1. Introduction
This article explores the integration of Artificial Intelligence (AI) in e-commerce, focusing on its rapid adoption in Nigeria. AI refers to the ability of computer systems or algorithms to perform tasks that typically require human intelligence, such as reasoning, learning, and problem-solving. AI transforms e-commerce with enhanced personalization, operational efficiencies, and fraud prevention. However, its deployment raises significant legal challenges in data protection, liability, and consumer privacy.
AI adoption necessitates rigorous legal standards to protect consumer rights, ensure data security, and maintain fairness in data processing. In Nigeria, the Nigeria Data Protection Act (NDPA) of 2023 addresses some of these issues but leaves gaps in AI accountability and liability. The discussion examines Nigeria's legal responses to AI in e-commerce in protecting consumer privacy, referencing local platforms like Jumia and Konga. It compares these responses with international standards like the GDPR to highlight regulatory gaps and potential improvements.
2. Artificial Intelligence and Its Role in E-Commerce Platforms in Nigeria
AI has transformed global e-commerce, significantly enhancing operational efficiency, optimizing customer experiences, and promoting business growth. In Nigeria, AI is playing a critical role in addressing key market challenges such as fraud prevention, customer trust, and logistical inefficiencies, making it increasingly indispensable to the sector1.
E-commerce giants like Jumia and Konga are utilizing AI-driven technologies to provide personalized product recommendations. They analyze customers' browsing histories, purchase patterns, and demographic data using machine learning algorithms and natural language processing to tailor product suggestions. This personalization not only enhances the user's experience but also increases sales and customer retention2.
Additionally, AI is vital for efficient inventory management, which is particularly important in Nigeria where supply chain challenges are common. AI technologies can predict demand with greater accuracy by analyzing real-time sales data and customer trends. This allows businesses to automate reordering processes, ensuring that popular products are promptly replenished and reducing the likelihood of stockouts or overstocking3.
Furthermore, the integration of AI into fraud detection systems is crucial for protecting both businesses and consumers in Nigeria's expanding e-commerce market. AI systems utilize machine learning models to analyze transaction data in real time, identifying suspicious activities that could indicate fraud. This proactive approach not only enhances security but also builds customer trust, which is vital for the growth of e-commerce4.
AI-powered customer service, particularly through the deployment of chatbots, has revolutionized how businesses interact with their customers. These chatbots handle basic queries such as order tracking and product availability efficiently, allowing human agents to focus on more complex issues. This shift not only reduces operational costs but also improves response times and significantly enhances customer satisfaction5.
Despite these advantages, the adoption of AI in Nigerian e-commerce is not without significant challenges. Ethical considerations, data privacy concerns, and transparency issues in AI decision-making processes pose substantial hurdles. Algorithmic bias, for instance, can result in discriminatory outcomes if the data used to train AI models is fully representative of the population. This can manifest in skewed pricing strategies, product recommendations, or customer service responses, potentially undermining consumer trust and leading to reputational damage6.
Privacy risks are another critical concern, as AI systems that rely heavily on consumer data can breach privacy if not properly managed. Compliance with data protection laws such as the NDPA is essential but challenging for many businesses7. Additionally, the opaque nature of some AI systems can lead to a lack of transparency, particularly in dynamic pricing and fraud detection8.
While AI offers significant benefits for Nigeria's e-commerce platforms, from enhanced operational efficiency to improved customer engagement, the accompanying legal, ethical, and regulatory challenges must be navigated carefully. By addressing issues such as data privacy, algorithmic bias, and transparency, Nigerian e-commerce platforms can harness the full potential of AI while maintaining trust and compliance with legal standards9.
