Managing AI to Ensure Compliance with Data Privacy Laws


INSIGHT
Published
Apr 3rd '24
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Artificial intelligence (AI) is a powerful technology that can enhance business performance, innovation, and customer satisfaction.

 

However, AI also poses significant challenges to data privacy and compliance, as it involves collecting, processing, and analyzing large amounts of personal and sensitive data. Data privacy laws, such as the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the US, impose strict obligations and restrictions on how organizations can use and share data, especially regarding AI applications.

 

Therefore, chief technology officers (CTOs) and chief information officers (CIOs) must implement effective data governance and AI compliance strategies to ensure their AI systems are ethical, transparent, and accountable.

 

Data governance and AI compliance challenges

Data governance is the process of establishing and enforcing policies, standards, and procedures for managing data throughout its lifecycle. Data governance aims to ensure data quality, security, availability, and compliance with relevant laws and regulations. AI compliance ensures that AI systems adhere to the legal and ethical requirements and expectations of data protection, fairness, accountability, and transparency. AI compliance also involves monitoring and auditing the performance and behavior of AI systems and providing mechanisms for human oversight and intervention.

 

Some of the main challenges that CTOs and CIOs face when integrating AI with data governance and compliance are:

 

Data quality and accuracy

AI systems rely on large and diverse datasets to train and operate. However, if the data is incomplete, inaccurate, outdated, or biased, it can affect the reliability and validity of the AI outputs and decisions. Therefore, it is crucial to ensure that the data used for AI purposes is accurate, relevant, consistent, and representative of the target population or domain.

 

Data security and privacy

AI systems are often involved in the processing of personal and sensitive data, such as biometric, health, or financial information. This data is subject to various data protection laws and regulations, which require organizations to obtain consent, provide notice, limit access, and implement safeguards to protect the data from unauthorized or unlawful use, disclosure, or breach.

 

It’s vital to ensure the data is securely stored, transmitted, and processed and that the data subjects’ rights and preferences are respected and fulfilled.

 

Data intelligibility and transparency

AI systems often use complex and opaque algorithms to generate outputs and decisions. However, these algorithms may not be easily understandable or interpretable by humans, especially when they involve deep learning or neural networks.

 

This can create challenges for explaining and justifying the logic, rationale, and criteria behind the AI outputs and decisions, as well as for providing information and disclosure to the data subjects, regulators, and other stakeholders. Given these challenges, it is essential to ensure the AI systems are transparent and explainable and that the data and algorithms are documented and accessible.

 

Data fairness and accountability

AI systems may exhibit or amplify biases, discrimination, or errors that can affect the outcomes and impacts of the AI outputs and decisions. These biases or errors may stem from the data, algorithms, or human factors involved in the AI systems’ design, development, or deployment.

 

This can create challenges for ensuring the fairness, accuracy, and reliability of the AI outputs and decisions and assigning and enforcing responsibility and liability for the AI actions and consequences. It should be imperative that the AI systems are fair and accountable and that the data and algorithms are tested and audited.

 

Understanding the legal landscape

Data privacy’s legal landscape constantly evolves, with each region boasting its regulations. The European Union’s GDPR and California’s CCPA/CPRA are prominent examples, emphasizing transparency, individual control, and stringent data security measures. For example:

 

  • GDPR Article 22 states that the data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning them or similarly significantly affects him or her.
  • The California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA) grant consumers the right to opt out of the sale of their personal information and the use of their data for profiling purposes. This includes specific automated decision-making processes based on personal data.

 

Additionally, (on the date this blog was written) two other US states included restrictions on automated processing:

 

  • Colorado: The Colorado Privacy Act (CPA) also gives consumers the right to opt-out of the sale of their personal data and the use of their data for profiling. Additionally, it outlines specific requirements for transparency and fairness in automated decision-making practices.
  • Connecticut: The Connecticut Data Privacy Act (CTDPA) grants rights similar to those of California and Colorado, including the right to opt out of the sale of personal information and the use of data for profiling. It also emphasizes fairness and transparency in automated decision-making.

 

However, regulations extend beyond specific data points. Algorithmic fairness and non-discrimination requirements are increasingly taking center stage for governments. The EU’s Artificial Intelligence Act exemplifies this trend, advocating for bias detection and mitigation strategies within AI systems.

 

Therefore, the first and most crucial step in managing AI for compliance is understanding the relevant legal framework in each jurisdiction. Consulting your legal department or external counsel and staying abreast of regulatory updates is crucial to avoid costly repercussions and build trust with your users.

 

Data governance and AI compliance best practices

Data privacy regulations are being created to empower individuals with various rights concerning their personally identifiable information. Your organization can be seen as respecting these various rights by enabling users to quickly and easily request access to, rectify, erase, and restrict the processing of their data using AI algorithms.

