This article aims to deeply explore the key principles of data ethics and to better consider the ethics in data collection. The article will explore the broader implications and ethical concerns organisations should have in their day-to-day processing activities when handling and collecting data. Hopefully, reading this article is a step forwards to understanding data ethics and considering how data collection methods and collecting data can be more ethical.
Key Takeaways
- Core Principles of Data Ethics: Ethical data practices foreground principles such as privacy, transparency, accountability, and fairness. These principles guide the responsible handling of data, ensuring that individual rights are respected and that trust is built and maintained.
- Importance of Ethical Practices: Ethical data use prevents harm, mitigates bias, and promotes fairness, promoting trust and cooperation between individuals and organisations. Compliance with principles of data ethics also supports compliance with laws like GDPR, reducing the risks of legal penalties. All of these aim to protect and safeguard data as much as possible.
- Challenges and Frameworks: Implementing ethical data strategies depends on addressing challenges including bias, data complexity, and regulatory compliance. Effective frameworks should ensure ongoing monitoring, informed consent, and efficiency in data use while integrating ethics into wider organisational culture.
What is Data Ethics?
Data ethics refers to the moral principles and guidelines that govern the collection, analysis, and use of data, especially sensitive data. In this digital age, data is the arguably the highest commodity in the world. Having ethical guidelines can help avoid unethical data collection and allow for appropriate and responsible data stewardship by organisations who hold the data.
In general, data ethics asks a very big question: what should we do with data? What moral considerations should we have? This question is always relevant because new technical possibilities and use dilemmas expand the field continuously.
Questions about ethical data use are attracting increasing attention, and we all have expectations for how our own data should or should not be handled.
Here are some basic features of data ethics, or ethical data practice:
- Privacy: Ensuring that data collection (or data gathering), data collection practices, data processing and data storage are carried out with the individual’s consent and in a manner that maintains their confidentiality and avoids data misuse.
- Transparency: Providing clear and accessible information about how data is being used, who has access to it, and for what purposes.
- Accountability: Establishing mechanisms for holding custodians of data responsible for adhering to ethical standards and data protection laws.
- Fairness: Implementing data practices that prevent bias and discrimination in data collection, analysis, and application.
Data ethics has several synonyms, for example ethical data practice, responsible data governance, and so on. Either way, whatever term is used, it is essential for ensuring that data is collected and used in a way that respects individuals’ rights and promotes trust.
Questions in Data Ethics and ethical data collection
Here are a few examples of important questions in data ethics:
- What data uses are ethically permissible or obligatory, and why?
- What data rights should individuals or organisations hold, and why?
- How should data practices be regulated, and why?
- What values should underpin data practices and regulations, and why?
- What data practices should be (un)lawful, and why?
Why Does Data Ethics Matter?
Moral Reasons
- Respect for privacy: Data ethics ensures the protection of data subjects’ personal information, which safeguards their privacy, autonomy and respect human dignity.
- Preventing harm: Ethical data practices help to avoid misuse of data that could lead to discrimination, exploitation, or other forms of harm to individuals and communities.
- Promoting fairness: Ethical data practice helps to prevent biases in data collection and analysis, which promotes and protects fair and just outcomes for all stakeholders.
- Building trust: Ethical handling of data fosters trust between data subjects and organisations, which promotes and enhances cooperation and engagement.
- Upholding accountability: Data ethics requires transparency and accountability in data use, which can help to ensure that those who manage data are held responsible for their actions. Consent is necessary for collecting someone’s personal data, and it should be informed, freely given, and revocable.
Legal Reasons
Adherence to ethical data practice tends also to ensure adherence to privacy laws, such as UK General Data Protection Regulation (UK GDPR), protecting individuals’ personal information from unauthorised access and misuse.
It also supports adherence with anti-discrimination laws, by promoting fair and unbiased data practices.
Adhering to ethical data practice helps to ensure that transparency requirements mandated by relevant regulations are met, and this fosters trust and accountability.
Ethical data management helps to reduce the risk of legal penalties and fines associated with data breaches and non-compliance being applied.
Finally, it helps to ensure that appropriately robust consent mechanisms are in place, aligning with legal standards for collecting and using personal data. Ensuring data subjects’ privacy is an ethical responsibility, and data should be stored in a secure database using data security methods.
Reputational Reasons
Giving priority to ethical data practices can enhance your organisation’s reputation.
It can differentiate you from less ethically sensitive competitors; it can help to ensure individuals feel safe and allow you to collect and use their data. And, by reducing your risks of data breaches, ethical data practices also minimise the potential costs of handling such breaches. So, it is morally, legally, commercially worth taking data ethics seriously.
