Artificial Intelligence
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Changing dynamics in Responsible AI

The EU is perceived as the global leader in regulation in comparison with the US. The AI Act, the world’s first regulatory framework grounded on risk, transparency, and rights, demonstrates the EU as the defender of Responsible AI .

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Despite this pioneering legislation, recent geopolitical events – mainly in the US - have shifted attention from ethics and human-centric AI towards national security and economic competition. Big tech companies are downsizing their Responsible AI commitments by laying off their ethics boards and prioritising competitiveness over transparency and responsible deployment.

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However, there are signals that many businesses across the globe do want to implement Responsible AI, though they lack support or mechanisms to act upon it. Although generally small in scale, progress is being made to put Responsible AI programs in place. Nevertheless, the gap between intention, talk, and action on Responsible AI remains.

Maturity

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Changing dynamics in Responsible AI From large to small language models AR and smart glasses become more prominent Increasing AI-enhanced user experience Growing big tech competition for the XR stack Increased application of FAIR principles to enable digital ecosystems in Europe Growing relevance of TRUST principles for data repositories to secure data More decentralisation of data ownership enabled by SSI solutions Emergence of organisational wallets Push for transparent supply chains with digital product passports Bringing computing resources closer to the ‘edge’ Growing need for automated cloud and edge resource management Growing emphasis on neuromorphic (brain-inspired) computing Integrating photonics for accelerated information processing The battle for airwaves AI/Ops, digital twinning and the emergence of the LLM in network operations Power usage and ecological footprint From ad hoc experiments to applications Towards Fault-Tolerant Quantum Computing (FTQC) Cyber-physical technology calls for resilient minds A growing urge for chip sovereignty in Europe

Drivers

Impact

education

Education

  • Copyright protection for teaching materials is been eroded, and there is a need to educate students in a more effective manner on how to use GenAI responsibly and safely.
  • Students need to learn not to blindly trust GenAI models, and as Virginia Dignum says, educate them in navigating an industry culture that may be increasingly ambivalent towards responsible AI.
Research

Research

  • Copyright issues are impacting research involving GenAI models. Researchers require access to independently developed models, rather than just those derived from major tech companies, to avoid legal complications and foster responsible innovation.
  • Industrial collaborations may be affected because of diverging policies on data management and processing.
  • Independent research into AI’s impact on society needs to be prioritised.
Operations

Operations

  • Assessment methods should be reassessed due to a lack of measures from OpenAI to prevent coursework cheating.
  • Clear guidelines for students and staff on using GenAI are needed.
  • Offline models for educational use should be provided to end-users.
  • Caution is required when using GenAI for administrative tasks, especially regarding data protection and ethical issues.
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