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Data ethics principles

Authorised Professional Practice

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This page is from APP, the official source of professional practice for policing.

First published
Data ethics
2 mins read

All officers and staff should consider the following principles when using data as part of their role.

The Appendix sets out additional questions that can help officers and staff to explore whether they are meeting these principles.

Public good

Processes should prioritise the safety and wellbeing of the public. When collecting and using data, the police should pay attention to any risks that may threaten an individual’s or group’s privacy or security. It is essential that the process has contributed to the advancement of public good.

Respect and dignity

Processes should show the highest standards of respect towards individuals and groups, as set out in the Code of Ethics.

Fairness and impartiality

Processes must operate with fairness and impartiality. They must not discriminate against, or disadvantage, individuals. This especially applies to vulnerable people, and to individuals and groups based on their ‘protected characteristics’, as defined in the Equality Act 2010.

Transparency and proportionality

Processes should be transparent. The processing of personal data by police officers and staff, particularly sensitive data, should be proportionate to the policing task. Processes should respect sensitive personal data and human rights in accordance with UK law, including the Human Rights Act 1998 and the Data Protection Act 2018.

Where a process restricts fundamental rights, such as the right of respect for private and family life and the right to freedom of expression, senior leaders should ensure that any limitations imposed:

  • are lawful, necessary and proportionate
  • fulfil a legitimate purpose

Robust evidence

Processes should be based on robust evidence that offers a thorough understanding of the data at hand, while raising awareness of potential biases and errors. This will include testing data that has been inputted or produced to ensure it is reliable. This ensures that ethical standards are maintained, and that decisions are made with precision and accountability.

Evaluation

Processes should be evaluated based on the level of associated risk to the data subjects, both prior to and after a process’s application. Further support can be found in the Data-driven technologies APP.

Human decisions, supported by technology

It is essential that a human makes the final decision when using information generated by a data-driven technology, and where there is a risk that the decision will have an impact on an individual, groups or communities. The human-in-the-loop makes the decision advised by the technology. The technology does not make the final decision.

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