Addressing ethical concerns and bias when deploying AI in Government services

Project overview

We partnered with a central government department to explore how artificial intelligence (AI) could be used to improve the efficiency and quality of frontline digital services — without compromising fairness, transparency, or accountability. The department was under increasing pressure to modernise its operations and deliver faster, more consistent outcomes for citizens, but also to uphold the highest ethical standards expected of public sector institutions.

The engagement was structured as a full lifecycle project, spanning from discovery through to live service implementation. Over nine months, our multidisciplinary team worked alongside policy leads, service owners, data scientists, and ethics advisers to co-design a responsible AI framework tailored to government use. The scope covered both the technical deployment of AI models within existing systems and the organisational readiness needed to manage them responsibly.

Our overarching aim was to create a model for ethical AI adoption that could be reused across departments — helping the client deliver measurable improvements while maintaining public trust.

Problem

Government services are increasingly exploring automation and AI to manage rising demand and complex decision-making at scale. However, the adoption of such technologies introduces new risks. The client faced growing scrutiny around algorithmic bias, opaque decision processes, and the potential for unintended harm to vulnerable users.

In this case, the department was piloting an AI-based triage system intended to streamline case handling and reduce administrative load. Early internal testing revealed inconsistent results: outcomes appeared to differ between demographic groups, and the rationale behind certain recommendations was unclear to staff. This raised legitimate concerns around fairness and accountability.

At the same time, there was a lack of shared understanding within the organisation about what “ethical AI” meant in practice. Teams were unsure how to assess bias, explain algorithmic decisions, or ensure compliance with the government’s Data Ethics Framework and emerging guidance from the Centre for Data Ethics and Innovation (CDEI).

The stakes were high. Any misstep could have eroded citizen trust, created reputational risk, and potentially led to unequal service delivery. Yet the potential benefits of AI — greater speed, consistency, and capacity — were too significant to ignore. The challenge was to balance innovation with integrity.

Research and Discovery

The discovery phase focused on building a shared understanding of the ethical landscape and the current state of AI use within the service. Our team began by conducting stakeholder interviews across policy, operations, legal, and data science teams to map existing attitudes, pain points, and governance processes.

Through workshops and desk research, we identified four core insights:

1. Bias often originates upstream. Data used to train algorithms reflected existing societal inequities — meaning even well-intentioned models risked reinforcing them.

2. Transparency was not built in by default. Staff using AI outputs often lacked visibility of how decisions were generated, undermining confidence and accountability.

3. Ethical considerations were fragmented. Different teams applied their own informal checks, leading to inconsistency and gaps in oversight.

4. Organisational culture mattered as much as the technology. Fear of making mistakes sometimes stifled innovation, while others pushed ahead without full understanding of the implications.

We complemented qualitative research with a technical audit of the AI models in use, including data lineage analysis and model explainability testing. This allowed us to quantify potential bias and assess the interpretability of model outputs.

User research was equally critical. We spoke with caseworkers and service managers to understand how AI recommendations influenced real-world decision-making. This surfaced important nuances: for example, caseworkers valued speed but not at the expense of confidence in fairness.

Findings from discovery shaped a clear direction: any technical improvements would need to be paired with organisational safeguards — from governance structures to staff capability-building.

Design approach

Our consultancy took an iterative, co-design approach that combined ethical governance design with practical delivery support. Working in partnership with the department’s digital and data teams, we established a set of guiding design principles anchored in fairness, transparency, and accountability.

Fairness meant ensuring the AI system did not create or amplify bias. Transparency required that decisions made or supported by AI could be explained clearly to both staff and the public. Accountability ensured that humans remained ultimately responsible for outcomes — supported, not replaced, by algorithms.

Building the framework

We structured our delivery around three interconnected workstreams:

1. Ethical Governance and Policy Alignment – We mapped the department’s policies to the UK Government’s AI Ethics Principles and the Data Ethics Framework, identifying where clearer lines of accountability were needed. We then co-developed an “AI Ethics Charter” setting out expectations for teams at each stage of the AI lifecycle — from data collection to model retirement.

2. Bias Detection and Mitigation Tools – Working with data scientists, we introduced bias assessment checklists and model validation templates aligned to FAIR data principles. We piloted open-source bias detection libraries and trained analysts to interpret the outputs.

3. Capability and Culture Change – Recognising that technology alone was insufficient, we designed a tailored training programme for civil servants. This included interactive sessions on ethical decision-making, scenario-based exercises, and guidance on communicating AI decisions to non-technical audiences.

Throughout the project, we used agile delivery methods to iterate and test approaches in short sprints. Each sprint culminated in a playback session with stakeholders, ensuring alignment and building collective ownership of the framework.

Collaboration was key. Our team included business analysts, service designers, and data specialists who worked as one unit with the client’s internal team. Together, we designed new processes for model review and escalation, established transparent documentation standards, and developed communication templates to explain algorithmic recommendations to end users.

Outcome and impact

By the end of the engagement, the department had implemented a robust, transparent, and repeatable process for deploying AI responsibly. Key achievements included:

• Ethical AI Framework adopted department-wide – A clear, practical framework now governs how AI is designed, tested, deployed, and monitored. This includes bias testing thresholds, documentation standards, and a mandatory ethics review before live deployment.

• Bias reduced by 37% in the pilot model – Following retraining with more representative datasets and improved feature engineering, demographic bias across key output categories was significantly reduced.

• Transparency dashboards introduced – We developed a simple, user-facing data dashboard that allows staff to see how model decisions are made, supported by plain-language explanations of key variables.

• Improved user confidence – Internal surveys showed a 52% increase in staff confidence when using AI-assisted tools, with caseworkers reporting they “better understood and trusted the system’s recommendations.”

• Reusable assets created – All artefacts, including the ethics charter, bias testing toolkit, and training modules, were published as open resources within the organisation’s knowledge base to encourage wider reuse across government.

The project demonstrated measurable impact beyond the initial service. The framework is now being referenced as a model for responsible AI adoption by other departments, contributing to cross-government consistency. Importantly, the department regained confidence that innovation and ethics can — and must — coexist.

Reflection

This project reinforced a central belief of our consultancy: that technology in the public sector must be designed not just to function, but to be fair. The success of this engagement was not rooted in a single technical breakthrough, but in the collaboration between digital specialists, policymakers, and frontline staff — each contributing a unique lens to ensure ethical integrity.

We learned that fairness cannot be “added” to an AI system after the fact; it must be designed in from the start. Transparency requires more than open data — it requires communication that is human, clear, and empathetic. And accountability thrives when governance and culture align.

By taking an end-to-end approach — from discovery and design to delivery and culture change — we helped the department build capability, confidence, and credibility in the responsible use of AI.

As the public sector continues to adopt AI, this work offers a repeatable model for how to balance innovation with public trust. It demonstrates that when government teams and specialist consultancies work together, ethical AI isn’t an abstract principle — it’s a practical, measurable reality.