Last month, all 193 United Nations member states gathered in Geneva for the inaugural Global Dialogue on AI Governance. For two days they debated how the world should govern AI, yet paid little attention to one of AI’s most consequential governance challenges: its impact on elections. The omission risks undermining progress on every other aspect of the AI debate: safety, trustworthiness, capacity building, societal implications, and human rights.
Elections are not simply another sector affected by AI; they are the main mechanism that makes every other democratic right enforceable. Yet when we think about AI and elections, many of us think about disinformation. The preliminary report from the U.N.’s Independent International Scientific Panel on AI — intended to inform the Dialogue — does the same, mentioning AI in elections only in the context of information integrity and deepfakes targeting candidates.
But one of the clearest lessons of 2024’s “year of elections” was that the impact of deepfakes was significantly overstated. This framing mistakes the loudest risk for the largest one. The more important question is not what happens when AI generates a fake video of a candidate, but what happens when it is embedded in the systems that administer elections themselves — in procurement decisions, biometric voter-verification systems, and data-sharing arrangements. That is where AI’s impact on democracy may prove most profound.
Consider India’s record. In 2015, the country’s election commission launched a program linking voters’ photo ID records to Aadhaar, the biometric national identity database. Aadhaar is run by the Unique Identification Authority of India, a statutory body under executive control, not an independent institution like the election commission. It trained a machine learning algorithm to cross-reference the two databases and flag duplicate and dead voters for deletion. Electoral data, meanwhile, flowed into a state-run data system, giving government authorities direct access to records the election commission alone was constitutionally meant to hold.
The results were staggering: Roughly 5.5 million voters were deleted from rolls in two states, many without any notice or path to reinstatement, before India’s Supreme Court halted the program. Right to Information disclosures later revealed that the matching algorithm’s failure rate was as high as 93%. Despite this, Aadhaar-voter linkage was revived nationally in 2021, bringing the same structural risk to all of India, and leaving a clearly flawed machine learning model to play a major role in deciding who retains the right to vote.
There is a deeper concern beyond any single algorithmic failure: the gradual transfer of electoral authority away from public institutions and into systems they do not fully control. Across much of Africa, Asia, and Latin America, election technologies are designed, supplied, and hosted by private vendors that operate beyond the reach of national electoral oversight.
Voter registers, biometric devices, identity databases, results-management software, and cloud infrastructure increasingly incorporate AI-enabled features, and voters are turning to chatbots for advice and information before going to the polls. This creates serious risks. Sensitive voter data may leave national jurisdictions, proprietary systems may be impossible to audit, and hidden algorithmic errors may exclude thousands of voters without ever being challenged, or even detected.
Chatbots such as OpenAI’s ChatGPT and Google’s Gemini, now fully incorporated into the search function, may hallucinate incorrect information, or reinforce bias, particularly in contexts where digital information is limited and linguistic diversity is high. This exacerbates existing inequities by providing poor-quality information to communities that are already least represented online.
Opacity and complexity
These risks are not unique to AI, but AI’s opacity and complexity — as well as the scale at which the technology operates — compound them exponentially. Absent clear governance standards, election authorities may find themselves accountable for decisions embedded in technologies they did not design, cannot fully inspect, and often lack the authority to regulate.
It is only by foregrounding elections in global AI governance discussions that governments, civil society, and the private sector can help ensure that AI does not undermine the institutional foundation of democracy. Key questions must be asked: Who controls electoral data? Who can audit these systems? And who is accountable when a machine learning model affects a citizen’s right to vote?
We can learn from the approach adopted by African institutions. In April, the African Union’s Peace and Security Council said that Africa must “shape and control” its AI ecosystem because a corrupted voter register is not only a rights violation, it is a trigger for conflict. The Council mandated the creation of the African Union Advisory Group on AI in peace, security, and governance to develop guidance for electoral commissions on the AI systems they procure. It deemed data localization, technology transfer, and disclosure of source code as nonnegotiable conditions. The AU’s approach does two things the U.N. Global Dialogue has thus far failed to do: It foregrounds elections in AI governance, and anchors that governance in regional realities. It is a regional model worth replicating on the global stage.
While concern about AI’s impact on health, food security, climate, and other sectors is growing, attention to AI governance in elections is equally necessary. Without the ability to elect or remove a government, citizens lose the principal peaceful means of holding power to account when leaders mismanage a drought, misallocate healthcare spending, or deny them their rights. By leaving elections out of the AI governance agenda, the Global Dialogue effectively removed the accountability mechanism for AI applications in every sector.