3. Privacy and Ethical Challenges of AI in E-Commerce
Privacy issues in AI-driven e-commerce largely revolve around the acquisition, use, and management of personal data. Daniel J. Solove, a noted scholar in privacy law, has formulated a taxonomy to classify these issues into information processing, dissemination, and invasion10. This classification aids in pinpointing specific privacy concerns within the AI application in e-commerce. AI systems frequently accumulate extensive data from consumers, often without their explicit consent or full awareness, leading to significant surveillance concerns regarding consumer behaviours and preferences. Once this data is amassed, it is processed and analysed to guide decision-making on e-commerce platforms. The opacity of these processes, often referred to as the "black box" phenomenon, obscures the understanding of how data is utilised and the extent to which it impacts outcomes directly affecting consumers. Additionally, the transmission of processed data to third parties for advertising, credit scoring, or other purposes introduces further privacy risks and potential misuse.
In addition to privacy concerns, AI systems manifest ethical challenges that encompass fairness, accountability, and transparency in their development and deployment. AI systems might reinforce or even heighten existing biases if trained on skewed or non-representative datasets, leading to unjust treatment or discrimination against certain groups, thereby violating ethical principles of fairness and justice. Mensah in his work11, reiterates the importance of utilising diverse datasets and applying fairness metrics to avert such discriminatory outcomes.
For AI to be deemed ethical, it must also be transparent, and its decision-making processes should be explicable. This clarity is crucial not only for effective privacy management but also for addressing ethical questions about accountability and the right to explanations. Mensah stresses the pivotal role of Explainable AI (XAI) systems in clarifying AI operations, ensuring that consumers comprehend how decisions, such as product recommendations and pricing, are made12. This necessity aligns with the stipulations of the NDPA, which demands that clear information on data usage be provided. However, realising compliance is challenging due to the complexities inherent in AI technologies.
4. AI-Driven E-Commerce Platforms and the Nigerian Data Protection Act
The NDPA provides a framework for addressing the privacy challenges posed by AI in e-commerce. This act mandates the fair, lawful, and transparent handling of personal data, aiming to protect individuals' rights within AI-driven platforms. The Act also addresses some ethical concerns associated with AI, such as fairness and non-discrimination, by mandating data processing in a manner that respects the rights and freedoms of data subjects13.
One of the core principles of the NDPA is data minimisation, stipulating that data collection should be "adequate, relevant, and limited to what is necessary" for specific purposes14. AI-driven e-commerce platforms often process large volumes of consumer data to fuel their algorithms, creating a potential conflict with this provision. The act's emphasis on limiting data to necessary purposes challenges e-commerce platforms to refine their data collection strategies, ensuring they do not collect more data than required for their intended processing activities.
Under the NDPA15, data controllers are required to inform data subjects prior to the collection of personal data about several key details: the identity and means of communication with the data controller, the lawful basis for processing, the specific purposes of the processing, and the recipients of the personal data. Additionally, data subjects must be informed of their rights under Part VI of the Act, the period for which their data will be retained, their right to lodge a complaint with the Commission, and the use of any automated decision-making processes, including profiling. This latter requirement is vital; data controllers must clearly communicate the significance and the anticipated consequences of such automated processing, empowering data subjects with the right to object to and contest these processes.
Despite these provisions, the NDPA does not explicitly require data controllers to communicate the method involved in AI processing. This omission could significantly challenge the data subjects' understanding of how their data is being processed, particularly given the often opaque nature of AI systems. Such transparency is crucial not only for enabling informed consent but also for fostering trust in digital ecosystems that increasingly rely on complex automated systems.
In essence, while Section 27 of the NDPA lays a groundwork for informing data subjects, it falls short of mandating full disclosure about the mechanisms of AI processing. This deficiency may restrict data subjects' ability to fully grasp and control how their data is processed, posing a barrier to the informed consent that is foundational to data protection.
Article 37 of NDPA16 makes provision for safeguarding data subjects from decisions solely based on automated processing, including profiling, that produce legal or significant effects. However, these protections are limited by exceptions, allowing automated decisions when they are necessary for contractual obligations, legally authorised, or made with the explicit consent of the data subject. In these situations, the Act outlines important protections, mandating that data controllers implement safeguards such as the right to human intervention, the opportunity to express a viewpoint, and the ability to contest such decisions.
