and reduce them. This field is often referred to as algorithmic ‘fairness’.
The techniques it proposes do not necessarily align with relevant non-discrimination law in the UK, and in some cases may contradict it, so should not be relied upon as a means of complying with such obligations. However, depending on your context, some of these approaches may be appropriate technical measures to ensure personal data processing is fair and to minimise the risks of discrimination arising from it.
In cases of imbalanced training data, it may be possible to balance it out by adding or removing data about under/ overrepresented subsets of the population (eg adding more data points on loan applications from women).
In cases where the training data reflects past discrimination, you could either modify the data, change the learning process, or modify the model after training.
In order to measure whether these techniques are effective, there are various mathematical ‘fairness’ measures against which you can measure the results.
Simply removing any protected characteristics from the inputs the model uses to make a prediction is unlikely to be enough, as there are often variables which are proxies for the protected characteristics. Other measures involve comparing how the AI system distributes positive or negative outcomes (or errors) between protected groups. Some of these measures conflict with each other, meaning you cannot satisfy all of them at the same time. Which of these measures are most appropriate, and in what combinations, if any, will depend on your context, as well as any applicable relevant laws (eg equality law).
You should also consider the impact of these techniques on the statistical accuracy of the AI system’s performance. For example, to reduce the potential for discrimination, you might modify a credit risk model so that the proportion of positive predictions between people with different protected characteristics (eg men and women) are equalised. This may help prevent discriminatory outcomes, but it could also result in a higher number of statistical errors overall which you will also need to manage as well.
In practice, there may not always be a tension between statistical accuracy and avoiding discrimination. For example, if discriminatory outcomes in the model are driven by a relative lack of data about a statistically small minority of the population, then statistical accuracy of the model could be increased by collecting more data about them, whilst also equalising the proportions of correct predictions.
However, in that case, you would face a different choice between:
- collecting more data on the minority population in the interests of reducing the disproportionate number of statistical errors they face; or
- not collecting such data due to the risks doing so may pose to the other rights and freedoms of those individuals.
Depending on your context, you may also have other sector-specific regulatory obligations regarding statistical accuracy or discrimination which you will need to be consider alongside your data protection obligations. If you need to process data in a certain way to meet those obligations, data protection does not prevent you from doing so.
Can we process special category data to assess and address discrimination in AI systems?
In order to assess and address the potential for discrimination in an AI system, you may need a dataset containing example individuals with labels for the protected characteristics of interest, such as those outlined in the Equality Act 2010. You could then use this dataset to test how the system would perform with each protected group, and also potentially to re-train the model to avoid discriminatory effects.
Before doing this kind of analysis, you need to ensure you have an appropriate lawful basis to process the data for such purposes. There are different data protection considerations depending on the kinds of discrimination you are testing for. If you are testing a system for discriminatory impact by age or sex/ gender, there are no special data protection conditions for processing these protected characteristics, because they are not classified as ‘special category data’ in data protection law. You still need to consider:
- the broader questions of lawfulness, fairness and the risks the processing poses as a whole; and
- the possibility for the data to either be special category data anyway, or becoming so during the processing (ie if the processing involves analysing or inferring any data to do with health or genetic status).
You should also note that when you are dealing with personal data that results from specific technical processing about the physical, physiological or behavioural characteristics of an individual, and allows or confirms that individual’s unique identification, that data is biometric data.
Where you use biometric data for the purpose of uniquely identifying an individual, it is also special category data.
So, if you use biometric data for testing and mitigating discrimination in your AI system, but not for the purpose of confirming the identity of the individuals within the dataset or making any kind of decision in relation to them, the biometric data does not come under Article 9. The data is still regarded as biometric data under the UK GDPR, but is not special category data.
Similarly, if the personal data does not allow or confirm an individual’s unique identification, then it is not biometric data (or special category data).
However, some of the protected characteristics outlined in the Equality Act are classified as special category data. These include race, religion or belief, and sexual orientation. They may also include disability, pregnancy, and gender reassignment in so far as they may reveal information about a person’s health. Similarly, because civil partnerships were until recently only available to same-sex couples, data that indicates someone is in a civil partnership may indirectly reveal their sexual orientation.
If you are testing an AI system for discriminatory impact on the basis of these characteristics, you are likely to need to process special category data. In order to do this lawfully, in addition to having a lawful basis under Article 6, you need to meet one of the conditions in Article 9 of the UK GDPR. Some of these also require additional basis or authorisation in UK Law, which can be found in Schedule 1 of the DPA 2018.