Some Key Issues and Concepts in Data Ethics
Consent
Consent is necessary for collecting someone’s personal data, and it should be informed, freely given, and revocable.
Consent is a centrally important ethical issue in data handling.
In general, to give or to seek consent is to give or to seek permission. In the data context, consent involves seeking and securing permission from individuals to use their data in particular ways.
Often, the consent process reflects what we are used to in various contexts in everyday life, e.g. when giving consent to undergo a medical procedure, or when asked for consent to our data being shared with another healthcare provider. Also, we are familiar with being asked by apps for consent to collect and store data about us. Algorithms used in data analysis can be biased and cause harm to individuals or groups.
Transparency
Many relevant data governance laws and documents require that there be clear and accessible communication about how personal data is processed. When processing personal data, you should:
- Inform people about their data rights;
- Explain the purposes of data processing;
- Flag the involvement of third parties, and
- Justify why you process data in a particular way.
Accountability
The following requirements are necessary for accountability under existing regulations of data use:
- First, organisations should be responsible for how they use data;
- Second, organisations should show that their data use complies with the law; and
- Third, there should be effective mechanisms to oversee projects involving the use of data.
Fairness
Fairness relates to transparency insofar as all individuals should be entitled to expect the same conditions and standards of data handling, and the details thereof
The Information Commissioner’s Office (ICO) and the Data Ethics Framework do not construe fairness in the same way.
The ICO is more concerned about the impact of data use on the relevant individuals, and whether such impact is justified. The Data Ethics Framework treats fairness as a value specifically targeting the biases involved in data processing.
Despite this discrepancy, it is vital that you take both senses of fairness seriously when processing data, from ethical data collection, to data analysis.
Privacy and Security
Data privacy involves the handling and protection of personal information to ensure it is not misused or accessed without permission, and that the relevant ethical considerations have been observed. It is important that to protect the data and adhere to the data protection principles, among which are ensuring lawfulness, fairness and transparency, purpose limitation, data minimisation, data accuracy, storage limitation, data integrity and confidentiality.
It is ethically important because it safeguards individuals’ autonomy, it maintains trust, and it prevents harm or discrimination that could arise from unauthorised use or exposure of sensitive information
Properly respecting data privacy requires the upholding of the freedom and control rights of the individuals concerned.
Data breaches can have severe consequences, and organisations should take measures to prevent and respond to such incidents.
Privacy and Security
Data privacy involves the handling and protection of personal information to ensure it is not misused or accessed without permission, and that the relevant ethical considerations have been observed.
It is ethically important because it safeguards individuals’ autonomy, it maintains trust, and it prevents harm or discrimination that could arise from unauthorised use or exposure of sensitive information.
Properly respecting data privacy requires the upholding of the freedom and control rights of the individuals concerned.
Data breaches can have severe consequences, and organisations should take measures to prevent and respond to such incidents.
Anonymised and Pseudonymised Data
Anonymisation refers to data processed in a way that it makes it practically impossible to re-identify an individual from the data, making the data no longer personally identifiable information. After this process is carried our the data is considered to be anonymous data. Anonymisation of data can be distinguished from a related process, which is known as pseudonymisation.
UK General Data Protection Regulation (UK GDPR) stipulates terms of permitted data use, but in general this applies only to personal data, not non-personal data. Personal data is defined in the UK GDPR as personal data as any information relating to an identified or identifiable natural person.
There is one exception though, for what is known as pseudonymised data, which is personally identifiable information which is processed in a way that prevents it being attributed to a specific individual without the use of additional information, such as a digital key, as long as that information is kept separately and governed by security measures to ensure non-attribution to a specific individual. Access to the key must be limited to specific individuals and / or for specific purposes if needed.
Here, the information has not been fully anonymised, as it is still technically possible to re-identify individuals within the dataset, with access to the key, but it will make reidentification more difficult and thus provide a layer of protection.
Challenges for Implementing Ethical Data Strategies
Bias
Algorithms used in data analysis can be biased and cause harm to individuals or groups. Data collection practices can perpetuate biases and reinforce existing inequalities.
You might be familiar with stories about ‘racist artificial intelligence (AI)’ or ‘sexist AI’, where biased data has led to an AI producing correspondingly biased outputs, e.g. predictions, amplifying those biases.
This matters from the perspective of social justice, wherever it appears, whatever the sector.
Balancing Innovation in Data with Ethical Considerations
Business owners must balance innovation with ethical considerations, such as fairness, transparency, and accountability.
