Government 

Digital Service 

Data Ethics Framework 
© Crown copyright 2020 

This publication is licensed under 

the terms of the Open Government 

Licence v3.0 except where otherwise 

stated. To view this licence, visit 

nationalarchives.gov.uk/doc/opengovernment-licence/version/3 

This publication is also available on our 

website at www.gov.uk/government/ 

publications/data-ethics-framework 

2 
Data Ethics Framework 3

Introduction 

Data Ethics Framework 

What is it for? 

The Data Ethics Framework guides 

appropriate and responsible data use in 

government and the wider public sector. 

It helps public servants understand 

ethical considerations, address these 

within their projects, and encourages 

responsible innovation. 

Who is it for? 

Teams should work through the 

framework together throughout the 

process of planning, implementing, 

and evaluating a new project. Each 

part of the framework is designed to 

be regularly revisited throughout your 

project, especially when any changes 

are made to your data collection, 

storage, analysis or sharing processes. 

Questions in each section will guide you 

through various ethical considerations 

in your project. Record your answers 

to create a data ethics assessment. 

The self-assessment scoring scale is 

designed to help you identify which 

aspects of your project need further 

work. 

How to use it? 

This guidance is aimed at anyone 

working directly or indirectly with data 

in the public sector, including data 

practitioners (statisticians, analysts 

and data scientists), policymakers, 

operational staff and those helping to 

produce data-informed insight. 

Structure 

The framework is split into overarching 

principles and specifc actions. 

Overarching principles are applicable 

throughout the entire process and 

underpin all actions and all aspects 

of the project. Specifc actions will 

guide you through different stages 

of the project and provide practical 

considerations. 

In addition, the framework provides 

specifc actions you can take at 

each stage of the project to advance 

transparency, accountability, and 

fairness. These are marked within each 

section. 
4 

Introduction 

Data Ethics Framework 

4 

5 

Overarching principles 

Defne and 

understand public 

beneft and user need 

Specifc actions 

1 

Involve diverse 

expertise 

Comply with the law 

Review the quality 

and limitations of 

the data 

Evaluate and 

consider wider 

policy implication 

Specifc actions will guide you 

through different stages of the 

project and provide practical 

considerations. 

Overarching principles are 

applicable throughout the entire 

process and underpin all actions 

and all aspects of the project. 

2 

3 

Transparency 

Accountability 

Fairness 
Introduction 

Overarching principles 

Transparency 

Transparency means that your actions, 

processes and data are made open to 

inspection by publishing information 

about the project in a complete, open, 

understandable, easily-accessible, and 

free format. In your work with and on 

data and AI, use the available guidance, 

e.g. the Open Government Playbook, 

to ensure transparency throughout the 

entirety of your process. 

Transparency 

Score 

0 1 2 3 4 5 

Information about the Information about the 

project, its methods, and project, its methods, and 

outcomes is not publicly outcomes is widely available 

available to the public 

Key: 

Start During After 

Data Ethics Framework 5 
 

Introduction 

Accountability 

Accountability means that there are 

effective governance and oversight 

mechanisms for any project. Public 

accountability means that the public or 

its representatives are able to exercise 

effective oversight and control over 

the decisions and actions taken by 

the government and its offcials, in 

order to guarantee that government 

initiatives meet their stated objectives 

and respond to the needs of the 

communities they are designed to 

beneft. 

Accountability 

Score 

0 1 2 3 4 5 

Mechanisms for scrutiny, 

governance, or peer review 

for the project haven’t been 

established 

Long-term oversight and 

public scrutiny mechanisms 

are built into the project cycle 

Key: 

Start During After 

Data Ethics Framework 6 
Introduction 

Fairness 

Fairness — It is crucial to eliminate your 

project’s potential to have unintended 

discriminatory effects on individuals 

and social groups. You should aim to 

mitigate biases which may infuence 

your model’s outcome and ensure that 

the project and its outcomes respect 

the dignity of individuals, are just, nondiscriminatory, and consistent with the 

public interest, including human rights 

and democratic values. 