While this provision addresses some of the risks associated with AI-driven decisions, it does not fully engage with the more intricate challenges posed by AI technologies. For example, there is ambiguity surrounding the nature of the consent required, particularly in ensuring that data subjects fully understand the implications of consenting to AI-driven decision-making. This is especially problematic given the opacity of many AI systems, whose decision-making processes often remain obscure even to those deploying them. Therefore, although Article 37 introduces essential protections, it requires further refinement to clarify the nature of the consent and the transparency obligations of AI deployers to fully safeguard data subjects in an increasingly AI-driven landscape.
The Act17 mandates Data Privacy Impact Assessments (DPIAs) before processing personal data that poses high risks to individuals. This is especially relevant in AI-driven e-commerce, where vast data harvesting and complex processing can significantly impact user privacy. The DPIA process under the NDPA includes evaluating the processing operations, assessing the necessity and proportionality of these operations, and identifying risks to data subjects along with measures to mitigate these risks. This ensures adherence to privacy by design principles, aiming to pre-emptively address potential privacy issues.
However, while the NDPA establishes a foundation for conducting DPIAs, it needs to be complemented with detailed guidelines as found in frameworks by ICO18 and EDPB19, which offer guidelines on conducting DPIAs, including criteria for identifying high-risk processing. Additionally, DPIAs should be modified to include additional questions that capture the ethical issues associated with AI development and deployment. Finally, enhancing the NDPA with more explicit DPIA guidelines could better equip data controllers to handle the nuanced risks associated with AI, aligning with global standards and improving overall data protection efficacy.
5. Guide to Ensuring Compliance and Accountability for AI
Conducting a comprehensive DPIA and having proper frameworks, documentation and controls in place can aid compliance with not just the NDPA but best practices. Maintaining accountability requires a thorough approach, ensuring transparency, compliance, and privacy protection throughout both development and deployment. Key steps include defining the purpose of the AI, managing ownership and data use, assessing the AI's ethical and operational impact, and establishing ongoing monitoring. The work of Nigel Gooding20 is a very useful resource on how to ensure accountability and conduct DPIA on AI systems. I will attempt to explain some of the guides contained in his book below.
Begin by clearly documenting the goals the AI system is intended to achieve and ensure these align with your objectives, such as enhancing customer experience or improving operational efficiency. Assess whether these objectives are essential and justifiable within the context of personal data processing. It's vital to not only outline how the AI will achieve these goals but also maintain records of both the anticipated and actual outcomes over time, ensuring they remain relevant to the original purpose.
Establish the legal basis for data processing and document how data is collected, ensuring this is done ethically and inclusively. Data Protection by Design should guide the entire process, emphasising data minimisation and compliance with NDPA. Assess the data sources, whether new, existing, or combined datasets and confirm that any integration or repurposing aligns with original consent. For systems bought off the shelf, it is essential to also ask these questions and review the DPIA that was completed by the developer against system operations. Maintain agreements with data suppliers to outline responsibilities and ensure transparency through privacy notices with users about data use and retention. Have a clear retention period, implement secure disposal processes for data no longer needed, and safeguard against both internal and external security threats using measures such as encryption, anonymisation, and access controls.
Clearly outline ownership details for both the AI and the data it uses. Document who owns the training data, identify if the algorithm is hosted by a third-party provider, and assess whether any external parties have data access. Conduct due diligence on any third-party vendors to confirm they have strong data protection practices and clear compliance measures. Define a change management process, with an identified product owner to oversee AI updates and ensure that any system adjustments adhere to data protection standards.
Document the AI model type and ensure it undergoes both performance and ethical evaluations. Conduct thorough testing to validate AI outcomes bearing in mind customer base and demographics, with full documentation explaining the model's predictions and decision-making process. Where possible, consider models that provide high interpretability, especially in high-stakes applications, and ensure robust techniques to prevent overfitting or underfitting.
