Rania Wazir

• Accuracy and evaluation methods in the context of NLP systems

Artificial Intelligence

• Towards EU AI Act Compliance: Data Quality and Bias in AI systems

As co-founder of an AI start-up and a consortium partner in research projects involving other SMEs, I am very aware of the importance of clear and simple guidelines that can help smaller organisations comply with regulations. SMEs often lack the resources to identify appropriate methods for compliance through extensive research. This project should therefore have a positive impact on SMEs ability to govern and use data correctly, and to produce and assess bias in AI systems, thus simplifying compliance with the AI Act requirements.

The bias and datasets standards support the European Commission’s standardisation request two on data and data governance. Such a standard is beneficial to consumers, who can rely on quality products and be protected from discrimination, as well as to regulators and auditors, who will benefit from the fact that AI system developers adopt more systematic approaches to data governance and bias risk management. My work on other standards, such as 23282, supports the SR on accuracy, while 12792 supports the standardisation request 4 on transparency. Transparency helps downstream providers and system integrators build safe systems, helps deployers use systems properly, and assists auditors and regulators in carrying out their oversight duties. Finally, just as I have benefited from the experience of other standards experts in learning about standardisation, I feel it is my turn to give back. Whether as project editor, as head of delegation attending plenaries, or as expert participating in working group discussions, I am happy to share knowledge and experiences with those who are new to standardisation.