You can read more about fairness and 

its different types in the Understanding 

artifcial intelligence ethics and safety 

guide developed by the Government 

Digital Service and the Offce for 

Artifcial Intelligence. 

You can read more about the 

standards regarding human rights in 

AI and machine learning in the Toronto 

Declaration developed by Access Now 

and Amnesty International. 

Fairness 

Score 

0 1 2 3 4 5 

There is a signifcant risk The project promotes just 

that the project will result and equitable outcomes, 

in harm or detrimental and has negligible detrimental 

discriminatory effects for effects, and is aligned with 

the public or certain groups human rights considerations 

Key: 

Start During After 

Data Ethics Framework 7 
Introduction 

Self-assessment tables for 

the 3 overarching principles. 

Please use the following self assessment to mark, track and 

share your overall progress over time against the following 3 

overarching principles. 

Data Ethics Framework 8

Score 

Principles 0 1 2 3 4 5 

Transparency 

Accountability 

Fairness 

Key: 

Start During After 

Example 
Introduction 

Self-assessment tables for 

the 5 specifc actions. 

Please use the following self assessment to mark, track and 

share your overall progress over time against the following 5 

specifc actions. 

Key: 

Start During After 

Data Ethics Framework 9

Score 

Specifc actions 0 1 2 3 4 5 

1 Defne public beneft and 

user need 

2 Involve diverse expertise 

3 Comply with the law 

4 Check the quality and 

limitations of the data 

4.1 Check the quality and 

limitations of the model 

5 Evaluate and consider wider 

policy implications 

Example 
Introduction 

Self-assessment tables for 

the 3 overarching principles. 

Please use the following self assessment to mark, track and 

share your overall progress over time against the following 3 

overarching principles. 

Key: 

Start During After 

Score 

Principles 0 1 2 3 4 5 

Transparency 

Accountability 

Fairness 

Data Ethics Framework 10 
Introduction 

Self-assessment tables for 

the 5 specifc actions. 

Please use the following self assessment to mark, track and 

share your overall progress over time against the following 5 

specifc actions. 

Key: 

Start During After 

Score 

Specifc actions 0 1 2 3 4 5 

1 Defne public beneft and 

user need 

2 Involve diverse expertise 

3 Comply with the law 

4 Check the quality and 

limitations of the data 

4.1 Check the quality and 

limitations of the model 

5 Evaluate and consider wider 

policy implications 

Data Ethics Framework 11 
12 

Defne and understand public 

beneft and user need 

When starting a public sector data project, you 

must have a clear articulation of its purpose. 

This includes having clarity on what public beneft 

the project is trying to achieve and what are the 

needs of the people who will be using the service 

or will be most directly affected by it. 

Action 1 

Data Ethics Framework 

1
Data Ethics Framework 13 

Action 1 

Defne and understand public 

beneft and user need 

When starting a public 

Using the circles move them to where 

your project is on the spectrum. Do this 

at the start of a project, during a project 

and after a project has completed. This 

will help you track, communicate and 

share your progress. 

sector data project... 

Defne public beneft and user need 

Score 

0 1 2 3 4 5 

Public beneft and user need 

are not clearly defned or 

understood 

Public beneft and user 

need are well defned and 

understood by all team 

members 

Key: 

Start During After 
1 

1.1 

Understand the wider 

public beneft 

Action 1 

Defne and understand public 

beneft and user need 

Public Beneft 

Understand unintended and or negative 

consequences of your project 

● What would be the harm in not using data? What social 

outcomes might not be met? 

● What are the potential risks or negative consequences of the 

project versus the risk in not proceeding with the project? 

● Could the misuse of the data/algorithm or poor design of the 

project contribute to reinforcing social and ethical problems 

and inequalities? 