Electoral integrity deserves a dedicated track when the Global Dialogue reconvenes in New York in May 2027, with the same institutional weight given to safety, human rights, and the digital divide. Until elections are recognized as central to AI governance, global efforts to make AI safe, trustworthy, and rights-respecting will rest on a dangerous illusion — that democracy can be protected without protecting the institutions that make democracy possible.
Facts Only
* 193 United Nations member states gathered in Geneva for the Global Dialogue on AI Governance.
* The preliminary report from the U.N.’s Independent International Scientific Panel on AI mentioned AI in elections only regarding information integrity and deepfakes targeting candidates.
* The impact of deepfakes was found to be significantly overstated in 2024.
* The most profound impact of AI on democracy may lie in its embedding within electoral systems, such as procurement decisions, biometric voter-verification, and data-sharing arrangements.
* In India, linking voter photo ID records to Aadhaar (a biometric database) resulted in the deletion of approximately 5.5 million voters in two states due to a machine learning algorithm failure rate of 93%.
* The Aadhaar-voter linkage was revived nationally in 2021.
* Electoral technologies across Africa, Asia, and Latin America are often supplied by private vendors operating beyond national oversight.
* Voter registers, biometric devices, identity databases, results-management software, and cloud infrastructure increasingly incorporate AI features.
* Chatbots like ChatGPT and Gemini may hallucinate information or reinforce bias, potentially exacerbating inequities in areas with limited digital resources.
* Opacity and complexity of AI compound risks when no clear governance standards exist.
* The African Union mandated the creation of a group to develop guidance for electoral commissions on procured AI systems, requiring data localization and source code disclosure.
Executive Summary
The inaugural Global Dialogue on AI Governance in Geneva included 193 UN member states debating the regulation of artificial intelligence, though elections were largely omitted from the focus. The preliminary report from the U.N.’s Independent International Scientific Panel on AI mentioned AI in elections only regarding information integrity and deepfakes targeting candidates. A key finding from 2024 demonstrated that the impact of deepfakes was overstated, suggesting the larger risk lies in AI's integration into election administration systems, such as procurement, biometric verification, and data-sharing arrangements.
The analysis highlights risks related to historical precedents, exemplified by India’s experience with linking voter IDs to the Aadhaar biometric database, which resulted in significant deletion of voter rolls due to a flawed machine learning algorithm, despite subsequent challenges by the Supreme Court. A broader concern is the transfer of electoral authority from public institutions to private systems that lack oversight, particularly across Africa, Asia, and Latin America, where election technologies are provided by unobservable private vendors. Furthermore, the use of tools like chatbots risks exacerbating existing inequities by distributing poor-quality or biased information to underserved communities.
The text argues that the opacity and complexity of AI compounds these risks. Without clear governance standards, authorities risk accountability gaps when decisions are embedded in opaque systems. The piece advocates for foregrounding elections in global AI governance discussions to establish accountability mechanisms regarding who controls electoral data, system auditing, and accountability for algorithmic outcomes. A regional model from the African Union, which mandates control over AI ecosystems related to elections, is presented as a potential global framework.
Full Take
The narrative pivots from a specific technological risk (deepfakes) to an institutional risk (electoral integrity) by shifting focus to systemic control. The core tension is between the diffusion of AI technology and the maintenance of democratic accountability. The most salient pattern involves the systemic erosion of public control: the mechanism for holding power accountable—elections—is being subjected to opaque, externally controlled technological layers.
The argument about India's Aadhaar linkage functions as a powerful case study demonstrating how algorithmic failures, when tied to data infrastructure managed outside institutional checks (executive bodies vs. election commissions), translate into massive rights violations. This suggests that the failure point is not just the algorithm itself, but the structural placement of decision-making authority.
The call for foregrounding elections in global AI governance recognizes that safety and trustworthiness are insufficient if they do not address fundamental democratic structures. The AU's regional approach demonstrates a pattern where solutions to governance must be deeply context-specific; global frameworks must accommodate local realities regarding data sovereignty and institutional control, moving beyond generalized risk assessment to concrete mandates on ownership and auditability of electoral systems. The implicit assumption is that the failure of governance in one domain (elections) guarantees a failure in all others (safety, rights). The challenge for global governance is bridging the gap between high-level ethical debates and the granular, institutional realities of data control across diverse political systems.
Bridge Questions: If regional models successfully mandate control over procured technologies, what mechanisms exist to ensure adherence and enforcement on an international scale? How can global bodies shift from discussing AI risks in isolation to integrating electoral accountability as a primary lens for technological risk assessment? What is the specific institutional architecture required for external oversight of electoral data across sovereign borders?
Sentinel — Human
This text demonstrates strong, focused argumentation built around specific historical and institutional examples, suggesting human authorship rather than purely synthetic generation.