AI Ethics in Engineering: Practical Considerations for Developers

Ethics as Engineering Practice

AI ethics is often discussed abstractly. These are concrete engineering requirements to be specified, implemented, and tested like any other requirement.

Demographic Parity Testing

def test_demographic_parity(model_fn, test_cases):
    results = {}
    for case in test_cases:
        group = case['group']
        score = evaluate_outcome(model_fn(case['input']))
        results.setdefault(group, []).append(score)
    rates = {g: sum(s)/len(s) for g, s in results.items()}
    disparity = max(rates.values()) - min(rates.values())
    return {'rates': rates, 'disparity': disparity, 'pass': disparity < 0.05}

Transparency Requirements

Users should know when interacting with AI, what data is used about them, and what the limitations are. The EU AI Act mandates disclosure for high-risk AI systems.

Deployment Guardrails

Before deploying AI affecting access to services, jobs, or credit: bias audit with representative data, defined disparity thresholds, human override mechanisms, post-deployment monitoring, and a rollback plan.

The Business Case

Discriminatory AI creates legal liability, reputational risk, and regulatory exposure. A bias audit costs far less than an enforcement action or class action. Build ethics testing into your process from the start.