● What kind of mechanisms can you put in place to prevent this 

from happening? 

● What specifc groups beneft from the project? What groups 

can be denied opportunities/face negative consequences 

because of the project? 

● What are direct benefts for 

individuals in this project? (e.g. saving 

time when applying for a government 

service) 

● How does the project deliver positive 

social outcomes for the wider public? 

● How can you measure and 

communicate the benefts of this 

project to the public? 

● What are the groups that would be 

disadvantaged by the project/ that 

would not beneft from the project? 

What can you do about this? 

Data Ethics Framework 

1.2 

14 
Action 1 

1.3 

Human rights 

considerations 

1.4 

Justify the beneft for the taxpayers and appropriate use of 

public resources in your project 

● How does the design and implementation of 

the project/ algorithm respect human rights and 

democratic values? 

● How does the project/algorithm work towards 

advancing human capabilities, advancing 

inclusion of underrepresented populations, 

reducing economic, social, gender, racial, and 

● How can you demonstrate the value for other inequalities?

money of your project? 

● What are the environmental implications of the 

● project? How could they be mitigated? Is there effective governance and 

decision-making oversight to ensure 

success of the project? 

● Do you have evidence to demonstrate all 

of the above? 

1.5 

Make your user need and 

public beneft transparent 

● Where can you publish information on how 

the project delivers positive social outcomes 

for the public? 

● How have you shared your understanding 

of the user need with the user? 

Data Ethics Framework 15 
Data Ethics Framework 16 

Action 1 

User need 

Describe the user need in your project: 

● As a ... 

● I need/want/expect to... [what does the user want to do?] 

● So that... [why does the user want to do this?] 

Example 1: the Register to vote service’s 

user need: 

● As a UK resident 

● I want to get my details on the online 

electoral register 

● So that I can vote 

Example 2: user need when building a 

platform for fre safety checks: 

● As a data analyst working in a fre and 

rescue service 

● I need to identify homes which are 

likely to not have a smoke alarm ftted 

● So that I can advise how to prioritise 

fre safety checks 

For various user needs, using data can: 

● help you identify themes in large 

volumes of text 

● predict what will happen 

● automatically categorise stuff 

● spot something unusual 

● show you how things are connected 

to each other 

● spot patterns in large volumes of data 

● spot geographic patterns in services 

or data 

1.7 

User needs’ are the needs that 

a user has of a service, and 

which that service must satisfy 

for the user to get the right 

outcome for them. For more 

information about the user 

need, consult the GDS Service 

Manual. 

Example: 

● running and improving 

services 

● building new services 

● trialling new processes 

for internal operations 

● testing existing and new 

policies 

1.6 

Understand the 

user need 

Ensure there is a clear articulation of the 

problem before you start the project. 
Data Ethics Framework 17 

Action 1 

1.8 

Check if everyone in your 

team understands the user 

need and how using data 

can help. 

Does everyone in your team understand 

the user need? 

● Often projects involving data analysis 

are requested by non-practitioners 

- people with an ill-defned problem 

they would like to understand better. 

Reframing their request as a user 

need will help you understand what 

they’re asking for and why, or expose 

what you don’t know yet. 

1.9 

Repeatedly revisit your user 

need throughout the project. 

Consider these questions as the project 

evolves: 

● What is the overall problem you’re 

trying to solve? 

● Who are the users of this data 

process or analysis? 

● What needs do they have? 
18 

Involve diverse expertise 

Working in diverse, multidisciplinary teams 

with wide ranging skill sets contributes to the 

success of any data or tech project. If you do 

not have the suffcient skills or experience, you 

should involve others from your team or wider 

network with the right expertise. 

