Legal and ethical responsibilities further heighten the importance of high-quality data in AI development. Regulations like the General Data Protection Regulation (GDPR) in the UK and Europe underscore the need for transparent, accurate, and fair use of data in automated systems. Developers who ignore data quality not only risk building ineffective models but also open themselves and their organisations to potential lawsuits, fines, and reputational damage.
Under data protection laws in the UK and Europe, Personal data must be:
- processed lawfully, fairly and in a transparent manner in relation to the data subject;
- collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes;
- adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed;
- accurate and, where necessary, kept up to date;
- kept in a form which permits identification of data subjects for no longer than is necessary for the purposes for which the personal data are processed; and
- processed in a manner that ensures appropriate security of the personal data, including protection against unauthorised or unlawful processing and against accidental loss, destruction or damage, using appropriate technical or organisational measures
If used well, AI has the potential to make organisations more efficient, effective and innovative. However, AI also raises significant risks for the rights and freedoms of individuals, as well as compliance challenges for organisations.
Different technological approaches will either exacerbate or mitigate some of these issues, but many others are much broader than the specific technology. The data protection implications of AI are heavily dependent on the specific use cases, the population they are deployed on, other overlapping regulatory requirements, as well as social, cultural and political considerations.
While AI increases the importance of embedding data protection by design and default into an organisation’s culture and processes, the technical complexities of AI systems can make this more difficult. Demonstrating how you have addressed these complexities is an important element of accountability.