Country Profile – South Africa

While AI adoption by the Independent Electoral Commission (IEC) of South Africa is in its early stages, the IEC is already establishing standard protocols for designing, developing and evaluating AI-driven solutions. The IEC’s methodology ensures that new tools are implemented in response to substantive challenges and backed by robust financial justification, rather than being solely driven by novelty or the promise of innovation. To facilitate this needs-based approach, all new AI tools are assessed within a framework that considers practical, legal, financial and ethical implications. 

Within this framework, AI systems must undergo comprehensive proof-of-concept validation through rigorous pilot testing in controlled environments prior to broad implementation. These evaluations, combined with the expertise of IEC personnel and relevant regulatory frameworks, inform the development of AI policy and institutional understanding. The IEC emphasizes that while AI cannot be ignored, its adoption should be approached with circumspection and governed by regulations that uphold democratic values.

How is AI used to improve electoral management in South Africa?

The IEC has already begun integrating AI-supported tools in selected management processes. These early applications primarily leverage AI functionalities embedded within existing software licenses that are used for various other operations, allowing the Commission to capitalize on immediately available resources. In doing so, the IEC secures short-term productivity gains while, in parallel, strategically planning long-term, coordinated investments into scalable AI projects.

The three main examples of AI currently in use at the IEC are:

• Voter identity verification: A key example of leveraging established services at the IEC is the integration of Microsoft’s Azure Vision. This technology has been utilized in a production environment since August 2025 for the purpose of image analysis of identity documents submitted via the IEC’s online registration portal. The software enables the Commission to swiftly verify the identity of applicants through an automated process, with over 200,000 IDs processed since the introduction of the tool.
• As productivity tools: The IEC is also employing an institution-wide Microsoft Copilot license to automate everyday operations, such as note-taking, summarizing information, and editing.
   

Areas where AI tools are currently under consideration

The IEC is actively developing several additional applications of AI across different facets of its operations. The projects under construction broadly serve three purposes: (1) streamlining time-consuming processes to expedite and reinforce the security of public service delivery; (2) improving voter access to accurate, verified electoral information; (3) safeguarding the integrity of that electoral information.

The leading AI-based initiatives that are currently being designed, developed or piloted at the IEC are:

• Party registration: The IEC are currently developing an AI image analysis tool similar to the one used for voter identification to support the online registration of political parties. In South Africa, all parties seeking to register with the IEC require a logotype and a unique party-name abbreviation or acronym. With over 500 registered political parties in the country, the manual processing of party registration is highly labor-intensive. By automating this process using AI image analysis and intelligent character recognition (ICR), the IEC can efficiently compare details across all registered parties to flag when applications do not comply with requirements of distinctiveness.
• Threat detection: The IEC currently engages an external service provider to monitor the election information environment, manage its ‘digital attack surface’ (i.e. all its points of access that may be vulnerable to attack) and protect the integrity of its online presence. This service consists largely of scouring the web to take down impersonation attempts that may mislead voters, ensuring that staff credentials are not compromised and monitoring the impact of mis- and disinformation. While continuing to collaborate with a third-party provider, the IEC is exploring how threat detection systems may be enhanced by AI. Here, machine-learning classifiers can be trained to detect linguistic patterns and branding misuse in text and images, supporting the taking down of websites that attempt to impersonate the IEC through ‘typosquatting’ (i.e. websites whose URLs exploit common typographical errors) or domain registration pattern anomalies (Koide et al. 2023). Natural language processing (NLP) models can also support real-time social media monitoring to mitigate the spread of misinformation, as well as safeguard staff credentials through AI-based phishing detection and tracking any leaking of credentials on the dark web (Sharma & Singh 2024; Ahmad et al. 2025). During the 2026 municipal elections, the IEC faced significant issues relating to the use of generative AI to microtarget segments of the voter base in “hyper-local” contexts (SA News 2026). NLP models are therefore being considered for supporting the IEC’s social media monitoring, by locating patterns in hyper-specific content that may otherwise go unnoticed, strengthening the Commission’s ability to isolate and respond to threats to electoral integrity.
• Chatbot: In order to expedite responses to the less complex type of voter inquiry, the IEC is developing a human-services chatbot that is scheduled for release in April 2026. The model is intended to provide verified, easily accessible answers to straightforward questions on topics such as voter registration status or polling station opening hours. To avert the risk of improper outputs, the IEC is restricting the model to relay information only from IEC data, including public statements, regulations, permitted documents and South African election law.
• Voter roll management/verification: The IEC is constructing a machine-learning-based system to standardize the validation of voter roll data. South Africa’s voter rolls contain addresses for more than 27 million registered voters, and the quality and consistency of these data are critical for the correct assignment of voters to polling stations. Through machine learning, the IEC is training a model to recognize the formatting of a valid data unit, allowing the system to automatically detect incomplete, non-conforming or duplicate entries and flag them for human review to support with the cleaning of voter rolls.
• Real-time results transmission: Election results in South Africa are captured in two databases—a primary database that stores results, and a secondary database from which results reports are drawn. To transfer results between the databases, the IEC currently uses a batched extract transform load (ETL) process, meaning that results are transmitted in set time intervals. The IEC is exploring how it can utilize Microsoft 'Fabric’ Data Architecture to replace batch processing with real-time transmission (RTT) to reduce result latency. The Fabric service also includes AI-powered data analytic tools, which the IEC intends to use for results analysis and anomaly detection.
• Intelligent character recognition (ICR) for vote tabulation/result compilation: In a similar way to the IEC’s use of AI for voter identification and party registration, the Commission is exploring how its existing Microsoft licensing framework can be utilized for intelligent character recognition (ICR) in preliminary vote tabulation. For this application, AI tools are used to scan the manual result form that is filled out at polling stations, converting handwriting into text. This expedites the process for capturing results by allowing polling station workers to scan forms, review the ICR conversion and submit the documents more efficiently. The system is currently undergoing rigorous testing with the use of previous election data to ensure that the system can reliably recognize handwritten characters and will not be deployed for the Local Government Elections in 2026/2027.
• e-Learning Platform: The IEC are planning to use AI for the creation of internal staff training material. Specifically, they intend to use generative models to produce knowledge resources alongside a dedicated agentic AI that helps answer questions relating to the course content.
   