Ethical considerations should be considered when making decisions about data collection and data analysis.
Consultation with experts and stakeholders can help ensure that ethical considerations are prioritised.
Navigating Regulation
All members of any organisation which handles people’s data should be aware of the latest changes in data protection laws and regulations.
Employees should be trained and educated on the latest regulatory practices to ensure legal compliance of their organisation’s policies and processes.
Navigating complex regulatory landscapes can be challenging, but it is essential for ensuring ethical data practices.
Data Complexity
Complexity is a challenge for ensuring confidentiality, data privacy and data security, and for several reasons, the scale of the challenge increases in proportion to the complexity of the organisation.
There are many instances in any large and complex organisation where data sharing might be necessary for the provision of services – insurance or mortgage broking, banking, retail, utilities, and so on. Irrespective of the service or sector, in each case it is a function of complexity and size that there are also many points at which a breach of confidentiality could occur.
For any and all of these instances, the robustness of the data governance procedures must be commensurate with the risk posed by the size and complexity of the organisation.
Why Does Ethical Data Practice Matter?
Building Trust with Customers and Partners
Ethical data practices can build trusting relationships with customers and partners.
Trust is a valuable asset for any organisation, and ethical data practices can enhance a business’s competitive advantage.
Ethical data practices can also mitigate legal and compliance risks. Here are some examples of why data ethics matters:
- Respect for privacy: Data ethics ensures the protection of individuals’ personal information, which safeguards their privacy and autonomy.
- Preventing harm: Ethical data practices help to avoid misuse of data that could lead to discrimination, exploitation, or other forms of harm to individuals and communities.
- Promoting fairness: Ethical data practice helps to prevent biases in data collection and analysis, which promotes and protects fair and just outcomes for all stakeholders.
- Building trust: Ethical handling of data fosters trust between data subjects and organisations, which promotes and enhances cooperation and engagement.
- Upholding accountability: Data ethics requires transparency and accountability in data use, which can help to ensure that those who manage data are held responsible for their actions. Trust is a valuable brand asset, and ethical data practices can enhance a business’s competitive advantage.
What is the Relation Between Ethics and Law or Regulation in Data?
The relation between ethics and law is imperfect, which means lawful data practices might and should be but are not necessarily ethical.
In general, it is prudent to assume that data breaches, i.e. failures of legal compliance, are likely also to be an ethical risk.
So, in general this also means that by committing to ethical data practices, you can reduce the risk of encountering legal problems.
Adherence to ethical data practice tends also to ensure adherence to privacy laws, such as UK General Data Protection Regulation, protecting individuals’ personal information from unauthorised access and misuse.
It also supports compliance with anti-discrimination laws, by promoting fair and unbiased data practices.
Adhering to ethical data practice helps to ensure that transparency requirements mandated by relevant regulations are met and this fosters trust and accountability.
Ethical data management helps to reduce the risk of legal penalties and fines associated with data breaches and non-compliance being applied.
Finally, it helps to ensure that appropriately robust consent mechanisms are in place, aligning with legal standards for collecting and using personal data.
Building a Data Ethics Framework
Purpose Limitation and Data Minimisation
Data should be collected and used only for legitimate purposes and not further processed in a way that runs contrary to or undermines those purposes.
Organisations should collect and retain only the minimum data necessary for the intended purpose.
Purpose limitation can also help reduce privacy risks and mitigate potential misuse or unauthorised access.
Limitations of Data Ethics Frameworks
Frameworks are a useful way to start thinking through, formalising, and creating a reliable process for ensuring that relevant ethical standards are met.
Especially in the absence of formal training in (research) ethics, and when employees in organisations are busy and need to be able to work efficiently and confidently.
However, frameworks are not infallible and they require ongoing monitoring. For example, do further risks appear that haven’t been anticipated? What happens when an ethical challenge is not captured precisely by the framework?
Frameworks are not a complete substitute for close engagement with substantive ethical issues, since ethical reasoning about data, or anything, is not about just completing a checklist.
Data Ethics Guidance Documents
In the UK, several documents are available that can provide useful general guidance for ensuring ethical data practices, and developing your own data ethics framework for your own ethical data processes in your own organisations.
- UK Government Data Ethics Framework;
- Scottish Government Data Ethics Framework;
- UK Statistics Authority Ethics Self-Assessment Tool;
- Open Data Institute Data Ethics Canvas; and
- Organisation for Economic Co-operation and Development (OECD) Good Practice Principles for Data Ethics in the Public Sector
Conclusion
Embedding data ethics into organisational data practices is essential for fostering, promoting, and developing a culture of responsibility and integrity. Ethical data collection practices not only safeguard individuals’ rights and privacy but also ensure the accuracy and reliability of the data being used. By applying these key principles, organisations can minimize the risk of harm and enhance the quality of insights derived from data, building a strong foundation for ethical decision-making.
