AI can help companies automate processes and perform complex analysis. However, it can also perpetuate biases if the data sets used to train them are not diverse and representative.
Data protection laws such as the GDPR include requirements for businesses to be transparent about how they use personal data, including where automated decision-making takes place. Here is how AI can be useful in Data Protection Law:
Legal Issues
AI uses huge datasets to learn, creating powerful tools that can improve our lives. AI challenges the ability of information privacy laws to function as they have previously. This is because AI systems often incorporate the biases and stereotypes of the data they analyze, which can result in discriminatory decisions or worsen existing disparities. This type of discrimination is in direct violation of the equality laws.
The emergence of AI also brings new issues to data protection law, such as how to address human rights, data security, and ethics in an increasingly automated world. AI is often used to perform complex and sensitive tasks, such as analyzing medical records or interpreting financial data. This creates a significant risk for data breaches and privacy violations, especially when the systems are being operated by non-human operators.
This brings up the question of how to implement effective consent mechanisms for data processing involving AI, as well as how to determine whether a given activity is covered by data protection law at all. These issues are complicated by the fact that AI processes data differently than humans, and it is sometimes difficult to determine what is personal data. This can make it harder to satisfy the requirements of the GDPR and other data protection regulations, which require purpose specification, collection limitation, and use limitation for any activity involving data.
In addition, the CCPA specifically addresses “automated decision making, including profiling” directly. This means that AI activities may be subject to additional requirements, such as being informed, being able to opt-out and requesting the deletion of data.
All of this adds up to a very challenging situation for companies using AI, particularly in the US, where there is no national or multinational privacy laws but an ever-growing patchwork of state and local privacy laws. In this context, organizations need to do a much more detailed and rigorous privacy assessment for any AI-related project, as well as ensure that any privacy policies and consent agreements clearly reflect any AI-related activities.
Data Protection Impact Assessments (DPIAs)
If you’re responsible for implementing new processes, systems, or products that involve personal data, you need to know how these projects will impact individuals’ privacy. Data protection impact assessments (DPIAs) are one way of assessing potential risks and mitigating them accordingly. DPIAs are often a legal requirement in some circumstances, and failure to carry one out could leave you open to enforcement action. DPIAs are typically carried out when a project involves processing personal data, particularly sensitive information, or when a new technology or process is introduced.
The GDPR provides some guidance on DPIAs, particularly in Article 35. This states that a DPIA is “always required where the processing of personal data is likely to result in a high risk for the rights and freedoms of natural persons.” This includes automated decision making that may have a significant effect on individuals, processing of special categories of personal data or personal data related to criminal convictions and offenses, and systematic monitoring of public areas on a large scale. The DPIA criteria developed by the WP29 (Article 29 Working Party) have been largely adopted by the EDPB, the EU’s new supervisory authority, and these are used to determine whether or not a DPIA is required for particular processing operations.
A DPIA should be conducted for every new processing operation that involves personal data, although a single assessment can cover multiple processing operations if they present similar high risks. DPIAs should be conducted as early as possible in the lifecycle of a project, with their findings and recommendations incorporated into the project’s design, so they can help to inform data processing decisions.
DPIAs can also be used to raise awareness of privacy and data protection within the organization, which can have further benefits including ensuring compliance with the GDPR, inspiring confidence in the public, and helping reduce costs through optimizing information flows and eliminating unnecessary data collection and processing.
The UK’s ICO has published guidelines on DPIAs, which offer a straightforward approach you can follow to ensure your DPIAs are conducted efficiently and effectively. It is also good practice to reassess a DPIA regularly, for example, after 3 years or earlier if the context in which the processing operates changes significantly.
Privacy by Design
Privacy by design (PbD) is a framework that incorporates privacy into a product or service from the beginning of its development. It aims to prevent privacy-invasive events from occurring, rather than fixing them after they have occurred. The framework was developed in the 1990s by Ann Cavoukian, former Information and Privacy Commissioner of Ontario, and has since been incorporated into data protection laws around the world.
The PbD philosophy avoids trade-offs between privacy and usability and offers users a choice and control over the data they provide. It also focuses on ensuring that the data collected is secure throughout its lifecycle. PbD principles include avoiding unintended data leaks by separating and encrypting sensitive information, preventing the transfer of personal data to unauthorized third parties, and ensuring that consent is not obtained for unnecessary purposes. Additionally, the philosophy advocates for using user-friendly and granular consent mechanisms.
