The pervasive impact of Artificial Intelligence (AI) is discernible across every sector of modern life. This advanced technology is reinvigorating services and industries with unprecedented pace and efficiency. As AI continues to pervade various aspects of society, which indicates its far-reaching use in businesses and data manipulation, it has become dire to protect its abusive use. Almost all data protection regulations have recognised AI data protection as the call for time. General Data Protection Regulation (GDPR), for example, has published a study offering companies dealing with the personal information of EU citizens, some well-defined rules and suggestions.
In this article, we will go through the need for data and privacy protection in today’s AI-powered business landscape in light of GDPR.
What is GDPR
GDPR is a set of rules and regulations enforced on organisations dealing with the personal data of the citizens of the European Union (EU) and European Economic Area (EEA). The European Parliament and the Council of the European Union enacted GDPR on April 20, 2016, which was put into action on May 25, 2018. GDPRapplies extraterrestrially on any organisation and third-party processors that collect, share, store, and process personal data of EU citizens. It has been taken as the foundation of most regulations in the world due to its stringency and commitment to protect the privacy rights of individuals.
AI is compatible with the GDPR: The Intersection Between Them
AI aims to mimic human cognitive skills through machine and computer systems. For this, it leverages advanced technologies, such as machine learning (ML), Natural Language Processing NLP), etc. With the increasing use of AI in digital systems for data collection and manipulation, regulating its impact has become dire.
This is because AI comes with both good and bad – on one side, it streamlines operations while on the other hand hackers also use AI technologies to mask themselves and breach data privacy which may lead to catastrophic consequences for both organisations and data owners. Therefore, it becomes necessary to assess the use and operation of AI systems in light of the requirements of data privacy laws.
In the study we mentioned earlier, the European Parliament addressed the interception of GDPR and AI and how GDPR fits in the context of AI.
Even though GDPR is widely used to regulate the implementation of AI in personal data processing, it doesn’t provide data controllers or processors enough guidelines on the effective use of AI.
To address the limitations of the study, the European Parliament started developing the proposed EU AI Act. One of the very first regulations in the world delineating the regulated use of AI, the proposed EU Act is committed to ensuring transparency and lawful use of AI systems in personal data processing.
That being said, GDPR doesn’t explicitly mention AI in the provisions. However, the provisions can be used while implementing AI systems in data processing. Needless to mention, how AI is being used in personal data processing also throws challenges to some of the provisions. In addition, conflicts arise when you try to implement traditional data protection regulations on data gathered and processed by AI and big data. However, most of the GDPR provisions hold good when you use AI and big data systems.
Let’s go through how GDPR principles apply to AI systems as well.
Lawful Data Processing
Article 6 of GDPR requires data controllers to ensure personal data processing activities are lawful, fair and transparent. The Regulation sets out the lawful bases of processing and mandates organisations to comply with at least any of these bases that hold good for AI processing systems as well. These bases include consent, contractual necessity, legal obligations, or legitimate interests, to ensure compliance with data protection laws and safeguard individuals’ rights and privacy.
Purpose Limitation
One of the key principles of the Regulation, purpose limitation, mandates organistaions to limit processing of personal data to the defined purpose only. Any further processing beyond this already-mentioned purpose would be considered an act of non-compliance, hence penalty. In terms of AI and big data, this principle obligates companies to keep data subjects well-aware of the intended purpose of using AI systems for data processing. This would help comply with the provision. processing purposes In the context of AI data protection, ensuring compliance with purpose limitation is essential to prevent misuse of personal data. GDPR obligates organisations (whether controllers, processors or AI system developers) to clearly define and abide by the original purpose of AI-based data processing (controllers, or processors) or the development of an AI tool (developers) to ensure accountability, transparency, and regulatory compliance during the process. Organisations should curate their processing purpose that align with the flexibility of big data and AI-based data collection methods. It should be created in a way that allows reuse of data if further processing is not incompatible with the original purpose.