Ensuring consistency with ethical data principles is crucial for building trust with stakeholders and enhancing an organisation’s reputation. Transparent and fair data practices demonstrate respect for individuals and society, which reinforces public confidence and loyalty. Ethical practices also help businesses remain compliant with data protection laws as they evolve in line with technological advances, mitigating legal risks and demonstrating a commitment to social responsibility. This consistency creates a competitive advantage in an increasingly data-conscious marketplace.
Finally, organisations must balance innovation with ethics by developing robust frameworks for accountability and social benefit. As data-driven technologies evolve, ethical data practices should act as a guide or direction of travel, ensuring that innovation serves individuals, society, and humanity rather than harming it. By embedding accountability processes and prioritising the public good, organisations in all sectors can lead the way in responsible data usage, contributing to a sustainable and ethical digital future.
Frequently Asked Questions
What is data ethics and why is it important?
Data ethics is the study and application of moral principles to the collection, use, and sharing of data, ensuring fairness, transparency, and respect for individuals’ rights. It can also be referred to by synonyms such as ‘ethical data practice’, or ‘responsible data stewardship’.
A general introduction to data ethics and its significance for ensuring responsible data usage can be found here:
An Introduction to Data Ethics: What is the Ethical Use of Data?
What are the basic principles of data ethics?
The basic data ethics principles include, but are not limited to, fairness, accountability, transparency, privacy, consent, fairness and the responsible use of data.
An overview of important ethical principles, such as transparency, accountability, privacy, fairness, and data quality can be found here:
5 Principles of Data Ethics for Business
What ethical considerations are relevant to data privacy and security?
Ethical considerations include, but are not limited to, safeguarding personal information, preventing unauthorised access, optimising the efficiency of data linkage, and being transparent about data usage.
A general overview helpful for understanding data ethics, with a useful focus on issues such as the protection of personal information, observance of privacy regulation, and data anonymisation can be found here:
Big Data Ethics – Redefining Values in a Digital World
How can bias in data and algorithms be identified and mitigated?
Bias can be identified through using diverse training and testing datasets, regular impact assessments, and can be mitigated by improving inadequate dataset diversity, algorithm design, and regulatory oversight.
A general overview of data ethics with useful guidance about how to detect and reduce bias in datasets and ensure fairness when using AI models can be found here:
Data Ethics: Examples, Principles, and Uses
What ethical risks arise from data breaches?
Data breaches can expose sensitive personal or commercial information, which can in turn lead to a range of ethical harms including privacy violations, identity theft, financial loss, and data misuse.
Regulatory information for Small to Medium Enterprises and Sole Traders about how to mitigate ethical risks of data breaches when an individual’s personal data has been compromised can be found here:
Understanding and Assessing Risk in Personal Data Breaches
What ethical challenges arise in data sharing and collaboration on data projects?
Relevant ethical challenges include the maintenance of privacy of data subjects, the protection of intellectual property, ensuring informed consent, and addressing any power imbalances among collaborators to eliminate risk of coercion.
A set of FAQs outlining ethical practices in data, which contains useful information addressing concerns about consent, ownership, and security when sharing or linking data across organisations can be found here:
FAQ – Principles of Data Ethics
How should organisations manage ethical dilemmas arising from conflicts of interest?
To manage these ethical issues, all organisations should develop clear, implementable policies, they should ensure transparency, and create oversight mechanisms which can address and resolve conflicts in an impartial way.
A framework for decision-making when basic ethical principles clash with business or research priorities, developed for the context of manufacturing, but applicable to other professional contexts can be found here:
Data Ethics FAQs for Manufacturing Businesses
How can ethical principles be integrated into data governance frameworks?
Ethical principles can be integrated through the development and application of appropriate policies, training, accountability mechanisms, and regular oversight to ensure consistency with the relevant ethical standards.
An overview of strategies that can be used for embedding data ethics into policies, training, and organisational culture can be found here:
Data Ethics: What it Means and What it Takes
What are the consequences of unethical data practices?
Consequences of unethical data practice can include, but are not limited to, a loss of trust, reputational damage, legal penalties, and harm to individuals or communities.
A magazine article providing an accessible introduction to potential risks of unethical data practices, such as legal penalties, loss of public trust, and harm to individuals or communities can be found here:
It’s Practically Impossible to Run a Big AI Company Ethically