Regardless of whether or not your business is subject to GDPR or other privacy regulations, incorporating PbD into your product or service is a best practice that will benefit both your consumers and your business. It shows that you recognize the value of privacy to your consumer base and respect their right to control their own data. It also shows that you are willing to go above and beyond the requirements of your regulatory body.
For example, imagine a company’s website requires visitors to agree to data collection before they can use the site. Instead of an intrusive pop-up that restricts the website’s functionality, the company uses a non-intrusive cookie banner that allows users to opt-in to data collection while still providing full functionality.
This is a simple yet effective way to ensure compliance with European data protection standards while maintaining user satisfaction and building trust.
While it may seem like a minor distinction, it is one of the many small changes that can be implemented to improve data protection and help build consumer confidence in your brand. Privacy is a fundamental human right that your customers deserve to be respected, and it’s important to remember that the law supports this belief.
Data Security
The large amounts of data needed to fuel AI processing pose a security challenge. This means companies need robust tools to collect, process and store the information in a secure way. It also requires strong policies and procedures that allow them to comply with privacy and data protection laws when using the data for AI processing.
Companies rely on AI to identify patterns and relationships in vast amounts of data that might not be obvious to humans. However, it is possible that these systems could highlight personal information that was not intended to be used for this purpose, or repurpose that information in ways that were not reasonably expected by the individual. This raises issues around the use of AI in breach of the transparency, notice and consent objectives of information privacy law.
Another concern is the risk that the underlying algorithms of an AI system may produce decisions that are unlawful or unfair because they reflect biases in the data used to train the algorithm. This risks running into the equalities and fairness requirements of broad data protection law. This is a common concern raised by civil rights and consumer groups.
It is also important for AI to be able to verify that the decisions it makes are correct. This can be achieved by providing counterfactual edge cases that show what would have happened if a decision had been made differently. This can help to mitigate the risks of unintended consequences caused by the assumptions and biases of the people who create and use the AI.
Lastly, it is essential for AI to be secure enough to prevent attacks from hackers. This is a significant challenge because the technology is constantly evolving and the threat landscape is always changing. Attacks can target the huge volumes of data that AI processes, as well as the resulting analysis and predictions.
The good news is that comprehensive data protection and privacy laws can address most of these issues. The GDPR, for example, already includes a requirement to inform individuals of any automated decision making taking place that affects them, and there are plans to extend this to cover AI in California.
Ethical Considerations
AI’s contribution to data protection law is not limited to legal considerations. The ethical considerations of AI’s use in data protection and privacy are crucial. Here are some important points to keep in mind:
Fairness and Transparency
AI can unintentionally perpetuate biases in the training data. This can lead to unfair or discriminatory results. Ethics requires that AI systems be designed and trained in a way that is fair, unbiased, and equitable. This is especially important when automating decisions that impact individuals’ rights and opportunities.
Data protection laws require AI systems to be transparent and explain their decisions. Users and data subjects are entitled to know how and why AI systems handle their data. This is crucial for establishing trust and accountability.
Accountability
Organizations are responsible for AI systems that they deploy. Ethics involves establishing clear lines for AI systems. This includes oversight, monitoring, and the ability to correct errors or unintended effects.
Ethical AI encourages the use and development of privacy-preserving technology. These technologies enable organizations to gain valuable insights from their data without compromising the privacy of individuals. Techniques such as federated learning, homomorphic cryptography, and differential privacy are important in maintaining data protection principles.
Ethical AI encourages data minimization. Organizations should only collect and use the data they need for a particular purpose to reduce the risk of privacy breaches.
Ethical AI demands informed consent before processing data. This consent must be specific, clear, and revocable to ensure that the individual has control over his or her personal data.
Ethical use cases and Data Security
Organizations should carefully examine the ethical implications of AI applications they pursue. Some applications such as deepfakes and invasive surveillance may raise ethical concerns, or may not align themselves with data protection principles.
Ethical AI requires robust data security to protect sensitive personal information from breaches and unauthorized access. It is vital to maintain the trust of individuals by ensuring that AI systems are secure.
Human oversight is important in many AI applications. Ethical AI acknowledges the limitations AI systems have and makes sure that human intervention is possible when needed, especially for high-stakes decision.
Engagement of Stakeholders
To ensure that AI systems are ethically developed and deployed, it is important to engage with a variety of stakeholders. These include data subjects, regulators and advocacy groups.
These ethical considerations are becoming increasingly important to regulators and organizations, and they’re incorporating them in data protection laws and guidelines. Ethics in AI are crucial to ensuring that AI technology enhances data protection, privacy and human rights while maintaining fundamental values and principles.