GDPR considers data reuse for statistical purposes compatible, thereby permitting it unless it poses any risk to the data subject.
Data Minimisation and Pseudonymisation
GDPR allows organisations to collect and store personal data that is required to meet the intended purpose. Storing data once the purpose is met without legitimate reasons would be considered an act of non-compliance. This principle holds good in the context of AI tool development or using AI systems for personal data processing. GDPR stresses the need for reducing the identifiability of the stored personal data using techniques such as anonymization and pseudonymisation. The aim is to secure personal data and augment an individual’s privacy.
Stringent measures should be implemented to contain unauthorised re-identification unless lawful criteria are met. Anonymisation and pseudonymisation of personal data should be compatible with the original purpose of data processing.
Information Requirements for AI Processing
GDPR acknowledges the complexity of AI-based data processing that makes it challenging for organisations to meet the information requirements of the Regulation. The sheer amount of data AI-systems generate or process is another key challenge. However, complying with this principle is mandatory for organisations and requires them to clearly define all necessary information to data subjects in an easily understandable manner. GDPR suggests organisations to skip the technical jargons and make data subjects understand the purpose of AI-based processing, and the limitations.
Automated Decision-making
GDPR mandates organisations that inference or make decisions solely on automated processes that can affect data subjects to inform them of this practice. Data subjects should be informed of the decision-making logic, its implications and impact on them. Organisations must keep this information easily accessible to data owners. They should be able to challenge automated decisions if felt required.
Privacy by Design
GDPR mandates organistaions to take proactive technical and organisational measures to uphold data subject privacy. THis principle also holds good on AI tools or using AI-based data processing and doesn’t impede their operations. However, organistaions may incur additional costs to meet these requirements.
Individual Rights
The GDPR grants the following individual rights in connection with the use of data in AI models:
- Access and portability: GDPR empowers data subjects with the right to access their personal data at any time and request to get this data for reuse. This right also applies to AI tools allowing users to get their data recovered while also transferring it to another system if required.
- Right to explanation: Data subjects hold the right to get insight into the reasons behind the controller’s decision made through automated data processing. AI systems should be transparent enough to offer users easily comprehensible decision-making processes.
- Right to be forgotten: According to this principle, AI systems should have proper measures to ensure personal data can be deleted upon receiving erasure requests from data subjects.
Best Practices to Ensure AI Development and Implementation Complies with GDPR
Following a few steps can guide you through your GDPR compliance journey and help dodge penalties.
Integrating Data Security and Privacy into AI Development
Keep Dara security and privacy on the top of your compliance checklist to comply with your AI development process with GDPR. For this:
- Security reviews for API endpoints: Design and implement the security APIs of your AI system with extra caution to ensure. It will streamline your GdPR compliance journey while also helping prevent accidental data loss.
- SDLC audit: To track down issues, rectify errors, and keep the AI system at its best performance level, you need to conduct SDLC audit once in every other year. Thus, you can ensure your system is highly protected and shielded from the reach of scammers – a prerequisite of GDPR compliance.
Defining Data Governance Standards for AI Projects
Another key to AI data protection, hence GDPR compliance is to establish transparent, clear and comprehensive data governance standards for AI projects. With these, you will have a fixed process for gathering, analysing, storing and interpreting data within an AI system for all.
Having a clear process also facilitated the process of GDPR compliance.
Execution of DPIAs
In its Article 35, GDPR has made conducting Data Protection Impact Assessments (DPIAs) for AI tools dealing with high-risk processes mandatory. Conduct DPIs as a preventive strategy, specially for intricate AI tools that handle data processing tasks.
Informing Users About AI-Driven Decision Logic
GDPR mandates organisations inform data subjects if their decisions are based on automated processes. It includes making them understand the decision logic behind AI’s data processing capabilities, and how decisions are made. GDPR demands that users be informed about the reasoning behind AI-driven choices.
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