Action 2 

Data Ethics Framework 

2
Action 2 

Involve diverse expertise 

Involve diverse expertise 

Score 

0 1 2 3 4 5 

The project team is 

homogeneous and there is 

little expert input 

The project team is diverse, 

multidisciplinary, with 

extensive expert input 

Key: 

Start During After 

Data Ethics Framework 19 
Data Ethics Framework 20

Action 2 

2 

Ensure diversity within your team 

Involve diverse expertise 

● Beyond data scientists, who are the relevant 

policy experts and practitioners in your team? 

● What other disciplines and subject matter 

experts need to be involved? What steps have 

been taken to involve them? 

● What should their roles be? Have you defned 

who does what in the project? 

● Ask your team and experts if you have the right 

data for your research questions. Get their help 

in matching the dataset to the problem. 

● How have you ensured diversity in your team? 

Having a diverse team helps prevent biases and 

encourages more creativity and diversity of thought. 

● Avoid forming homogenous teams, embrace 

diversity of lived experiences of people from 

different backgrounds. If you fnd yourself in a 

homogenous team, challenge it. 

2.1 

Get the right expertise 

2.2 
Data Ethics Framework 21

Action 2 

2.3 

Involve external stakeholders 

● How have you engaged external domain experts in 

your project (e.g. academics, ethicists, researchers)? 

● Have you consulted the relevant civil society 

organisations? 

● What is the impact that external engagement/ 

consultations have had on the project? 

● Have you considered consulting the target audience or 

the users of your project? This could be done through 

a range of deliberative processes and consultations. 

Transparency 

● Wherever appropriate, publish information 

on expert consultations and the project 

team structure 

2.5 

Effective governance 

structures with experts 

2.4 

● What senior/external oversight is there for your project? 

● What are the governance mechanisms that enable 

domain experts to challenge your project? 

● Who are the external experts/consultants who 

could review and assess the progress and ethical 

considerations of your project? How will you involve 

them? 

● What is the termination mechanism if the project stops 

being ethical? 
22 

Action 3 

Comply with the law 

You must have an understanding of the 

relevant laws and codes of practice that relate 

to the use of data. When in doubt, you must 

consult relevant experts. 

Data Ethics Framework 

3
Action 3 

Comply with the law 

Comply with the law 

Score 

0 1 2 3 4 5 

There is little clarity on legal 

requirements for the project 

Relevant legal requirements 

have been met, compulsory 

assessments completed, 

legal experts have been 

consulted 

Key: 

Start During After 

Data Ethics Framework 23 
Data Ethics Framework 24

Action 3 

3 

It is your duty and obligation to obey 

the law in any data projects. You must 

ensure the project’s compliance with 

GDPR and DPA 2018 

Comply with the law 

● Have you spoken to a legal adviser 

within your organisation? 

● Have you spoken to your 

information assurance team? 

● Have you consulted your 

organisation’s Data Protection 

Offcer when doing a DPIA? 

● What legal advice have you 

received? 

If you are using personal data, you must comply with the 

principles of the EU General Data Protection Regulation 

(GDPR) and Data Protection Act 2018 (DPA 2018) which 

implements aspects of the GDPR and transposes the 

Law Enforcement Directive into UK law. It also provides 

separate processing regimes for activities which fall 

outside the scope of EU law. 

Personal data is defned in Section 3(2) DPA 2018 (a 

wider explanation is detailed in Article 4 of the GDPR). 

3.1 

Get legal advice 

3.2 
Data Ethics Framework 25

Action 3 

Accountability Data protection by design and by default is a legal 

requirement under the GDPR (see Article 25). 

It is a legal obligation under Article 35 of the GDPR 

to complete a data protection impact assessments 

(DPIA) (also known as privacy impact assessments) 

when there’s likely to be high risk to people’s 

rights, particularly when using new technologies. 

It is good practice to do a DPIA for any use of 

personal data. 

An important aspect of complying with data protection 

law, is being able to demonstrate what measures you 

are taking to ensure everything is documented, as seen 

in Article 5(2) of the GDPR (the accountability principle) 

and Article 30 on keeping records of processing 

activities. 

