Legal Lighthouses In The A.I Storm; A Business Guide For Today’s African Legal Counsel — By TripleOKLaw — Catherine Kariuki and Janet Othero

In this article, we’ll journey through the landscape of Artificial Intelligence (AI) adoption in the business world, but with a unique twist. Our exploration will be presented as a guide, viewed through the lens of a legal counsel. We center our discourse on a hypothetical scenario involving ‘SimbaTech’, an innovative African enterprise.

Fresh from the boardroom, SimbaTech has made a riveting announcement – their intent to embrace AI to propel their business forward. As we dissect this narrative, our goal is to spotlight the multifaceted considerations their legal counsel must juggle, guiding SimbaTech to not only harness the potential of AI but also fortify its interests and remain steadfast in its compliance with evolving regulations.

AI Landscape & SimbaTech’s Position

At its core, AI mimics human cognitive functions like learning and problem-solving. As various sectors including healthcare, education, agriculture, manufacturing, infrastructure, legal and finance leverage its capabilities, understanding the multifaceted nature of AI, from machine learning algorithms to neural networks, becomes foundational for any legal discourse. The African tech space is blossoming, with AI at its forefront. The AfCFTA not only promises greater intercontinental data fluidity but also mandates stringent data protection norms.

Due diligence while acquiring AI

AI’s integration in a business can follow varied paths. If a business opts for white-label AI Solutions or external acquisition, it’s not just about rebranding. Due diligence becomes paramount. Assess the intellectual property and ownership of AI systems, scrutinize algorithms, and verify compliance with licenses. Evaluate transferability, regulatory adherence, and contractual ties. Consider the developer team’s expertise, ethical use, and performance metrics, while also identifying any legal risks or potential litigation to safeguard against future challenges.

Vendor evaluation includes a deep dive into warranties, performance assurances, and data usage. Given that AI’s core is data, it’s paramount to understand what data the vendor has utilized to train their models, and their data access levels post-implementation. A pilot period or testing AI models in a controlled, limited environment prior to full-scale integration may mitigate risks, validate performance, assess compatibility, facilitate user acceptance testing and ensure regulatory compliance.

For an A.I product that arises out of a Merger or Acquisition, for instance, should SimbaTech consider acquiring a company with AI assets, a meticulous due diligence process is non-negotiable. This includes assessing the value of the AI models, understanding the data they’ve been trained on, potential liabilities, and integrating these considerations into the merger agreements.

In-house Development

AI blurs traditional intellectual property boundaries. For homegrown solutions, protection against infringement becomes critical. When leveraging third-party or open-source tools, understanding licensing nuances ensures legal compliance. Regular audits can preempt potential disputes.

If SimbaTech takes the path of in-house development, safeguarding intellectual property becomes pivotal. This encompasses copyrights, potential patents, ownership rights and most importantly, protecting AI-driven trade secrets. They must foster innovation-friendly environments and lay down clear guidelines for employees.

Consider the risks and liabilities associated with data training and model refinement.  Recognize the ethical and legal implications of data sources used. In-house models, though granting better control, also mean complete responsibility.

Joint Ventures

A collaborative approach can speed up AI integration, but it demands clear contractual delineations, especially concerning data ownership, sharing, access rights, and mutual responsibilities. In case of collaborations, ensure clarity on IP ownership, rights, and protections.

Data Governance, Compliance & Consumer Protection

Data fuels AI. Hence, how businesses collect, store, and process data directly affects AI’s efficiency and legality. Ensure SimbaTech’s compliance, not just in data collection and storage, but also in AI-driven data processing. The business must understand both local data protection regulations and international standards, especially when dealing with cross-border data transfers, and maintain adherence to data protection principles and well-known A.I principles. Consider that you may have to notify regulators in some instances. For instance, in some cases, you may need to file a DPIA with the data protection regulator. A robust cybersecurity framework, addressing potential data breaches, is a must-have in this data-centric era. Map out and clean up the data used to train the models. Mitigate risks associated with third-party data access.

As AI enhances customer interactions, be it through chatbots or personalized marketing and other modes of automated processing, establishing clear protocols for consumer grievances related to AI decisions is crucial. Also, identifying potential risks associated with breach of consumer protection regulations is important.

Board members, employees, partners, and even customers might need proper briefing of the product. Consider reviewing the consumer product terms and conditions and privacy policies.

