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Putting Science Into Standards workshop on Data quality requirements for inclusive, non-biased and trustworthy AI

CEN and CENELEC, together with the European Commission’s Joint Research Centre (JRC), carry out an annual ‘foresight on standardization’ exercise under the Putting Science into Standards (PSIS) initiative.

The 2022 PSIS workshop will focus on the topic of “Data quality requirements for inclusive, non-biased, and trustworthy artificial intelligence”.

The EU’s approach to artificial intelligence focuses on excellence and trust, aiming to boost research and industrial capacity and ensure fundamental rights. Bias in existing state-of-the-art AI models has been widely proven, raising concerns on societal consequences. Researchers in academia and industry have proposed different methods to eliminate bias, starting from the input data and by curating the AI model training. However, no common approach to measure requirements in data quality has been defined.

Standards and guidelines are needed to define types of data used in creating AI models to ensure that bias is not present. Ensuring that AI model data upholds quality standards that result in non-biased, inclusive AI systems will provide the right foundation from which trustworthy AI can be further developed and deployed to improve EU citizens' lives.

The objectives of the workshop are:

  • Presenting current and future needs and recommendations to address data biases and related ethical concerns in the context of AI;
  • Mapping of existing and missing standardization efforts;
  • Developing guidelines in view of data quality standards for AI models;
  • Proposing and recommending steps to start the process of drafting standards.

Register now

  • artificial intelligence | standardisation
  • Wednesday 8 June 2022, 09:00 - Thursday 9 June 2022, 16:00 (CEST)
  • Online only

Practical information

When
Wednesday 8 June 2022, 09:00 - Thursday 9 June 2022, 16:00 (CEST)
Where
Online only
Languages
English
Website
Data quality requirements for inclusive, non-biased, and trustworthy artificial…