Introduction

Generative AI in democracy evaluation is both an opportunity and a governance challenge. The PrEval policy brief by Quito Tsui and Linda Raftree of The MERL Tech Initiative argues that GenAI tools can support evaluation work, but may also destabilize democratic systems, amplify bias, concentrate information power, and reshape what evaluators consider measurable or meaningful.

The brief uses a dual-focused approach: it examines how AI interacts with democratic systems and how AI changes the practice of evaluating democracy-focused programming. It highlights possible applications such as sentiment analysis, demoscraping, signal monitoring, digital trace analysis, transcription, translation, data visualization, querying, evaluation synthesis, and summarization.

For evaluators, the case is important because it cautions against treating AI as a neutral efficiency tool. AI can support evaluation processes, but it can also affect evaluation outputs, democratic participation, public accountability, privacy, labour conditions, environmental sustainability, and the balance of power between citizens, governments, and technology companies.

Case Background

The PrEval brief responds to the rapid mainstreaming of GenAI tools and their implications for democracy-focused programming and evaluation. It notes that contradictory claims now sit side by side: AI may destabilize democratic foundations, yet it may also improve evaluation capacity and increase access to democratic processes.

The brief argues that governance, policymaking, and democracy programming practitioners need a nuanced understanding of AI capabilities and limitations. It presents AI not only as a tool evaluators may use, but also as a force that can shape democratic institutions, information integrity, participation, accountability, and public trust.

The case therefore sits at the intersection of two questions: how can AI help evaluators work more effectively, and how might AI itself change the democratic systems evaluators are trying to understand?

The Evaluation Problem

Democracy evaluation requires attention to context, voice, inclusion, power, institutions, and participation. The use of AI may appear attractive because it can summarize large volumes of text, translate documents, identify patterns, monitor signals, or support synthesis. Yet democracy evaluation is especially sensitive to bias, representation, accountability, and the risk of turning complex social and political realities into simplified technical metrics.

The main evaluation problem is therefore not simply whether AI works. The deeper question is whether AI use strengthens democratic values or quietly undermines them through opaque models, biased datasets, extractive data practices, corporate control, exclusion of marginalized voices, or overreliance on what can be easily measured.

AI Applications in Democracy Evaluation

The brief organizes AI tools into two broad categories: tools with specific utility for democracy evaluation and tools with broader utility for evaluation practice.

Risks and Blind Spots

1. Unreliability and validity challenges

AI tools can produce inaccurate outputs, false information, fabricated citations, or fictional quotes. In evaluation, these risks directly affect trustworthiness and can undermine the credibility of findings.

2. Bias across the AI lifecycle

Bias can appear in data collection, labelling, model training, and deployment. Sampling bias, selection bias, and exclusion bias can shape what models learn and whose experiences are represented.

3. Information integrity and journalism risks

The brief highlights concerns that algorithmically mediated news and extractive model training practices can undermine journalism, intellectual property, copyright, and information integrity, all of which matter for democratic accountability.

4. Uneven governance and geopolitical power

Global Majority countries and communities are often sidelined in AI governance despite being likely to experience disproportionate harms. This exclusion can deepen existing inequalities in AI development and deployment.

5. Labour, environmental, and privacy harms

The brief urges evaluators to consider wider harms, including data labelling labour conditions, energy and water consumption by data centres, environmentally destructive resource extraction, and privacy risks.

6. Big Tech concentration of power

The concentration of information power among large technology firms can conflict with democratic ownership, access to information, public oversight, and citizen interests.

Ethical Considerations

The brief identifies ethical concerns that should prompt decision-makers to pause before permitting broad or uncritical AI use in democracy evaluation. These include the risk that AI mediation creates distance between elected officials and citizens, filters out less common viewpoints, magnifies discriminatory practices, and operates behind closed doors without meaningful public oversight.

It also warns against the technocratization of democracy evaluation. If AI pushes evaluators toward what is easiest to count, it may encourage a narrow understanding of democracy that ignores cultural, social, historical, and relational dimensions of democratic life.

This means AI can change not only how evaluators work, but also what evaluation notices, values, and reports.

Recommendations

The brief recommends that policymakers and evaluators take a strategic, evidence-informed approach to AI in democracy evaluation. It calls for low-risk uses, capacity building, grassroots approaches to AI design, and alternatives to corporate-dominated AI systems.

• Develop a robust, evidence-informed approach to exploring AI tools in democracy evaluation.
• Focus on low-risk, discrete uses that do not rely on personally identifying information or expose individuals to harmful automated decisions.
• Build capacity across funders, evaluators, policymakers, academics, and democracy organizations.
• Support grassroots and community-led AI approaches that address inclusion, representation, and data sovereignty.
• Explore nonprofit or public-sector AI alternatives to address corporate dominance.
• Challenge pressure to use AI when there is no clear need or when safer alternatives exist.
• Be transparent about AI risks and honest about whether those risks can actually be mitigated.

Evaluation Framework for AI Use in Democracy Programming

EvalCommunity Academy users can adapt the following framework when deciding whether and how to use AI in democracy-focused evaluations.

Practical Lessons for M&E and Democracy Professionals

First, evaluate AI before using AI for evaluation. Democracy evaluators should assess whether a tool aligns with democratic principles before applying it to democracy programming.

Second, start with low-risk uses. Data cleaning, translation, synthesis, or exploratory analysis may be appropriate if personally identifying information is excluded and human review is strong.

Third, protect democratic complexity. Evaluators should avoid allowing AI to narrow democracy to what can be counted, scraped, or summarized easily.

Fourth, build capacity across the ecosystem. Funders, evaluators, policymakers, academics, and democracy organizations all need enough AI literacy to make informed decisions.

Finally, support alternatives. Community-led, grassroots, nonprofit, and public-sector AI initiatives can help counter extractive data practices and corporate dominance.

FAQ

Conclusion

This case study shows that AI in democracy evaluation is not simply a technical issue. It is a democratic governance issue. AI can help evaluators process data, synthesize evidence, and manage complexity, but it can also shape which voices are heard, whose knowledge counts, and how democratic performance is understood.

For EvalCommunity Academy users, the practical lesson is clear: do not begin with the tool. Begin with democratic principles, evaluation standards, stakeholder accountability, risk assessment, and a clear explanation of why AI is needed. Responsible AI in democracy evaluation requires strategic restraint as much as innovation.