Which (if any) of these conditions for processing special category data are appropriate depends on your individual circumstances.
Example: using special category data to assess discrimination in AI, to identify and promote or maintain equality of opportunity
An organisation using a CV scoring AI system to assist with recruitment decisions needs to test whether its system might be discriminating by religious or philosophical beliefs. While the system does not directly use information about the applicants’ religion, there might be features in the system which are indirect proxies for religion, such as previous occupation or qualifications. In a labour market where certain religious groups have been historically excluded from particular professions, a CV scoring system may unfairly under-rate candidates on the basis of those proxies.
The organisation collects the religious beliefs of a sample of job applicants in order to assess whether the system is indeed producing disproportionately negative outcomes or erroneous predictions for applicants with particular religious beliefs.
The organisation relies on the substantial public interest condition in Article 9(2)(g), and the equality of opportunity or treatment condition in Schedule 1 (8) of the DPA 2018. This provision can be used to identify or keep under review the existence or absence of equality of opportunity or treatment between certain protected groups, with a view to enabling such equality to be promoted or maintained.
Example: using special category data to assess discrimination in AI, for research purposes
A university researcher is investigating whether facial recognition systems perform differently on the faces of people of different racial or ethnic origin, as part of a research project.
In order to do this, the researcher assigns racial labels to an existing dataset of faces that the system will be tested on, thereby processing special category data. They rely on the archiving, research and statistics condition in Article 9(2)(j), read with Schedule 1 paragraph 4 of the DPA 2018.
Finally, if the protected characteristics you are using to assess and improve potentially discriminatory AI were originally processed for a different purpose, you should consider:
- whether your new purpose is compatible with the original purpose;
- how you will obtain fresh consent, if required. For example, if the data was initially collected on the basis of consent, even if the new purpose is compatible you still need to collect a fresh consent for the new purpose; and
- if the new purpose is incompatible, how you will ask for consent.
What about special category data, discrimination and automated decision-making?
Using special category data to assess the potential discriminatory impacts of AI systems does not usually constitute automated decision-making under data protection law. This is because it does not involve directly making any decisions about individuals.
Similarly, re-training a discriminatory model with data from a more diverse population to reduce its discriminatory effects does not involve directly making decisions about individuals and is therefore not classed as a decision with legal or similarly significant effect.
However, in some cases, simply re-training the AI model with a more diverse training set may not be enough to sufficiently mitigate its discriminatory impact. Rather than trying to make a model fair by ignoring protected characteristics when making a prediction, some approaches directly include such characteristics when making a classification, to ensure members of potentially disadvantaged groups are protected. Including protective characteristics could one of the measures you take to comply with the requirement to make ‘reasonable adjustments’ under the Equality Act 2010.
For example, if you were using an AI system to assist with sorting job applicants, rather than attempting to create a model which ignores a person’s disability, it may be more effective to include their disability status in order to ensure the system does not indirectly discriminate against them. Not including disability status as an input to the automated decision could mean the system is more likely to indirectly discriminate against people with a disability because it will not factor in the effect of their condition on other features used to make a prediction.
However, if you process disability status using an AI system to make decisions about individuals, which produce legal or similarly significant effects on them, you must have explicit consent from the individual, or be able to meet one of the substantial public interest conditions laid out in Schedule 1 of the DPA.
You need to carefully assess which conditions in Schedule 1 may apply. For example, the equality of opportunity monitoring provision mentioned above cannot be relied on in such contexts, because the processing is carried out for the purposes of decisions about a particular individual. Therefore, such approaches will only be lawful if based on a different substantial public interest condition in Schedule 1.
What if we accidentally infer special category data through our use of AI?
There are many contexts in which non-protected characteristics, such as the postcode you live in, are proxies for a protected characteristic, like race. Recent advances in machine learning, such as ‘deep’ learning, have made it even easier for AI systems to detect patterns in the world that are reflected in seemingly unrelated data. Unfortunately, this also includes detecting patterns of discrimination using complex combinations of features which might be correlated with protected characteristics in non-obvious ways.
For example, an AI system used to score job applications to assist a human decision-maker with recruitment decisions might be trained on examples of previously successful candidates. The information contained in the application itself may not include protected characteristics like race, disability, or mental health.
