9 Ethical considerations and bias

Ethical deployment of GenAI involves ensuring fairness, transparency, accountability, and inclusiveness. AI's impact on employment is a major concern, as automation may displace lower-skilled roles while disproportionately benefiting those with digital expertise. This poses the risk of deepening socio-economic divides (Gmyrek et al. 2023).

Algorithmic bias occurs when AI systems reproduce and amplify discriminatory patterns found in training data. This is not a technical glitch – it reflects societal inequities embedded in data collection and labelling. Meanwhile, bias in training data can lead to discriminatory outcomes (Pump Court Chambers, 2023), particularly in recruitment, finance, or law enforcement. Ethical AI development requires diverse datasets, ongoing auditing, and human oversight. Organisations should also consider workforce and employee ethics – offering reskilling, protections, and equitable access to AI benefits.

Addressing bias requires using representative training datasets, identifying proxy variables that encode discrimination, and conducting fairness audits. Human reviewers must oversee automated decisions, particularly in sensitive domains. Inclusive design practices also help anticipate harms and build trust.

Inclusive design – engaging a wide range of stakeholders in AI development –ensures systems reflect diverse perspectives and societal values. Ethical AI also demands anticipatory governance – evaluating impacts before deployment, particularly in dual-use technologies like facial recognition that could threaten privacy and fundamental freedoms.