Your organisation and information assurance teams will 

be responsible for this at a high level including ensuring 

policies and training are in place. However, it is essential 

to show how you are doing this at an individual level, 

through thorough documentation of things like Data 

Protection Impact Assessments. 

3.3 

Data protection by 

design and DPIA 

3.4 

Publish your DPIA and other 

related documents 

3.5 

Transparency 
Action 3 

3.6 

Ensure the project’s compliance with 

the Equality Act 2010 

Data analysis or automated decision making must not 

result in outcomes that lead to discrimination as defned 

in the Equality Act 2010. 

● How can you demonstrate that your project meets 

the Public Sector Equality Duty? 

● What was the result of the Equality Impact 

Assessment of the project? 

Consider additional relevant legislation 

and codes of practice. 

3.7 

Ensure effective governance 

of your data 

Organisations have a responsibility to keep both 

personal data and non-personal data secure. 

● How have you ensured that the project is 

compliant with data governance policies within 

your organisation? 

3.8 

Ensure your project’s 

compliance with any 

additional regulations 

Data Ethics Framework 26 
27 

Review the quality and 

limitations of the data 

Insights from new technology are only as good 

as the data and practices used to create them. 

You must ensure that the data for the project 

is accurate, representative, proportionally 

used, of good quality, and that you are able to 

explain its limitations. 

Action 4 

Data Ethics Framework 

4
Action 4 

Review the quality and 

limitations of the data 

Review the quality and limitations of the data 

Score 

0 1 2 3 4 5 

Data for the project is of 

bad quality, unsuitable, 

unreliable, notrepresentative 

Data used in the project is 

representative, proportionally 

used, accurate, and of good 

quality 

The model is reproducible 

and able to produce valid 

outputs 

The model is not 

reproducible and is likely to 

produce invalid outputs 

Key: 

Start During After 

Data Ethics Framework 28 
4 

Action 4 

Review the quality and 

limitations of the data 

4.1 

Data source ● What data source(s) is being used? 

● Are individuals and/or organisations providing the data aware of how it will be used? If the 

user is repurposing data for analysis without individual consent, how have you ensured 

that the new purpose is compatible with the original reason for collection (Article 6 (4) 

GDPR)? 

● Are all metadata and feld names clearly understood? 

● Do you understand how the data for the project is generated? Remember that 

depending on where the data came from, the feld may not represent what the feld 

name/metadata indicates. 

● What processes do you have in place to ensure and maintain data integrity? 

● What are the caveats? How will the caveats be taken into account for any future policy or 

service which uses this work as an evidence base? 

● Would using synthetic data be appropriate for the project? Synthetic data is entirely 

fabricated or abstracted from real data through various processes, e.g. anonymisation or 

record switching. It is often created with specifc features to test or train an algorithm. 

Data Ethics Framework 29 
Data Ethics Framework 30

Action 4 

Bias in data 

● How has the data being used to train a model 

been assessed for potential bias? You should 

consider: 

● Whether the data might (accurately) refect 

biased historical practice that you do not 

want to replicate in the model (historical bias) 

● The data might be a biased misrepresentation 

of historical practice, e.g. because only 

certain categories of data were properly 

recorded in a format accessible to the project 

(selection bias) 

● If using data about people, is it possible that 

your model or analysis may be identifying proxy 

variables for protected characteristics which could 

lead to a discriminatory outcome? Such proxy 

variables can potentially be a cause of indirect 

discrimination; you should consider whether the 

use of these variables is appropriate in the context 

of your service (i.e. is there a reasonable causal 

link between the proxy variable and the outcome 

you’re trying to measure?; do you assess this to be 

a proportionate means to achieve a legitimate aim 

in accordance with the Equality Act 2010?) 

● What measures have you taken to mitigate bias? 