Risk assessment and Data Protection Impact Assessment (DPIA)

Every business process that AI touches has inherent risks. High-risk processes, especially those involving sensitive personal data, may demand a DPIA. Identify high-risk processes unsuitable for full AI autonomy and evaluate where human oversight remains indispensable, despite AI intervention, and conduct a DPIA before AI integration. This will demonstrate that you have assessed the risks posed to the data subject and addressed the same before adoption.

Policy Reframing 

AI’s integration and use will invariably affect job roles. Training programs, clear usage guidelines, and proactive communication are essential. Revising organizational policies becomes inevitable.

Consider crafting organization-specific AI ethics guidelines. Also consider developing a well-defined policy that governs employee use of external AI tools, to enhance productivity, that are not necessarily provided or sanctioned by the organization. This prevents unforeseen liabilities.

Consider which existing internal policies need revision. Some may include:

• Data Privacy and Protection Policy- Catering to AI data processing needs.
• IT and Cybersecurity Policy- Address AI’s unique security vulnerabilities.
• Employee and HR Policies-Cover AI training, role shifts, and tool usage.
• Ethics and Conduct Policy- Address potential AI biases.
• Innovation and IP Policy- Addressing internally developed AI.
• Vendor Engagement Policy-Given third-party AI integrations.
• Customer Engagement Policy- Adjusting for AI-enhanced customer interactions.
• Risk Management Policy- Factor in AI-specific risks.
• Product Development and Quality Assurance Policy- guide integration of AI into product development.
• Research and Development Policy- include specific AI Research & Development practices.
• Marketing and Advertising Policy- to avoid breaches of laws and standards.
• Compliance and Regulatory Policy-prevents legal penalties and ensures ethical operations.
• Business Continuity and Disaster Recovery Plan- robust framework for AI specific disruption.

Dispute Resolution & Contractual Safeguards

In an AI-driven business environment, the nature of disputes can shift and take on a new complexion, often characterized by their complexity and the technical nuances of AI systems. They can range from model inaccuracy to data breaches. Dispute resolution mechanisms must evolve to address these unique challenges. SimbaTech must ensure that its contracts with AI vendors, partners, and customers include robust safeguards and articulate clear processes for resolving any disputes that arise. Some Key considerations include:

Specificity in AI Contracts

Contracts should clearly define the expectations and standards for AI performance, accuracy, and reliability. This includes setting out service level agreements (SLAs) that are specific to AI outputs and establishing benchmarks against which the AI system’s performance can be measured.

Jurisdiction and Applicable Law

Due to the cross-border nature of AI technologies, it’s vital to establish which legal frameworks and jurisdictions will govern the contract. The contractual terms should address international and local laws that may impact the enforcement of agreement provisions, especially in the diverse legal landscapes of African nations.

Escalation and Alternative Dispute Resolution (ADR)

Clearly outlined escalation procedures for disputes must be put in place, allowing for step-wise resolution efforts starting from negotiation, moving to mediation, and finally to arbitration or litigation if necessary.

ADR methods such as arbitration and mediation should be tailored to AI disputes, with provisions for expert determination where technical matters are central to the dispute. This ensures that those resolving the disputes have the necessary expertise in AI.

Data Breaches and Liability

AI-related contracts must clearly stipulate the protocols for data breaches, including immediate response actions, notification procedures, and liability for losses. Liability clauses must also consider scenarios where AI behavior diverges unpredictably from its intended use.

Intellectual Property (IP) Disputes

Provisions must be included to handle disputes over IP rights associated with AI technologies, including ownership of the AI model, its outputs, and the data used for its training.

Model Inaccuracy and Performance Issues

The contract should set forth remedies for scenarios where AI models do not perform as expected or promised, including thresholds for inaccuracy that may trigger contractual remedies.

Enforceability and Execution

To avoid enforceability issues, contracts should include clauses that specify how obligations will be executed in the context of AI, such as the use of digital signatures and electronic records, which are recognized under various national laws.

Force Majeure

AI systems can be disrupted by unforeseen events, including those that might not traditionally fall under force majeure clauses. Contracts should consider including AI-specific force majeure events, such as critical data center failures or loss of data connectivity.