Set Key Performance Indicators to monitor the AI system's effectiveness, relevance, and security once deployed. Ensure regular reviews to confirm that the AI remains aligned with its intended purpose, with security measures like updated patches and monitoring systems in place to detect issues like model drift or biases. Establish mechanisms to gather user feedback, particularly regarding decisions the AI makes, and engage regularly with consumers to communicate the system's impact. Continuous monitoring is essential, with protocols for updates and a clear process for handling feedback and potential concerns.
Maintain detailed logs and audit trails to track data use, model training, and decision-making. Regularly assess the AI's impact, check for potential risks or biases, and provide mechanisms that allow consumers to appeal AI-driven decisions. Publish transparency reports that detail the AI's performance and any actions taken to address concerns to build consumer trust and demonstrate accountability.
6. Conclusion
NDPA represents a major step in aligning Nigeria's e-commerce sector with global data protection standards, such as the GDPR. By enforcing strict requirements for DPIAs and mandating detailed disclosures to data subjects regarding the use of their data especially in AI-driven processing, the NDPA seeks to enhance transparency and protect consumer rights. However, the legislation could benefit from further refinement to fully address the complexities of AI technology. Clear guidelines on conducting DPIAs and more specific guidance around AI processing methods and AI use would better equip data controllers to manage risks and support truly informed consent from data subjects. Bridging these gaps will be essential for fostering trust and ensuring that the benefits of AI in e-commerce are realized without compromising privacy and ethical standards. Finally, following the recommended guide for compliance above will aid developers and deployers of AI systems in e-commerce to maintain compliance and uphold consumer trust.
Footnotes
1. Mary John A Lefe, 'AI in eCommerce: Explanation, Benefits, and Impacts' (2024) Webretailerhttps://www.webretailer.com accessed [20 October 2024].
2. Ibid
3. Noel Nonso Ozoemena, 'The Future of AI in Marketing in Nigeria' BusinessDay (September 2024) https://www.businessday.ng accessed [20 October 2024]
4. Ibid
5. Artem Dilanyan, 'Artificial Intelligence in Ecommerce: Use Cases, Benefits, Platforms' (2024) EPAM Systemshttps://www.epam.com accessed [20 October 2024].
6. 'AI in Ecommerce: Applications, Benefits, and Challenges' Shopify Blog (June 2023) https://www.shopify.com accessed [20 October 2024].
7. 'Nigeria: Artificial Intelligence (AI) Systems Use in Nigeria: Charting the Course for AI Policy Development' Alliance Law Firm (October 2023)
8. Ibid
9. n 3
10. Daniel J Solove, 'A Taxonomy of Privacy' (2006) 154 University of Pennsylvania Law Review 477-564.
11. G B Mensah, 'Artificial Intelligence and Ethics: A Comprehensive Review of Bias Mitigation, Transparency, and Accountability in AI Systems' (2023) Preprinthttps://doi.org/10.13140/RG.2.2.23381.19685/1 accessed [21 October 2024].
12. Ibid
13. Section 24, Nigerian Data Protection Act
14. Ibid
15. Section 27, Nigerian Data Protection Act
16. Section 37, Nigerian Data Protection Act
17. Section 28, Nigerian Data Protection Act
18. Information Commissioner's Office, 'Data Protection Impact Assessments' (ICO) https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/accountability-and-governance/guide-to-accountability-and-governance/accountability-and-governance/data-protection-impact-assessments/ accessed [25 October 2024].
19. European Data Protection Board, 'Guidelines, Recommendations, Best Practices' (EDPB) https://www.edpb.europa.eu/our-work-tools/general-guidance/guidelines-recommendations-best-practices_en accessed [27 October 2024].
20. Nigel J Gooding, Mastering AI: A Guide for Data Protection Practitioners (1st edn, Nigel J Gooding 2024).
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
