What counts as “AI success”? New FORSEE research suggests it depends on where you look.
To understand differing hopes and fears on AI, FORSEE researchers Charis Papaevangelou, Lucie Loubère, Nikos Smyrnaios, and Pierre Ratinaud (University of Toulouse) analysed how AI is discussed both in media and between citizens.
AI is often framed as a driver of growth and productivity, and economic analyses focus on productivity gains, potential for GDP growth, and job market impacts. Yet this perspective overlooks how AI is reshaping the social fabric of work: the organisation of workplaces, the distribution of power, and the conditions under which people participate in economic life.
Our collective understanding of what makes AI successful remains narrow and incomplete. Success in AI is often equated with technological sophistication or economic efficiency. How quickly can systems learn? How much productivity will they generate? How do they scale in specific contexts?
As Europe moves toward greater AI sovereignty, defining what societal success means has never been more crucial. Last week, our webinar titled “What is success in European AI?” set out to discuss just that.
On September 18th, the FORSEE project brought together small business representatives for a half-day workshop in Dublin to explore what makes AI adoption successful for SMEs. The workshop marked a milestone in capturing the lived experiences of SMEs: their expectations, challenges, and ideas for shaping an enabling environment in Europe.
There is a growing recognition that the development of AI has an environmental dimension. However, the AI industry increasingly frames efficiency gains as a step toward sustainability and greener AI. This is where Jevons paradox comes in: the more efficient and accessible AI becomes, the more it’s used, and its larger environmental footprint grows.
The AI Act represents a significant step toward regulating AI technologies across diverse sectors, with the dual aims of ensuring safety and safeguarding fundamental rights. However, as there are multiple uncertainties in the enforcement of the Act, it should be interpreted as a framework for learning.
Researchers are increasingly required to evaluate how AI use in conducting, communicating, and evaluating research aligns with core values of reliability, honesty, respect, and accountability. There is an urgent need to accelerate policy and best practice development in addressing challenges regarding the responsible use of AI in research.
What would a human rights-based approach mean to AI governance, and how could it counter ethics washing in the AI industry? In a recent episode of the Brain for Business podcast, FORSEE researcher Dr. Marta Lasek-Markey (ADAPT Centre) and Professor Linda Hogan (Trinity College Dublin) delve into their paper, Towards a Human Rights-Based Approach to Ethical AI Governance in Europe.
FORSEE submission to call for evidence of Apply AI Strategy: Large-scale AI models depend on energy-intensive infrastructures such as data centres, which consume vast amounts of electricity, water, and other natural resources. Despite this, public and policy discussions continue to treat AI as largely immaterial, sidelining the physical and ecological realities of digital expansion.
Project lead
Dr Elizabeth Farries
Director of the UCD Centre for Digital Policy
elizabeth.farries@ucd.ie
Lead of Communication and Impact
Johannes Mikkonen
Demos Helsinki
johannes.mikkonen@demoshelsinki.fi
Project Manager
Evangelos Papadamakis
UCD Centre for Digital Policy
vangelis.papadamakis@ucd.ie
Funded by the framework of the Horizon Europe Research and Innovation Programme under grant agreement Nº 101177579
