AI is powerful, but without responsibility, it can cause harm
poorly managed ai has the potential to exacerbate existing inequalities, concentrate power, or create negative externalities that harm communities
Your people, customers, and stakeholders need confidence that AI is safe, fair, and aligned with your values
What is Responsible AI?
the 6 essentials of responsible Ai
Rollover each card below to read more about how to get started with each area (or click the doc to the right to see it in full screen)
Who's job is it?
Using AI in your organisation will mean new skills, new responsibilities, or even completely new roles
people who will need new ai skills
Legal Counsel: Needs to develop expertise in emerging AI regulations and standards to advise on compliance and risk mitigation specific to AI applications.
Risk Managers: Needs to incorporate AI-specific risks into enterprise risk frameworks, including reputational, operational, and regulatory dimensions.
Product Manager: Must embeds AI ethical considerations directly into product development lifecycles, ensuring responsible design from concept through to deployment.
HR: Should develop guidelines for using AI in hiring, promotion, and employee assessment that protect against discrimination while capturing benefits.
Privacy Officers: Must address new privacy challenges created by AI's data requirements and potential for unintended inference about individuals.
Programme / Portfolio Management: Needs to build industry standards and ethical frameworks into AI project planning prioritisation frameworks.
Data Managers/ML Engineers: Must expand beyond technical optimisation to consider fairness, explainability, and potential social impacts of their models.
Learning & Development: Develops and delivers educational programs to ensure all employees understand responsible AI principles relevant to their roles.
IT System Testers: Should introduce specific testing to mitigate bias in AI systems
cross functional / Board roles
Chief AI Officer: Provides executive leadership on responsible AI practices, sets organisational standards, and ensures alignment between AI implementations and company values
AI Governance Lead: Develops and maintains the frameworks, policies, and processes that guide responsible AI development and deployment across the organisation.
regulations & future landscape
european union
EU IA ACT
The world's first comprehensive AI regulation, categorising AI systems by risk level with varying obligations. While the UK is no longer an EU member, this regulation affects any UK business serving EU customers or using AI systems that impact EU citizens.
General Data Protection Regulation (GDPR)
Though primarily a data protection law, it contains important provisions affecting AI, including restrictions on solely automated decision-making, requirements for data minimisation, and the right to explanation.
Coming soon...
See below for info on the UK AI Regulation Bill
UK
Data Protection and Digital Information Bill
The post-Brexit evolution of UK data protection law, maintaining many GDPR principles while potentially diverging in areas relevant to AI innovation and automated decision-making.
https://ico.org.uk/about-the-ico/the-data-use-and-access-dua-bill/
UK Algorithmic Transparency Standard
A government initiative requiring public sector organisations to publish information about how they use algorithmic tools in decision-making, potentially expanding to affect private sector suppliers.
https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub
National AI Strategy
While not regulation per se, the UK's strategic approach to AI includes regulatory components and signals future regulatory direction, particularly around innovation-friendly frameworks.https://www.gov.uk/government/publications/national-ai-strategy
Financial Conduct Authority (FCA) AI Guidelines
Specific regulatory guidance for financial services firms using AI, focusing on explainability, fairness, and governance of AI systems.
cross border considerations
OECD AI Principles
International guidelines that the UK has adopted, emphasising AI systems should be transparent, explainable, robust, secure, and respect human rights
ISO/IEC Standards
Emerging technical standards for AI systems (such as ISO/IEC 42001 for AI Management Systems) that are increasingly referenced in regulatory frameworks and procurement requirements.
Sectoral Regulations
Industry-specific rules covering AI applications in healthcare (MHRA), advertising (ASA), and employment (Equality Act implications), which impose additional requirements beyond general AI regulations.
emerging
UK Artificial Intelligence Regulation Bill
The UK’s proposed Artificial Intelligence (Regulation) Bill aims to create a central AI Authority, mandate AI Officers in businesses, enforce transparency on training data, and introduce independent audits. It promotes ethical, accountable AI use without stifling innovation—offering regulatory sandboxes for testing. Unlike the EU AI Act, which enforces strict, risk-based rules across all sectors, the UK’s approach is more principles-led and flexible. Although still a private member’s bill, it signals growing pressure for formal regulation. UK businesses should prepare for likely changes by strengthening governance, data practices, and audit readiness, especially if competing in or partnering with EU-regulated markets.
Algorithmic Impact Assessments
Increasingly required by various regulations, these structured evaluations help organisations systematically assess potential harms of AI systems before deployment.
Corporate AI Transparency Requirement
Evolving expectations for organisations to disclose how they develop, deploy and govern AI systems, including to shareholders and in ESG reporting.
AI Assurance Ecosystem
The UK's approach to building trust in AI through standards, tools and services that help organisations verify claims about their AI systems