In today’s digital age, organisations increasingly use AI systems to process large amounts of personal data. These systems can potentially transform businesses and society in profound ways. But they also pose significant risks to the rights and freedoms of data subjects. To mitigate these risks and meet their accountability obligations, organisations must conduct Privacy Impact Assessments (PIAs) for AI systems.

In this post, we’ll define a PIA, offer reasons for its relevance, and explain how to conduct one for an AI system. In the end, you’ll have a foundational understanding of how to conduct PIAs for your AI systems.

What is a PIA?

A PIA is a systematic assessment of the potential risks and impacts of a project or system on the privacy of data subjects. It aims to identify and evaluate the privacy risks associated with a project or system and develop measures to mitigate them.

The relevance of PIAs for AI systems

AI systems are particularly complex and often involve large amounts of personal data. Plus, using AI systems to process personal data can have significant privacy implications. To address these implications, PIAs help you identify and mitigate the privacy risks of AI systems before they harm data subjects.

Conducting a PIA for an AI system

  1. Define the scope. The first step is to define the PIA’s scope. This process involves identifying the AI system’s purpose, the data it processes, the users, and the intended outcomes. The scope should also consider applicable laws and regulations governing data privacy, such as POPIA, the GDPR, or the CCPA.
  2. Identify and map data flows. The next step is identifying and mapping the AI system’s data flows. This step includes understanding how you collect, process, store, and share data. Identifying all the data types the system collects—including personal and other valuable or sensitive data—is essential (as you’ll see later).
  3. Identify the privacy risks. Once you’ve identified the data flows, the next step is identifying the potential privacy risks of the AI system. At this stage, you’d also assess the likelihood and impact of the risks occurring. Common privacy risks related to AI systems include:
    • bias and discrimination,
    • inaccurate data,
    • data breaches, and
    • the unexpected use of personal data.
  4. Evaluate the impact. The next step is to evaluate the effect of the identified risks on the rights and freedoms of data subjects. The evaluation should also consider the context of the data processing and the sensitivity of the data.
  5. Mitigate risks. The following step is to identify measures to mitigate the identified risks. This process involves identifying technical and organisational measures to reduce the likelihood of the risks occurring and their impact. Ultimately, your team should design the measures to protect data subjects and comply with applicable laws and regulations.
  6. Review and update the PIA. Finally, it’s essential to review and update the PIA regularly. How? By monitoring the AI system’s processing activities, assessing new risks that may arise, and updating the PIA as necessary.

Actions you can take next

This post gave you a foundational understanding of Privacy Impact Assessments for AI systems. Still, there’s much more to consider regarding AI PIAs to comply with data protection laws, like managing variations and margins of error in AI systems and documenting trade-offs relating to the data protection principles. So, here are some actions you can take next: