AI & Data Privacy Laws: How Businesses Can Stay on the Right Side of Regulation

Introduction to AI and Data Privacy

Currently, 75% of financial services and the largest law businesses are already using AI technology. So are 68% of large accountancy and consulting businesses.

However, as with many valuable technological advancements, unleashing AI potential introduces a set of challenges. AI systems rely on large volumes of personal and sensitive data to recognise patterns and make decisions. That includes contact details, health records, and credit history.

Most businesses have become accustomed to complying with UK General Data Protection Regulation (GDPR) and the Data Protection Act (DPA) 2018 requirements due to AI tools processing personal data. But successful businesses take this a step further. They approach these challenges not as a compliance burden but as a competitive advantage.

By engaging the appropriate teams from the outset and avoiding the creation of siloed AI system development projects, they ensure that the necessary data foundations are established from the very beginning.

Here is how you can follow their blueprint and build AI tools that handle data ethically and responsibly, meet customers’ expectations, and comply with AI data privacy laws.

Safeguarding Privacy With AI Data Privacy Laws: GDPR, DPA 2018, and the ICO

Understanding key legal frameworks such as the GDPR and DPA 2018 is essential for businesses utilising AI systems that handle personal data in the UK. Here is a breakdown of their main requirements:

The General Data Protection Regulation (GDPR)

GDPR mandates that every business using AI tools to process personal information of anyone based in the UK adheres to the following fundamental principles:

• Lawfulness. All AI systems must process personal data only where there are valid legal grounds, such as consent or legitimate interest. That implies that if a user visits your website and only accepts essential cookies, your AI-based solutions can’t use any other personal information associated with this customer.
• Fairness. Businesses must ensure that AI models don’t process information in a way that could result in unfair treatment. For example, an AI-based recruitment tool shouldn’t discriminate against candidates based on gender, race, age, or ethnicity.
• Transparency. Users must be clearly informed about how their data is utilised. Thus, you shouldn’t disclose only what data is collected, but also how AI decisions are generated and the potential consequences of such decisions.
  Purpose Limitation. Data should be used only for the original, legitimate purpose for which it was collected. For instance, if you have implemented an AI-based fraud detection system, you can’t use the outcomes for unrelated marketing activities (e.g., personalised offers or quotes).
• Data Minimisation. The more data you feed into AI, the more accurate the results. Nonetheless, AI models shouldn’t hoard all information available. They should only collect and process data that is really necessary for the AI tool’s intended function.

GDPR and AI: Algorithms Don’t Rule the World

Article 22 of the GDPR imposes strict limitations on automated decision-making for UK businesses, requiring:

• Human involvement in decisions that may significantly affect individuals legally or financially,
• Transparency in the decision process, and
• The opportunity for individuals to contest decisions.

For instance, while you can use automated profiling to evaluate credit card applications, any refusal must be reviewed by a human.

On the other hand, decision-making can be fully automated if the process is clearly explained, explicit consent is obtained, it is necessary for contractual obligations, or if authorised by UK law.

The Data Protection Act (DPA) 2018

The DPA 2018 works in tandem with GDPR and AI. It defines the role and powers of the UK Information Commissioner’s Office (ICO), as the data protection authority and addresses specific data processing concerns within the UK context by:

• Clarifying the processing of personal data carried out by government bodies, the House of Parliament, and Ministers of the Crown.
• Covering areas outside the UK GDPR’s scope related to data processing by law enforcement and intelligence services.
• Specifying regulations and exceptions for cross-border data transfer requirements.

Essentially, both GDPR and DPA are more than mere legal frameworks. They empower each individual to control how their personal data is handled, used, shared, and analysed.

When Frameworks Overlap

In the past few years, countries have been rapidly introducing multiple AI data privacy laws. That has led to additional complexities and overlaps. As a result, businesses now face an intricate framework of AI data privacy laws that introduce additional liability rules and are enforced by different authorities.

For instance, imagine you have deployed an AI-based credit scoring system to streamline loan approvals and personalise the products offered to your customers. The tool already complies with GDPR and the DPA Act.

But that isn’t enough. The system must also meet the UK government’s pro-innovation AI regulation strategy. Moreover, if you have European customers, you will also be scrutinised under the EU AI Act, a collection of AI data privacy laws with a risk-based approach and hefty fines up to EUR 35,000,000 or 7% of worldwide annual turnover.

Nowadays a responsible AI deployment requires more than ensuring compliance with the GDPR and the Data Protection Act 2018.

AI Meets Personal Data: 4 Real-World Compliance Trigger Examples

• AI-based systems can create substantial business value and benefits. However, these data-hungry tools require a robust AI governance strategy, particularly when used to process personal and sensitive data. For instance,
• Automated credit scoring can lead to discrimination. Financial businesses using AI for credit assessments must ensure their models don’t perpetuate biases. If the training data reflects historical ethnic biases, AI could discriminate against specific ethnic groups, leading to unfair credit decisions.
• Recruitment algorithms can cause ethical and legal issues. When you use AI models to analyse resumes automatically, it is essential to ensure that they are transparent and unbiased. If an algorithm favours candidates based on gender or age, for example, it may result in serious ethical issues and costly legal repercussions.
• AI-based customer profiling puts you at risk of data breaches. AI tools are a blessing for analysing consumer behaviour. However, the harvested personal data must be handled responsibly and securely. Failure to do so can expose your business to data breaches or misuse of sensitive information.
• Predictive analytics may impact individual rights. Insurance providers often leverage AI to analyse policy applications based on the likelihood of potential future claims. But such predictions might discriminate against certain groups or individuals, especially if decisions are taken without human oversight.

Don’t Risk It, Prioritise and Assess

To mitigate discrimination, data bias, and misuse, implement responsible data practices which emphasise:

• Transparency. Clearly document and communicate data processing methods and how algorithms make decisions.
• Explainability. Ensure that the decision-making process of AI models isn’t a black box. Each step of the process must be easy to understand and justify.
• Right to human review. Include a human review of outcomes. It’s an essential step that will protect the individuals involved against unfair outcomes and enhance accountability.

To support these compliance efforts and identify risks:

• Run regular Data Protection Impact Assessments (DPIAs). It’s a legal requirement for high-risk processing activities. DPIAs help organisations identify and mitigate risks to personal data before implementing AI technologies. They are necessary whenever a new project or technology poses significant risks to individuals’ rights and freedoms.
• Don’t underestimate third-party AI tools (e.g., chatbots or automated HR platforms). Even if you don’t own these systems, you are still responsible for compliance alongside the provider.
• Ensure both you and the provider conduct thorough due diligence to meet the same legal and ethical standards for data privacy and protection.

AI Tools’ Fundamental Problem: Transparency

Transparency is one of the GDPR’s central principles in AI contexts. The ICO takes this key principle a step further. The executive body emphasises the need for “meaningful information about the logic involved” in AI operations, alongside an understanding of the significance and potential consequences of data processing.

To be fully compliant with AI data privacy laws, businesses must therefore clearly inform individuals when AI systems process their data, providing clear and concise information about:

• What data is being collected.
• How AI systems handle information.
• The purposes of processing and the potential consequences of automated decisions.

However, translating complex machine learning algorithms into easily understandable language can be extremely difficult. For example, a credit scoring model may include numerous variables and intricate patterns that are hard to explain to a general audience.

This intricacy can result in significant compliance gaps. It can also lead to misunderstandings and a lack of trust among users as they may struggle to grasp how AI tools make decisions that affect them.

The Solution: Accountability

In such a complex environment, accountability lets you demonstrate compliance, enhance transparency, and earn users’ trust. Prove your adherence to AI regulations by:

• Creating robust documentation.
• Implementing clear data governance policies.
• Setting up audit trails.
• Running periodic internal AI ethics reviews.

Addressing transparency challenges isn’t just about compliance. It’s about proactive governance and fostering a culture of ethics and trust that strengthens relationships with customers and stakeholders.

4 Ways Businesses With Limited Resources Can Manage AI Risks

Large businesses typically have dedicated teams to assess and manage AI risks. But medium-sized businesses can’t do that due to limited resources and their:

• Dependence on vendors or off-the-shelf AI tools. Clinging to third-party tools without a thorough understanding of the data flows involved can lead to unintentional non-compliance with data protection laws.
• Over-reliance on third parties for compliance. If one of your vendors fails to meet legal standards or doesn’t implement adequate data protection measures, it can expose your business to vulnerabilities.
• Poor staff training. As a result, employees may be unaware of their responsibilities concerning data privacy and ethical AI use.

These four strategies can help medium-sized businesses proactively mitigate AI risks:

• Run simplified DPIAs. If you don’t know where to start, use ICO’s templates and resources. They will allow you to evaluate how the data is processed and the potential impact on each individual while ensuring compliance with legal obligations, even with limited resources.
• Ensure your vendor contracts include data protection clauses. Your contracts should clearly state that your vendors must comply with relevant data protection laws and regulations. It will ensure that you both meet compliance requirements.
• Regularly review your AI systems’ outputs for bias or unfair outcomes. Get a human to verify AI systems’ decisions. For instance, if you’re a financial institution, monitor loan approval rates across different demographics and genders. It will facilitate the identification and rectification of any biases or discriminatory practices that may arise, ensuring fair treatment of all individuals.
• Publish clear privacy statements and adopt opt-in consent mechanisms. Providing transparent privacy notices helps your users and customers understand how you collect, process, and use their data. Implementing opt-in consent tools empowers them to control their data, boosting compliance and trust.

Future-Proof Your AI Compliance Strategy With a Proactive and Ethical Approach

The integration of AI tools into operations brings unique challenges that extend beyond mere legal compliance. To address these challenges, adopt a structured approach that balances ethical standards and responsible AI throughout the AI lifecycle.

It won’t only safeguard your business against regulatory breaches, but also foster trust and transparency.

• Establish dedicated ethics boards. Cross-functional ethics boards that combine different perspectives (e.g., legal, IT, and compliance) facilitate a holistic understanding of AI implications. They ensure that ethical considerations are embedded in your AI development and deployment processes.
• Leverage AI assurance frameworks. These frameworks help you evaluate the trustworthiness of your AI systems, ensuring they align with ethical principles and AI data privacy laws. For instance, the UK’s proposed AI regulatory framework emphasises transparency and accountability in AI deployment and the necessity for adaptable rules.
• Cooperate internationally. AI technology transcends borders. Keep up to date with evolving ICO guidelines and international best practices to ensure alignment across regions. It will help you in maintaining compliance while embracing best practices.

Ultimately, responsible AI adoption goes beyond avoiding fines. It fosters trust and transparency, providing a competitive advantage in an increasingly data-driven landscape. Acora’s Data & AI experts can help you confidently navigate the complexities of AI, while complying with regulations and maintaining high standards of protection and innovation. Get in touch.