4.3 4.2 

Determining proportionality 

You must use the minimum 

data necessary to achieve your 

desired outcome (Article 5(1) 

(c) of the GDPR). Personal data 

should be adequate, relevant 

and limited to what is necessary 

in relation to the purposes for 

which they are processed. 

● How can you meet the 

project aim using the 

minimum personal data 

possible? 

● Is there a way to achieve 

the same aim with less 

identifable data, e.g. 

pseudonymised data? 

● If using personal data 

identifying individuals, what 

measures are in place to 

control access? 

● Is the input data suitable 

and necessary to achieve 

the aim? 

● Would the proposed 

use of data be deemed 

inappropriate by those 

who provided the data 

(individuals and/or 

organisations)? 

● Would the proposed use 

of data for secondary 

purposes make it less likely 

that people would want 

to give that data again for 

the primary purpose it was 

collected for? 

● How can you explain why 

you need to use this data to 

members of the public? 

● Does this use of data 

interfere with the rights of 

individuals? If yes, is there 

a less intrusive way of 

achieving the objective? 
Data Ethics Framework 31 

Action 4 

4.4 

Data anonymisation 

If you plan to anonymise or pseudonymise 

personal data before linking or analysis, make 

sure you follow the ICO’s Anonymisation: 

managing data protection risk code of practice 

and document your methods. You can fnd 

more technical advice in the UK Anonymisation 

Networks anonymisation guidance. 

If the assumption is that data in the project is 

anonymised, consider the following: 

● How can you demonstrate that the data has 

been de-identifed to the greatest degree 

possible? 

● Can the data be matched to any other datasets 

that will make individuals easily identifable? 

What measures have you taken to mitigate 

this? Have you considered any determined 

intruder testing? 

Robust practices 

4.5 

● If necessary, how can you (or external scrutiny) validate that 

the algorithm is achieving the correct output decision when 

new data is added? 

● How can you demonstrate that you have designed the 

project for reproducibility? 

● Could another analyst repeat your procedure based 

on your documentation? Have they tried? 

● Have you followed the 3 requirements for 

reproducibility? (Applying the same logic (code, 

model or process), to the same data, in the same 

environment). 

● How have you ensured that high quality 

documentation will be kept? 

● Have you considered Reproducible Analytical 

Pipelines? (RAP) 

● How confdent are you that the algorithm is robust, and that 

any assumptions are met? 

● What is the quality of the model outputs, and how does this 

stack up against the project objectives? 
4.7 

Action 4 

Make your data open and 

shareable whenever possible 

4.6 

Share your models - developed data 

science tools should be made available for 

scrutiny wherever possible. If data is non-sensitive, non-personal, 

and if Data Sharing Agreements with the 

supplier allow it, you should make the data 

open and assign it a digital object identifer 

(DOI). For example, scientists share data 

when publishing a paper on Figshare and 

Datadryad. This gives others access to the 

data and the code, so the analysis can be 

reproduced. You can also publish data on 

Find open data and the UK Data Archive. 

● Can you openly publish your methodology, metadata about 

your model, and/or the model itself e.g. on Github? 

There are 2 main types of algorithms used in data science. 

The frst is the algorithmic methodology used to train a model. It’s 

often more useful and clear to share a document describing the 

analytical process than the code. 

The second is the trained model itself (the result of applying the 

methodology to the dataset). Releasing this model allows others 

to scrutinise and test it, and may highlight issues that you can fx 

as part of your continual improvement. 

When sharing models it’s important that it does not endanger 

either the: 

● privacy of those whose data was used to train it 

● integrity of the task being undertaken. 

Data Ethics Framework 32 
Action 4 

4.8 

How to ensure transparency 

of sensitive models 

Data Ethics Framework 33

Explainability 

Explainability is the extent to which the workings in a machine 

learning algorithm can be explained in human terms. It means 

expanding on the transparency of what variables are used 

to provide information on how the algorithm came to give an 

output, and how changing the inputs can change the output. 

