The SDE Framework allows us to establish a standard, structured profile for a data ethics framework or methodology. These profiles allow us to quickly identify the which domains are addressed by the source.
When combined with data ethics culture and strategy diagnostics performed by tools based on the same framework, we’re able to detect alignment and highlight possible challenges.
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Strength Gradient: Lowest
Source level bias
Source dimension bias
Aspects of this source in the matrix
These are the key principles and values defined in this source.
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Rights around data sources
Where did you get the data from? Is it produced by an organisation or collected directly from individuals?
Was the data collected for this project or for another purpose? Do you have permission to use this data, or another basis on which you’re allowed to use it? What ongoing rights will the data source have?
Limitations in data sources
Are there limitations that could influence your project’s outcomes?
bias in data collection, inclusion/exclusion, analysis, algorithms
gaps or omissions in data
provenance and data quality
other issues affecting decisions, such as team composition
Ethical and legislative context
What existing ethical codes apply to your sector or project? What legislation, policies, or other regulation shape how you use data? What requirements do they introduce?
Consider: the rule of law; human rights; data protection; IP and database rights; anti discrimination laws; and data sharing, policies, regulation and ethics codes/frameworks specific to sectors (eg health, employment, taxation).
Reasons for using data
What is your primary purpose for collecting and using data in this project?
What are your main use cases? What is your business model?
Are you making things better for society? How and for whom?
Are you replacing another product or service as a result of this project?
positive effects on people
Which individuals, groups, demographics or organisations will be positively affected by this project? How?
How are you measuring and communicating positive impact? How could you increase it?
negative effects on people
Who could be negatively affected by this project?
Could the way that data is collected, used or shared cause harm or expose individuals to risk of being re-identified? Could it be used to target, profile or prejudice people, or unfairly restrict access (eg exclusive arrangements)?
How are limitations and risks communicated to people? Consider: people whom the data is about, people impacted by its use and organisations using the data.
minimising negative impact
What steps can you take to minimise harm?
How could you reduce any limitations in your data sources? How are you keeping personal and other sensitive information secure?
How are you measuring, reporting and acting on potential negative impacts of your project?
What benefits will these actions bring to your project?
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Source practices and requirements
Name/describe your project’s key data sources, whether you’re collecting data yourself or accessing via third parties.
Is any personal data involved, or data that is otherwise sensitive?
Are you routinely building in thoughts, ideas and considerations of people affected by your project? How?
What information or training might be needed to help people understand data issues?
Are systems, processes and resources available for responding to data issues that arise in the long-term?
communicating your purpose
Do people understand your purpose – especially people whom the data is about or who are impacted by its use?
How have you been communicating your purpose? Has this communication been clear?
How are you ensuring more vulnerable individuals or groups understand?