Termination, Data Destruction, and Continuity

In the event of contract termination or a dispute that results in discontinuation of AI solutions, there must be provisions for the orderly transition of services and data to prevent business disruption. This foresight is critical in preventing data breaches, loss of intellectual property, and service disruption in the sensitive phase of contract termination.

In this context, termination of contracts carries specific implications, especially concerning data handling and the continuity of services. Given the data-driven nature of AI systems, the process of data destruction and deletion upon termination, scope of destruction, method of destruction, certification of data destruction, and handling of derived insights should be catered for. Transition of services must also be carefully managed and explicitly outlined in the contract. These include transition of data models, retrieval of company data, business continuity assurance, responsibilities of the parties, and final settlements.

Incident Response Mechanism

In the intricate world of AI, incidents don’t merely entail system downtimes or minor glitches. They can span from algorithmic biases producing discriminatory outcomes to unauthorized data accesses that compromise vast amounts of sensitive data.

A proactive incident response mechanism not only mitigates potential reputational and financial damages but also minimizes legal repercussions. For SimbaTech, this would involve creating a dedicated AI oversight team, regular vulnerability assessments, and simulated incident drills.

The immediacy with which an incident is detected and responded to can make the difference between a minor operational hiccup and a full-blown organizational crisis. AI, with its capacity for rapid, vast-scale operations, can exacerbate incidents at unprecedented speeds. A swift, coordinated, and effective response ensures continuity and trust in the system.

Data Breach Procedures

While AI can bolster cybersecurity measures, it also introduces new vulnerabilities. Advanced AI tools can be employed to detect anomalies indicative of breaches. However, upon detection, swift notification protocols, compliant with regulations, must be triggered. This involves notifying not only regulatory authorities but also potentially affected individuals.

Understanding the ‘how’ and ‘why’ of a breach is pivotal. It aids in immediate containment and future prevention. AI can both be a tool and a target here. If an AI model is compromised, understanding the nature of the breach can be more complex, given the often ‘black-box’ nature of AI algorithms.

Implement restorative actions which involve restoring and validating system integrity, ensuring that no backdoors have been left open. In the AI context, this may also mean retraining models if they have been tampered with.

Data Subject Rights

Fulfilling data subject rights can be technically challenging with AI models, as they often operate on complex algorithms and massive data sets. Ensuring the capability to pinpoint and extract specific data subjects’ information can require sophisticated data management strategies. Incorporating data subject rights into the design of AI systems is crucial. In other words, the organization should adopt privacy by design.

The right to explanation, a cornerstone of data subject rights, becomes especially relevant in the AI world. Transparency is also a fundamental data protection principle. If SimbaTech’s AI models make decisions affecting individuals, they might be obligated to explain these decisions. This is challenging because AI decision-making processes can be complex and not always easily interpretable.

If a data subject exercises their ‘right to be forgotten’, this could have ramifications on AI models. The model might need to be retrained without this individual’s data, especially if it has a significant impact on the model’s decisions.

If SimbaTech uses AI for automated decisions, provisions must be in place for human intervention or review, especially for decisions that have legal or similarly significant implications for data subjects.

A data subject’s right to access information must be understood in the context of exactly what extent of information the data subject is entitled to access and how it will be retrieved and provided to the data subject, upon request.

Where data subjects have the right to receive their personal data in a structured, commonly used, and machine-readable format as well as the right to transmit that data to another controller, AI systems must be capable of exporting data in such formats.

Where data subjects have the right to have inaccurate personal data rectified, AI models must have the capability to do so.

Beyond Implementation

Continuous monitoring of effectiveness, ethical considerations, and compliance is essential. In the dynamic world of AI’s business integration, legal counsels, both internal and external, stand at the forefront of ensuring compliant and ethical practices. The role has shifted from being purely supportive to a strategic necessity. As we navigate this evolving landscape, collaboration between in-house legal counsel such as yourself and external experts, like Tripleoklaw, becomes paramount. Proactivity, foresight, and alignment with business goals are essential in this transformative era.

We wish you all the best as you champion your organizations through this dynamic terrain. May you always be the strategic force that steers the ship with foresight and integrity. Should you wish to delve deeper, discuss, or collaborate, please feel free to reach out to us via ckariuki@tripleoklaw.com. Together, we’ll ensure that the integration of AI in business is not just technologically sound but legally robust and ethically commendable.