The Promise and the Risk
AI and data-driven tools offer enormous potential to improve public service delivery, from automated eligibility screening that reaches beneficiaries faster to predictive analytics that enable earlier crisis response (Vaithianathan, 2020; Gholami, 2022). These capabilities can help governments serve millions more people with greater accuracy and efficiency than traditional manual processes allow. But without proper safeguards, these same tools can perpetuate existing biases, exclude the most vulnerable populations, and quietly erode the public trust that effective service delivery depends on (Hofmann, 2024; Heikkilä, 2023).
When it comes to the deployment of new AI tools in social protection, labor, health, and education systems, the same data-powered predictive variability that makes these systems so flexible and individually powerful is also what makes the stakes so high. A model or framework, based on flawed or biased data, would not give a false error message, but would proceed on its own, incorrect course, as any human would, denying a family in need food assistance, excluding a person from healthcare, or flagging a worker incorrectly for a penalty. The difference between AI and human error is that AI holds no accountability for decisions. Balancing the need for transparency and the desire for efficient and potentially lifesaving AI tools has become a greater concern today than ever before.
Human-in-the-Loop by Design
Responsible AI in public services requires human oversight at every critical decision point. The outcomes of poor AI implementation in the public sphere are not limited to the typical fiscal, reputational, and legal market consequences of technological misuse or poor implementation; they also extend to severe issues for residents to whom no choice was offered in the context of public services they rely on (Sengupta, 2026). This means designing systems in which automated recommendations are reviewed by trained staff before affecting real people's lives, whether it is determining eligibility for social assistance, flagging cases for intervention, or allocating scarce public resources. The principle is clear: AI should augment human judgment, not replace it.
This is not a technical limitation to be engineered away as models improve. It is a deliberate design choice that reflects the accountability structures of democratic governance. When an AI system recommends denying a benefit, a human official should be able to understand that recommendation, interrogate it, and override it. Without that oversight layer, government institutions cannot be held accountable for outcomes, and accountability, foundational to public trust, is not delivered properly. The auditability of AI decisions must remain paramount and is central to considering any AI system.
On the other side of the matter, we must also not lose sight of our original intention in using AI tools, which is, fundamentally, to improve the effectiveness of our systems and the efficiency of achieving results. Even if we assume human final decisions, the AI can greatly increase efficiency by triaging the number of cases that require high human effort. Design choices must find the most efficiency- and transparency-optimal mixture of human-in-the-loop design fundamentals and powerful AI-driven systems, whether in each individual project, or across public services as a whole. Human-in-the-loop is not only about approving or rejecting AI recommendations; it also involves capturing the reasoning behind both the AI's suggestions and the human decisions, while introducing separate audit mechanisms that do not compromise productivity. Having two complementary layers of information—one from the AI and another from the human decision-maker—productivity improves in the client-facing environment, and with the right review and audit processes in place, you can still achieve the necessary level of quality and accountability. That said, this space will always remain open for research and improvement.
"AI should augment human judgment, not replace it. This is not a technical limitation to be engineered away as models improve. It is a deliberate design choice that reflects the accountability structures of democratic governance."
Building Safeguards Into Every Layer
Responsible AI requires safeguards embedded across the entire delivery chain, from design through deployment to ongoing operation. Implementing this should not be a checklist to complete at the end of a project, but should remain as a core principle shaping every phase of the chain. In practice, this means conducting use-case mapping and risk tiering before any model is built, so that high-stakes decisions receive commensurate human scrutiny.
That requires running bias and fairness assessments against real population data, including disaggregated analysis by gender, age, geography, and other dimensions of vulnerability; meeting transparency and explainability standards so that affected individuals, and the officials who serve them, can understand how decisions are made; applying data minimization and privacy-by-design principles to limit exposure and protect sensitive information; and committing to ongoing performance monitoring across demographic groups after deployment, with clear appeal mechanisms that give individuals a meaningful path to challenge automated decisions that affect them.
These procedures should not be relegated to afterthoughts or compliance exercises. They are part of the core design requirements that determine whether an AI-assisted system genuinely serves its intended purpose. The outcomes of the alternative, from childcare discrimination in the Netherlands to false fraud claims in Denmark, France, and Sweden, have immense consequences for populations who do not get to opt-in to AI systems utilized in public services (EuroAIFund, 2025).
As governments worldwide accelerate AI adoption, the organizations that help them deploy these tools bear a special responsibility. Getting this right is a governance challenge, a capacity challenge, and ultimately a question of values. Technical innovations can hardly address these challenges. Building AI systems that are not only effective but also fair, transparent, and accountable to the people they serve is the standard to which responsible implementers must be held. Anything less risks turning the promise of technology into a new mechanism for exclusion.
References
- EuroAIFund (2025). How AI-Driven Welfare Systems Are Deepening Inequality and Poverty Across Europe. EuroAIFund. Link
- Gholami, S., Knippenberg, E., Campbell, J., Andriantsimba, D., Kamle, A., Parthasarathy, P., … Lavista Ferres, J. (2022). Food security analysis and forecasting: A machine learning case study in southern Malawi. Data & Policy, 4, e33. doi.org/10.1017/dap.2022.25
- Heikkilä, M. (2023, August 7). AI Language Models are Rife with Political Biases. MIT Technology Review. Link
- Hofmann, V., Kalluri, P. R., Jurafsky, D., & King, S. (2024). Dialect prejudice predicts AI decisions about people's character, employability, and criminality. arXiv preprint arXiv:2403.00742.
- Sengupta, P. (2026). What does Responsible AI Actually Mean in a Government Context. VE3. Link
- Tyler, S., Olis, M., Aust, N., Patel, L., Simon, L., Triantafyllidis, C., Patel, V., Lee, D. W., Ginsberg, B., Ahmad, H., & Jacobs, R. J. (2024). Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review. Cureus, 16(5), e59906. doi.org/10.7759/cureus.59906
- Vaithianathan, R., Putnam-Hornstein, E., Chouldechova, A., Benavides-Prado, D., & Berger, R. (2020). Hospital Injury Encounters of Children Identified by a Predictive Risk Model for Screening Child Maltreatment Referrals. JAMA Pediatrics, 174(11). doi.org/10.1001/jamapediatrics.2020.2770
