At QCon London 2026, Clara Higuera, Responsible AI Program Lead at BBVA, announced that many of the risks associated with AI systems are fundamentally engineering challenges rather than pure governance or policy issues. This session explored how AI systems are being integrated into critical products and decision-making processes. As deployments continue, failures in these systems can have significant real-world consequences. This shift requires engineers to treat the ethical properties of AI systems with the same rigor they apply to reliability, performance, and security.
The lecture began with the widely publicized case in the United States in which Robert Williams was wrongly arrested after being mistakenly identified by a facial recognition system. Incidents like this highlight how algorithmic errors can directly impact individuals and communities.
Such failures are often caused by technical choices made during development. Training datasets may not be representative of the population affected by the system, model architectures may lack explainability, and evaluation pipelines may fail to detect bias before deployment.
The talk emphasized that rather than viewing these issues as external policy concerns, they originate within the engineering process itself.
AI systems encode values built into the design. Decisions about data collection, feature engineering, model architecture, and evaluation metrics can all impact how the system behaves in production. For example, biased results in loan approvals, recruitment processes, or medical diagnostics can be caused by unrepresentative training data or poorly defined optimization goals. Without explicit checks, the model can reinforce historical biases present in the dataset.
According to the presentation, incorporating ethical principles into the AI lifecycle requires engineers to ask questions throughout development rather than after deployment. This includes assessing the representativeness of datasets, measuring model behavior across demographic groups, and ensuring the system is observable after deployment. The talk highlighted several principles that guide AI system design. Fairness, transparency, security, sustainability, and accountability were presented as important factors that engineers must consider when building AI-powered systems.
Equity requires evaluating how a model performs across different groups and ensuring that outcomes do not systematically disadvantage certain groups. Transparency involves increasing the interpretability and explainability of models so that stakeholders understand how decisions are made.
Security is also an emerging concern, especially as new attack vectors such as prompt injection and model extraction become more common in AI systems. Sustainability is also in the spotlight due to the computational costs associated with training and deploying large models. These aspects need to be addressed through engineering practice rather than abstract principles.
One of the challenges organizations face is translating high-level ethical concepts into practical engineering workflows. Teams often understand the importance of fairness and transparency, but lack a clear path to implementing them.
The presentation suggested incorporating ethics checks throughout the development lifecycle. This includes assessing fairness during model training, explainability analysis before deployment, security testing against adversarial attacks, and monitoring systems to detect unexpected behavior in production.
By incorporating these practices early in system architecture, organizations can reduce the risk of ethical issues being discovered after the system is already in use. The talk compared the current stage of AI development to earlier technology transitions. Industries such as aviation, electrical, and automotive engineering initially advanced faster than the safety standards needed to govern those industries. Over time, these industries have developed new engineering methodologies, standards, and regulatory frameworks to improve the reliability of large-scale systems.
AI seems to be entering a similar stage. As AI systems move from experimental tools to critical infrastructure, engineering practices are likely to evolve to incorporate safety, reliability, and ethical considerations as core system requirements. Software architects and engineering leaders play a critical role in shaping these practices. Because technology often evolves faster than regulations, developers often work in environments where formal standards have not yet been established.
In this context, ethical principles serve as design guidelines to help teams deal with emerging risks. Organizations that treat ethical AI as an engineering discipline rather than an afterthought may be better positioned to build reliable and resilient systems.
The presentation concluded by encouraging developers to treat the ethical properties of AI systems as measurable engineering requirements. Incorporating fairness assessments, explainability checks, security testing, and resource efficiency into the development lifecycle ensures that AI systems remain technically robust and socially responsible. As AI continues to be incorporated into products, platforms, and infrastructure, the engineering decisions made during development will increasingly shape how these systems impact society.