However, if the examples of employees used to train the model were discriminated against on those grounds (eg by being systematically under-rated in performance reviews), the algorithm may learn to reproduce that discrimination by inferring those characteristics from proxy data contained in the job application, despite the designer never intending it to.
So, even if you don’t use protected characteristics in your model, it is very possible that you may inadvertently use a model which has detected patterns of discrimination based on those protected characteristics and is reproducing them in its outputs. As described above, some of those protected characteristics are also special category data.
Special category data is defined as personal data that ‘reveals or concerns’ the special categories. If the model learns to use particular combinations of features that are sufficiently revealing of a special category, then the model may be processing special category data.
As stated in our guidance on special category data, if you use profiling with the intention of inferring special category data, then this is special category data irrespective of whether the inferences are incorrect.
Furthermore, for the reasons stated above, there may also be situations where your model infers special category as an intermediate step to another (non-special-category data) inference. You may not be able to tell if your model is doing this just by looking at the data that went into the model and the outputs that it produces. It may do so with high statistical accuracy, even though you did not intend for it to do so.
If you are using machine learning with personal data you should proactively assess the chances that your model might be inferring protected characteristics or special category data or both in order to make predictions, and actively monitor this possibility throughout the lifecycle of the system. If your system is indeed inferring special category or criminal conviction data (whether unintentional or not), you must have an appropriate Article 9 or 10 condition for processing. If it is unclear whether or not your system may be inferring such data, you may want to identify a condition to cover that possibility and reduce your compliance risk, although this is not a legal requirement.
As noted above, if you are using such a model to make legal or similarly significant decisions in a solely automated way, this is only lawful if you have the person’s consent or you meet the substantial public interest condition (and an appropriate provision in Schedule 1).
What can we do to mitigate these risks?
The most appropriate approach to managing the risk of discriminatory outcomes in ML systems will depend on the particular domain and context you are operating in.
You should determine and document your approach to bias and discrimination mitigation from the very beginning of any AI application lifecycle, so that you can take into account and put in place the appropriate safeguards and technical measures during the design and build phase.
Establishing clear policies and good practices for the procurement and lawful processing of high-quality training and test data is important, especially if you do not have enough data internally. Whether procured internally or externally, you should satisfy yourself that the data is representative of the population you apply the ML system to (although for reasons stated above, this will not be sufficient to ensure fairness). For example, for a high street bank operating in the UK, the training data could be compared against the most recent Census.
Your senior management should be responsible for signing-off the chosen approach to manage discrimination risk and be accountable for its compliance with data protection law. While they are able to leverage expertise from technology leads and other internal or external subject matter experts, to be accountable your senior leaders still need to have a sufficient understanding of the limitations and advantages of the different approaches. This is also true for DPOs and senior staff in oversight functions, as they will be expected to provide ongoing advice and guidance on the appropriateness of any measures and safeguards put in place to mitigate discrimination risk.
In many cases, choosing between different risk management approaches will require trade-offs. This includes choosing between safeguards for different protected characteristics and groups. You need to document and justify the approach you choose.
Trade-offs driven by technical approaches are not always obvious to non-technical staff so data scientists should highlight and explain these proactively to business owners, as well as to staff with responsibility for risk management and data protection compliance. Your technical leads should also be proactive in seeking domain-specific knowledge, including known proxies for protected characteristics, to inform algorithmic ‘fairness’ approaches.
You should undertake robust testing of any anti-discrimination measures and should monitor your ML system’s performance on an ongoing basis. Your risk management policies should clearly set out both the process, and the person responsible, for the final validation of an ML system both before deployment and, where appropriate, after an update.
For discrimination monitoring purposes, your organisational policies should set out any variance tolerances against the selected Key Performance Metrics, as well as escalation and variance investigation procedures. You should also clearly set variance limits above which the ML system should stop being used.
If you are replacing traditional decision-making systems with AI, you should consider running both concurrently for a period of time. You should investigate any significant difference in the type of decisions (eg loan acceptance or rejection) for different protected groups between the two systems, and any differences in how the AI system was predicted to perform and how it does in practice.
Beyond the requirements of data protection law, a diverse workforce is a powerful tool in identifying and managing bias and discrimination in AI systems, and in the organisation more generally.
Finally, this is an area where best practice and technical approaches continue to develop. You should invest the time and resources to ensure you continue to follow best practice and your staff remain appropriately trained on an ongoing basis. In some cases, AI may actually provide an opportunity to uncover and address existing discrimination in traditional decision-making processes and allow you to address any underlying discriminatory practices.