Artificial intelligence (AI) is rapidly transforming the public sector, offering unprecedented opportunities for efficiency, innovation and improved service delivery. However, this transformative power comes with a complex web of risks. For risk management professionals, understanding and mitigating these risks is paramount to ensuring responsible and effective AI adoption.
Earlier this month, the UK Government’s published its AI Playbook which provides a valuable framework for navigating this landscape, offering crucial lessons for anyone involved in AI risk management. Although it is aimed at government departments and similar public bodies, there are some general takeaways that apply to most organisations which we’ve attempted to capture here.
Artificial Intelligence (AI) presents transformative opportunities, from automating tedious administrative tasks to enhancing decision-making with data-driven insights. However, as AI adoption accelerates, so do the risks associated with its deployment.
Governance: The backbone of AI risk management
AI in government isn’t just about efficiency—it’s about accountability. The AI Playbook advocates for robust governance frameworks to oversee AI projects, ensuring transparency, fairness and compliance with legal standards.
For example, an AI governance board or a dedicated AI ethics committee can provide oversight, reviewing AI-driven decision-making systems to prevent unintended biases, where AI-assisted decisions must be fair, explainable and contestable.
Governance also means cross-departmental collaboration. AI risks don’t exist in silos. Engaging policy experts, legal teams, technologists, and ethicists ensures a well-rounded approach to risk mitigation. Furthermore, there must be mechanisms for regular audits and assessments to ensure compliance with evolving regulations and ethical guidelines. Transparency reports should be published to allow stakeholders to scrutinise AI applications. This especially so in government functions but can also apply more generally to private and charitable sectors.
Security: The emerging threats of AI
One of AI’s greatest strengths—its ability to process vast amounts of data—also makes it a cybersecurity challenge. AI systems are prime targets for data breaches, adversarial attacks, and misinformation campaigns.
Cybersecurity Threats: Generative AI can be weaponised for phishing scams, deepfakes, and automated hacking. Organisations need to adopt AI-specific security protocols, such as those developed by OWASP (a globally recognised nonprofit organisation focused on improving software security), to mitigate risks.
- Data and model vulnerabilities: AI models trained on compromised or biased datasets can be manipulated. For instance, if a facial recognition system used by law enforcement is trained on unrepresentative data, it may result in racial bias and misidentifications.
- AI-generated misinformation: With deepfake technology becoming more sophisticated, AI-generated misinformation could be used to manipulate public opinion or discredit officials. Strong [regulatory] oversight and AI content verification tools will be crucial.
To combat these threats, organisations should prioritise secure-by-design principles, ensuring that prior to deployment AI models undergo rigorous ‘red teaming exercises’ (where ethical hackers, security experts, and adversarial AI specialists simulate real-world attacks to identify vulnerabilities in AI systems).
Ethical and legal considerations: The trust factor
AI must operate under the principles of fairness, transparency and accountability. Some key concerns include:
- Bias and discrimination: AI models learn from historical data, which can reflect existing biases. In recruitment, for example, AI-driven hiring tools have been found to favour male candidates over female candidates if trained on biased historical hiring patterns.
- Hallucinations and false Information: Large Language Models (LLMs) can generate ‘hallucinations’ (misleading, or entirely fabricated information that appears credible and highly convincing but factually incorrect. In legal or healthcare settings, this could have serious consequences if professionals rely on AI-generated insights without verification.
- Algorithmic transparency: For those operating in the public sector, AI systems must be explainable. The UK’s Algorithmic Transparency Recording Standard (ATRS) requires government bodies to document and disclose their AI models’ use in decision-making. For those in other sectors similar attempts at transparency are likely to be welcome and expected amongst stakeholders.
- Public consultation and engagement: To maintain trust, public agencies should proactively involve civil society organisations, academics, and the public in discussions on AI’s societal impact. Public-facing AI applications should have clear disclaimers and opt-out mechanisms where applicable. Again, those organisations outside of the public sector should assess the need to adopt such practises based on the customers and clients they serve.
Human oversight: Keeping AI accountable
AI should augment, not replace, human decision-making—especially in high-stakes scenarios. The AI Playbook emphasises the importance of human-in-the-loop systems, ensuring that AI-driven decisions undergo human review where necessary.
For instance, while AI can assist in fraud detection, a flagged transaction should still be reviewed by a human analyst to confirm legitimacy before action is taken. This mitigates the risk of false positives that could unfairly penalise individuals.
Additionally, continuous monitoring mechanisms should be in place. AI models evolve over time, and what works accurately today might drift and become unreliable tomorrow. Having a process to audit AI performance and gather user feedback ensures that errors are caught early.
Another key concern is automation complacency, where humans may over-rely on AI outputs without critical assessment. Proper training programmes should be implemented to educate users on how to interpret AI-generated insights responsibly.
Economic and workforce implications
AI’s ability to automate tasks could reshape employment. While AI can streamline administrative work, job displacement risks must be acknowledged and managed. This is likely to be a significant feature of most organisations’ ‘people risks’ and their associated workforce plans.
- Upskilling and reskilling initiatives: Employees should have access to AI literacy programmes to help them transition into roles that require human judgment and oversight.
- Hybrid work models: AI should be seen as a collaborative tool rather than a workforce replacement. For example, in legal case assessments, AI can provide research assistance, but human solicitors should retain final decision-making power.
- Fair hiring practices: AI-assisted recruitment tools must be audited to ensure they do not reinforce socio-economic inequalities by disproportionately filtering out candidates from marginalised backgrounds.
Balancing innovation with risk mitigation
Organisations must strike a balance between embracing AI’s potential and managing its risks. This means:
- Implementing AI in areas where it provides clear benefits—such as automating form processing, predictive analytics and AI-assisted cybersecurity.
- Avoiding AI in high-risk scenarios where human judgement is irreplaceable—such as fully automated legal rulings or medical diagnoses.
- Investing in AI literacy for staff, ensuring that decision-makers understand AI’s capabilities and limitations.
- Ethical procurement of AI systems: Contracts for AI solutions should include clear accountability clauses, ensuring vendors adhere to ethical AI development standards.
Final thoughts: Building responsible AI
The UK Government’s AI Playbook provides a solid framework for mitigating AI risks while maximising its benefits. As AI adoption grows, so must our vigilance in ensuring security, fairness and transparency. Organisations should take a proactive approach, ensuring AI policies are shaped not just by technical experts but by a diverse range of voices, including legal experts, ethicists and stakeholders.