The debate around artificial intelligence (AI) is too often framed in the abstract. Words like “ethics” and “trustworthiness” can feel remote from day-to-day commercial reality. Yet for organisations actually deploying AI systems, ethics and safety must be translated into practical policies, governance structures, and safeguards. This is not merely a compliance exercise. Done well, ethical and safe AI becomes a route to building organisational resilience, protecting reputation, and securing competitive advantage.
This article sets out how businesses and public bodies can operationalise AI ethics and safety, drawing on current UK and international guidance, frameworks, and case studies.
From principles to practice
Most organisations are already familiar with the core ethical principles: fairness, transparency, accountability, explainability, and respect for privacy. But the challenge lies in moving from theory to implementation and embedding these values into systems, processes, and culture. Organisations should be aware of the following three themes considered best practice for ethical and safe use of AI:
- Ethics by design: embedding ethical considerations into system design from the outset, not retrofitting them after deployment.
- Governance and oversight: ensuring clear lines of responsibility for AI decision-making across the organisation.
- Documentation and explainability: creating an auditable trail withstand scrutiny from regulators, courts, or the public.
These principles are not abstract. They directly inform how organisations assess risk, test systems, manage vendor relationships, and respond when things go wrong.
Ethics by design: risk assessments and testing
The foundation of responsible AI deployment is a structured risk assessment. The Information Commissioner's Office (ICO) Guidance on AI and Data Protection provides a valuable model, requiring organisations to identify risks of bias, discrimination, or lack of transparency during the design phase, not after systems go live.
Key tools to support ethics by design include:
- Data Protection Impact Assessments (DPIAs) are mandatory under UK GDPR where processing is likely to result in high risk to individuals. For AI systems handling personal data, DPIAs should evaluate algorithmic decision-making, data quality, and individual rights.
- Algorithmic Impact Assessments (AIAs) go further, examining fairness and societal impacts. Modelled on tools developed by the Government of Canada, AIAs help organisations evaluate whether AI systems may disadvantage vulnerable groups or produce discriminatory outcomes.
- Bias testing and validation ensure datasets are representative and models do not unfairly disadvantage protected groups under the Equality Act 2010.
Case study: Uber’s algorithmic pricing
In 2022, the Competition Appeal Tribunal considered allegations that Uber’s dynamic pricing algorithms amounted to unlawful price fixing under competition law (Uber London Ltd v Sefton Borough Council, [2022] CAT 24). While the Tribunal dismissed the claim, the case illustrates how algorithmic systems can raise novel legal questions. Robust testing, oversight and documentation can mitigate such risks before disputes arise.
Transparency and explainability
Transparency is central to building trust in AI systems. Both regulators and courts expect that organisations can explain how their AI systems reach outcomes.
This does not require exposing proprietary source code, but it does mean providing plain-language disclosures that inform individuals when AI is used to make decisions affecting them. Model documentation should record design choices, data sources, and validation processes in a manner that can be reviewed by both internal stakeholders and external auditors. Explainability tools such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-Agnostic Explanations) can generate human-readable rationales for individual decisions.
Transparency is not only good practice, but also often a legal duty. Article 22 UK GDPR requires organisations to provide meaningful information about the logic involved in automated decisions with legal or similarly significant effects. This obligation extends beyond simply notifying individuals that automation is being used as it requires organisations to explain the factors that influenced the decision and the significance of those factors in a way that is genuinely comprehensible to the affected individual. Individuals also have rights to access information about how their data is used and to challenge decisions.
Case study: Ofcom and online safety
In 2023, Ofcom published draft guidance under the Online Safety Act requiring platforms to explain how algorithmic recommendation systems shape user experience (Ofcom consultation). This reflects a broader trend that regulators expect transparency not just toward users, but also toward oversight bodies, enabling effective supervision.
Governance and accountability
Even sophisticated technical safeguards will fail if governance is weak. Best practice involves assigning clear responsibility for AI oversight and embedding accountability into organisational structure. In order to establish effective governance, organisations can consider the following:
- AI Ethics Committees bring together cross-functional expertise, such as legal, compliance, technical, and business representatives, to review high-risk AI deployments and resolve ethical dilemmas.
- Board-level accountability ensures directors understand their duties under the Companies Act 2006, including managing long-term risks to the business. AI governance should be integrated into existing risk management and ESG reporting.
- Dedicated roles such as Chief AI Ethics Officers or expanded Data Protection Officer responsibilities help ensure ongoing oversight and coordination across departments.
The key is to ensure that ethical oversight is not merely a paper exercise. Ethics committees must have genuine authority to challenge or halt AI deployments, and their recommendations must be taken seriously by senior management. This requires both formal authority within governance documents and a culture that values ethical scrutiny as a contribution to business success rather than an obstacle to innovation.