 

Moreover, consider offering specific opt-out mechanisms for AI use, allowing users to choose how their data is used or whether they wish to engage with AI decision-making processes altogether.

 

To address the challenges and risks of AI and data privacy, CTOs and CIOs should adopt and implement the following best practices for data governance/data privacy and AI compliance:

 

  • Carry out a proactive AIA and, if required, complete a Data Protection Impact Assessment (DPIA) before purchasing or developing an AI system. The AIA is a systematic process of identifying, analyzing, and evaluating the potential hazards and harms associated with an AI system. A DPIA is a specific type of risk assessment that focuses on the data protection implications of an AI system, especially when it involves processing personal or sensitive data. The proactive AIA and DPIA will help CTOs and CIOs identify and mitigate AI technology’s data privacy and compliance risks and determine the appropriate measures and safeguards to implement.
  • Ensure that the AI capabilities satisfy the requirements for privacy by design and by default. Privacy by design and by default are principles that require organizations to embed data protection and compliance into the design and development of their products, services, and processes and apply the highest level of data protection and compliance settings by default.
  • Conduct regular tests and audits of the AI systems to ensure they comply with the data privacy and compliance standards and expectations. Tests and audits are processes of verifying and validating the performance and behavior of the AI systems, as well as the data and algorithms that underpin them. Tests and audits can help CTOs and CIOs to ensure that the AI systems are accurate, reliable, secure, and fair, as well as to identify and correct any errors, biases, or anomalies that may arise or emerge in the AI systems. Tests and audits can also help CTOs and CIOs to demonstrate and document the data privacy and compliance of the AI systems and provide evidence and assurance to the data subjects, regulators, and other stakeholders.
  • Disclose AI system-related details to the data subjects and other stakeholders. Disclosure is the process of providing information and notification to the data subjects and other stakeholders about the AI systems’ existence, purpose, and operation, as well as the data and algorithms that underlie them.
  • Honor opt-outs and consent from the data subjects. Specific AI consent is the process of obtaining and maintaining the agreement and permission of the data subjects to collect, process, and share their data for AI purposes. Opt-out enables the data subjects to withdraw or refuse their consent or participation in the AI systems.
  • Fulfill data subject rights (access, deletion, appeal/human review). Data subject rights are the rights and entitlements the data subjects have about their data and the AI systems that use their data. These rights include the right to access, delete, correct, or restrict their data and the right to appeal or request a human review of the AI outputs and decisions that affect them.
  • Demonstrate compliance and auditability. Compliance and auditability are the abilities and capacities of the AI systems to comply with the data privacy and compliance laws and regulations and to be subject to external or internal review and verification.

 

Managing AI use for data privacy compliance is a crucial strategy, but it’s only the beginning. As the industry navigates algorithm use’s complex ethical and legal landscape, embracing a broader concept of responsible AI development and use becomes vital. This requires going beyond legal mandates and actively prioritizing principles:

 

  • Fairness and Non-discrimination
  • Accountability and Transparency
  • Human Oversight and Control
  • Privacy by Design and Security
  • Societal Impact Assessment

 

AI is a transformative technology that can bring many opportunities and benefits. However, AI poses many challenges and risks for data privacy and compliance, as it involves using and processing large amounts of personal and sensitive data. Therefore, CTOs and CIOs should adopt and implement effective data governance and AI compliance strategies to ensure their AI systems are ethical, transparent, and accountable and comply with the relevant data privacy and compliance laws and regulations.

 

Many more AI laws will emerge in the following months and years that complicate CIOs’ and CTOs’ jobs. To help organizations with the growing AI compliance requirements, companies can look for help from specialized applications overseeing AI development, compliance, and use.

 

By following the best practices outlined in this article, CTOs and CIOs can leverage the power of AI while safeguarding the privacy and rights of the data subjects and other stakeholders.

 

FEATURED GUIDE: Generative AI and Compliance

 

Source: Smarsh

 

About Bill Tolson

Bill Tolson is President of Tolson Communications LLC, an advisory and consulting firm. He has 25-plus years in the archiving, information governance, data privacy, data security, and eDiscovery industries. Bill has held executive leadership positions in a wide range of high technology organizations, from consulting firms and technology startups to multinationals. Companies include Contoural, Hewlett Packard, StorageTek, Iomega, Hitachi Data Systems, Recommind, Actiance and Archive360 where he was the Vice President of Global Compliance and eDiscovery for seven years.

 

Bill is a frequent speaker at legal and information governance industry events and has authored four eBooks including Email Archiving for Dummies, Cloud Archiving for Dummies, The Bartenders Guide to eDiscovery and the Know IT All’s Guide to eDiscovery. Bill has also authored 60 plus industry articles and hundreds of blogs as well as hosting 37 podcasts with industry pundits, subject matter experts, state legislators, and attorneys.

 

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