● Explain what your project does and how it was designed 

in plain language to a non-expert audience. 

● Describe the process and the aim of your algorithm, as 

well as what variables are used for what outcomes without 

using technical terms. 

● Make this explanation publicly available (e.g. on GitHub, 

blogs, or gov.uk). 

● How are you planning to inform the 

public about the model? 

● Even if the model cannot be released 

publicly, you may be able to release 

metadata about the model on a 

continual basis, like its performance 

on certain datasets. 

● If your data science application is 

very sensitive, you could arrange for 

selected external bodies, approved 

by your organisation, to examine the 

model itself in a controlled context 

to provide feedback. This could be 

expertise from another government 

department, academia or public body. 

4.9 
34 

Action 5 

Evaluate and consider wider 

policy implications 

It is essential that there is a plan to continuously evaluate 

if insights from data are used responsibly. This means 

that both development and implementation teams 

understand how fndings and data models should be 

used and monitored with a robust evaluation plan and 

effective accountability mechanisms. 

Data Ethics Framework 

5
Action 5 

Evaluate and consider 

wider policy implications 

Evaluate and consider wider policy implications 

Score 

0 1 2 3 4 5 

There are no long-term 

evaluation and maintenance 

structures in place 

Continuous evaluation and 

long-term maintenance 

structures are in place 

Key: 

Start During After 

Data Ethics Framework 35 
Action 5 

5 Evaluate and consider wider 

policy implications 

Evaluate the project 

5.1 

Continuous evaluation - ask yourself and 

the team: 

● At the beginning of the project: 

‘are we doing the right thing?’ 

● During the project: ‘have we 

designed it well?’ 

● After the project: ‘is it still doing 

the right thing we designed it for?’ 

● How have you evaluated the project? 

Evaluation techniques you might 

use include holding retrospective 

roundtables at the end of the project; 

inviting an external expert or a ‘critical 

friend’ from a different team to observe 

and evaluate the project; request 

external consultations or audits. 

Repeatedly revisit the user need and 

public beneft throughout the project 

5.2 

● How has the user need changed? 

● Is the project still benefting the public? 

● Has there been a change of circumstances that 

might have affected the initial understanding of the 

public beneft in the project? If so, how can you 

adjust it? 

● How suffcient is the current human oversight of the 

automated project? 

● If any unanticipated harms emerged during the 

project, how have they been mitigated? 

Data Ethics Framework 36 
Action 5 

5.3 

Check how your project 

infuences policy 

● How accurately are the insights from the 

project used in the practical policy context? 

● How have you ensured if policymakers and 

secondary users of the tool fully understand its 

purpose and structure? 

5.4 

Ensure there are skills, training, 

maintenance for longevity of the project 

● How have you ensured that the users have the 

appropriate support and training to maintain the new 

technology? 

● What is the longevity of the project? 

● How have you checked if the users understand the 

software they need to maintain the project? Have they 

been appropriately trained? Accountability structures 

5.5 

● What are the governance 

structures in place to ensure 

a safe and sustainable 

implementation of the project? 

● How often will you update the 

board/governing bodies? 

Data Ethics Framework 37 
Data Ethics Framework 38

Action 5 

Public scrutiny 

5.6 

● Do members of the public/ end users of the 

project have the ability to raise concerns and 

place complaints about the project? If yes, 

how? If not, why? 

● What channels have you established for public 

engagement and scrutiny throughout the 

duration of the project? 

Share your learnings 

● How have you documented and shared 

the progress and case studies from your 

project with peers and stakeholders? 

5.7 

Next steps: if you have scored a 3 or less in any 

of the principles, this could indicate the need for 

additional checks and potential changes to make your 

project more ethical. Please explain the reason for the 

score and consult the outcome with your team leader/ 

organisational ethics board/data ethics lead to advise 

on the specifc next steps to improve the ethical 

standards of your project.