Case study: Barclays’ AI governance framework
Barclays has publicly described its internal AI governance model, which includes oversight committees, ethical risk assessments, and alignment with Financial Conduct Authority expectations (Barclays AI Principles, 2021). While voluntary, such transparency demonstrates a proactive approach that reassures regulators, investors and clients.
Redress and accountability mechanisms
A quality of ethical and safe AI is ensuring individuals can challenge and appeal automated decisions. Human-in-the-loop review is particularly important for high-stakes decisions such as credit scoring or recruitment, where automated systems may have profound effects on individuals' lives. Accessible complaints processes should be integrated into existing customer service channels, ensuring that individuals do not face additional barriers when seeking to challenge algorithmic decisions. Audit trails allow organisations to trace how an outcome was generated, which is essential both for responding to individual complaints and for identifying systemic issues that may require model retraining or redesign.
This is not only best practice but also aligns with statutory obligations under the Equality Act 2010, consumer protection law and data protection rights of access and rectification.
Toolkits and frameworks
Organisations do not need to start from scratch. Numerous frameworks are available to help organisations implement ethical and safe AI, including:
- ICO Guidance on AI and Data Protection (UK)
- OECD AI Principles (international)
- NIST AI Risk Management Framework (US)
- Singapore Model AI Governance Framework (Asia-Pacific)
While these frameworks are voluntary, regulators often view alignment with them as evidence of due diligence. In litigation, courts may treat them as reflecting industry standards, informing assessments of reasonable care.
Contractual considerations
For organisations procuring AI solutions from vendors, contracts are a vital tool for embedding ethical safeguards. Keys clauses to consider include transparency obligations, which should require suppliers to disclose training data, methodologies, and testing processes, enabling the buyer to assess whether the system meets ethical and legal requirements. Liability and indemnity clauses must allocate responsibility for discriminatory or unlawful outcomes, recognising that both vendor and buyer may bear legal obligations depending on their respective roles in the AI system's deployment. Audit rights allow buyers to verify compliance with ethical and legal requirements, either through their own technical teams or through independent third-party auditors. Intellectual property ownership clauses should clarify rights in trained models, particularly where customer data is involved in training or fine-tuning.
Case study: NHS and Babylon Health
In 2021, questions were raised about the use of Babylon Health’s AI chatbot for NHS triage services, after reports of inaccurate or unsafe outputs (BBC News, June 2021). While not a litigated dispute, the controversy highlights the importance of procurement diligence and contractual safeguards in public-private AI partnerships. The incident prompted NHS England to strengthen its guidance on AI procurement, emphasising the need for clinical validation and ongoing monitoring of AI medical devices.
Ethics and safety as a competitive advantage
Far from being a compliance burden, ethical and safe AI can differential organisations in crowded markets. Businesses that demonstrate fairness, transparency, and accountability are more likely to win consumer trust, attract investors, and secure government contracts.
Investors are increasingly applying ESG (environmental, social, governance) metrics to AI development and deployments. Organisations unable to demonstrate ethical governance may find themselves excluded from procurement processes or investment portfolios. Equally, robust AI governance can enhance valuation and market positioning.
Recommendations for organisations
To operationalise ethical and safe AI effectively, we recommend the following:
- Conduct structured risk assessments for all AI deployments, integrating DPIAs, algorithmic impact assessments, and bias testing from the design phase.
- Document design and decision-making processes comprehensively to create defensible audit trails that support accountability and continuous improvement.
- Establish governance structures including ethics committees, board-level oversight, and clear accountability for AI risks and performance.
- Review procurement contracts to ensure suppliers meet ethical and regulatory expectations with appropriate transparency, liability, and audit provisions.
- Prepare for scrutiny by assuming that regulators, courts, and the public will demand transparency and accountability.
- Monitor and iterate as AI systems can drift over time as data distributions change. Implement ongoing monitoring and be prepared to retrain or retire systems that no longer meet ethical standards.
Conclusion
AI ethics and safety is no longer an abstract debate, it is a practical necessity. The organisations that succeed will be those that embed ethics into design, governance, and oversight. This is not simply about avoiding fines or disputes, it is about building systems that are trusted, resilient, and aligned with long-term organisational values.
Solicitors and advisers have a key role to play by translating broad principles into tailored frameworks, drafting robust contracts, and ensuring governance models can withstand regulatory scrutiny. In a marketplace where trust is increasingly scarce, ethical AI is not just a shield against a risk, it is a strategic asset.
The question is no longer whether organisations should operationalise AI ethics, but how quickly and effectively they can do so. Those who act now will be better positioned not only to navigate evolving regulation but to lead in building the responsible AI systems of the future.