Symbiotic workflow for introducing AI solutions 

All innovative election technologies, including any AI-related tools, are introduced through a symbiotic relationship between end-user practitioners and the Commission’s IT department. In practice, this means that initial ideas can surface either when practitioners in non-IT departments encounter issues in their work that they believe could benefit from automation, or when IT staff initiate dialogues with departments that would ultimately implement the technology. 

Once a process is identified, the IEC has a standardized workflow for realizing potential technologies, beginning with a project plan and a budget estimate for the tool. IT staff members work in tandem with business analysts to assess whether the problem is best addressed by an internally developed system or by an externally provided service. Following the initial assessment, the Commission’s IT staff develop a proof of concept, clearly articulating the tool’s intended purpose and demonstrating the added value of designing or acquiring a new technical solution. When evaluating potential use-cases, the IEC is careful to assess the substantive merits of the technology. AI is not pursued as a novelty; it is implemented only when it addresses a defined challenge in a cost-efficient manner.

Once a proof of concept is developed, the tool undergoes a piloting process within a controlled testing environment to determine potential risks and limitations, as well as to evaluate performance. Finally, if the trial evaluation result is positive, the technology is formally approved at the level of the deputy CEO and officially integrated into the IEC’s operations. This standardized process for introducing new technologies is intended to link legitimate operational needs with viable technical solutions that comply with standardized metrics, within a manageable budget frame.

Bulwark against privacy risks and ethical concerns

The IEC underscores transparency as a necessary component when using AI for any public-facing applications. In practice, this means that users must be informed when they are interacting with an AI-powered system, such as a chatbot, and that they must be offered an alternative if they prefer to engage with a human representative. In the IEC’s experience, some voters are inherently skeptical of the use of emerging technologies in electoral management, citing concerns over system integrity and reliability. Therefore, the Commission considers it paramount to communicate to voters why AI is being used and how it may make service delivery more democratic, including as an additional tool for validating information, supporting research, relaying information from IEC data and freeing up the capacity of IEC representatives to focus on more complex operations. 

However, it is equally important to maintain human-led procedures that verify system output, intervene in case of system malfunction or respond to the needs of voters who may distrust AI systems. In addition, the IEC recognizes that AI systems are often trained on data containing systemic biases that may risk exacerbating patterns of discrimination. Particularly when used for electoral management, AI may reproduce the marginalization of voters from rural communities, as well as persons living with disabilities and those people from communities historically underserved by digital public services because of factors such as linguistic minoritization. The IEC is adopting a series of proactive measures to mitigate the potential harm of these biases, targeting three core links in the system-user chain:

• The pre-identification of risks and limitations through system trials: By adopting standard operating procedures (SOPs) for concept design and risk assessments, as well as conducting pilots in controlled environments, the IEC can identify systematic ethical issues before they risk impacting voters. Each case of AI adoption at the IEC is accompanied by an approved project plan, which incorporates a flow chart of all potential risks, the ways in which they are mitigated and the ways in which the technology supports operations.
• Voter education: To ensure that users are aware of how to interact critically with AI systems, their purpose and ways of assessing their outputs, the IEC intends to pair the rollout of new voter-facing systems with a public education campaign. This campaign will explain the purpose of the technology, clarify how it can be accessed and openly address the potential for bias, while outlining the safeguards that the IEC has put in place to mitigate these risks. The aim is to ensure public trust and awareness of how potential biases may compromise the reliability of systems, encouraging users to seek recourse from a human representative if the AI malfunctions or produces questionable outputs.
• Staff handbook: On the other side of the end-user spectrum, the IEC is in the process of forging a staff literacy training program on the use of AI. This involves the introduction of an institution-wide handbook on the authorized and ethical use of AI, which is intended to bring all employees into alignment regarding the standards and boundaries governing AI use.