Research also benefits greatly from the use of AI: one example is the Italian Institute of Technology (IIT), a scientific research center funded by the State, with the goal of supporting excellence in basic and applied research to foster the development of the national economic system. Its activity is characterized by strong multidisciplinary, and is organized into four main areas: robotics, nanomaterials, computational sciences, and life tech, within which AI is used transversally to accelerate research.
The use of AI can also be fundamental for the Third Sector, a field in which social organizations work every day in complex contexts: educational poverty, health vulnerabilities, marginalization, population aging, labor inclusion, and environmental sustainability. These are areas where decisions require listening, closeness, interpretation of needs, and trust. Precisely here, AI can become useful if introduced carefully into organizational processes, and if it remains at the service of people.
This article presents how these technologies are already being applied in practice, through examples of assistive robotics and organizational tools developed at IIT, and the responsibilities that must guide their use within the civil society organizations (CSOs).
Responsible Use Equals Studying
AI is a set of computational methods capable of recognizing patterns in data, generating text or images, classifying information, formulating predictions, and suggesting solutions. The novelty of recent years lies in scale: more data, more computing power, more complex models. This has produced a clear leap forward, especially in generative systems, capable of writing, synthesizing, translating, and answering questions based on natural language instructions.
The decisive question is not only what these systems can do, but what still isn't fully understood about how they work. In science and engineering, researchers are used to building tools whose behavior can be predicted within reasonable margins. With the most recent AI models, this predictability is harder to achieve: the result can be effective, but the path that led to it is not always transparent. This is one of the reasons why many people feel wary, an understandable reaction to a technology that works even before being fully understood.
This is why the first step is to study it. Studying does not mean blocking innovation, but making it more solid. The European approach, which distinguishes between research, applications, and levels of risk, does not slow down knowledge, but calls for greater responsibility when a technology enters the market, services, public decisions, and everyday life.
In the Third Sector this principle is particularly relevant. An organization can use AI to organize documents, summarize materials, analyze emerging needs within the communities it serves, improve communication with citizens, translate content, and direct scarce resources. These are seemingly simple uses, but they can free up valuable time that operators, educators, social workers, and volunteers can dedicate to relationships and care. AI does not replace social work: it can lighten the repetitive parts and allow people to focus on what truly requires human judgment.
For example, many organizations manage large amounts of information that remain scattered across separate archives, without becoming shared knowledge. An AI system can help connect these materials, bring out recurring patterns and needs, and make organizational memory more accessible.
CSOs, Robotics and AI: Example from Italy
However, that is not the only way AI can help in CSO’s work and missions.
In Italy is facing with growing urgency: population aging. The shortage of healthcare and care staff is already a reality today in many areas of the country, where finding nurses, healthcare assistants, or caregivers is increasingly difficult. In this scenario, AI does not replace care, but can make it more accessible through remote monitoring systems that promptly flag risk situations, assistants that help elderly people manage therapies and appointments, or diagnostic tools that speed up screening and free up staff for the most complex cases. Assistive robotics too, on which IIT has worked for years, can offer concrete support in the daily life of those with reduced autonomy, without replacing the human relationship that remains central to care.
This was proven through the Rehab Technologies lab, born from the collaboration between IIT and INAIL (The National Institute for Insurance against Accidents at Work), where advanced robotic devices are developed in collaboration with patients, who are involved from the earliest stages of design. Among these is Hannes, the robotic prosthetic hand that restores about 90% of the functionality of a natural hand to people with upper limb amputation. It is controlled myoelectrically, through artificial intelligence algorithms that interpret signals from residual muscles, and thanks to a mechanism it adapts its grip to the shape of objects, making the gesture fluid and natural. There is also Twin, the robotic exoskeleton for lower limbs that allows people with spinal cord injuries or other motor deficits to stand and walk again, with operating modes that adapt to the patient's remaining degree of motor autonomy.
What to Always Keep in Mind
There is also a question of equity. AI can broaden access to skills that until recently were concentrated in a few large organizations: data analysis, multilingual production, design support. For a small association, this can make a real difference. In this scenario, data quality takes on a fundamental role. Social organizations often work with sensitive information related to vulnerable people. It is not enough to ask whether a tool "works": one must understand what data it uses, who controls it, and what risks of error or discrimination it carries. An algorithm trained on biased data can produce biased decisions. An opaque system can make it difficult to contest a result.
This highlights a specific role for the Third Sector: moving beyond the position of technology user to become an active partner in interpreting and shaping innovation. Its close knowledge of communities can help researchers and institutions design AI systems that deliver real benefits while anticipating potential harms.
As researchers, we cannot design socially useful technologies while remaining shut inside laboratories: we must build them together with those who know the real problems.
AI is also an organizational change: introducing it means rethinking skills, roles, and decision-making processes. A useful question might be: does this application help us be closer to people, more transparent, more effective? If the answer isn't clear, it's better to proceed with caution.
Finally, researchers should not forget the material impact of AI. Training and using large models requires significant energy and infrastructure: sustainability cannot be a separate issue from digital innovation. If AI is to contribute to the ecological transition, it must itself become more efficient, less energy-intensive, and more proportionate to its purposes. It's not always the biggest model that's needed. Often what's needed is the right model, well built, verifiable, suited to the context. This too is a form of responsibility.
Italy, as well as Europe, carries an important responsibility: not merely to chase models developed elsewhere, but to contribute to an AI with their own social, democratic, and scientific values.
For the Third Sector, this is a challenge, but also an opportunity. It means entering the debate not as spectators, but as protagonists. It means bringing to the places of innovation questions that are often overlooked: Who benefits from this technology? Who risks being excluded from it? Which relationships are being transformed? Which rights need to be protected? What idea of society is embedding in our digital systems?
CSOs, Robotics, and AI: Final Thoughts
This is ultimately the lesson these examples offer: robotics and AI find their truest value not as standalone innovations, but when shaped together with CSOs to serve the people and communities they represent.
Artificial intelligence can help us see connections that we people cannot grasp with the naked eye. It can accelerate research, improve services, and support better-informed decisions. But social change does not come from automation. It comes from the human capacity to make better use of knowledge, to take on responsibility, and to steer technology toward shared goals. The challenge, then, is not to become more like machines. It is to build machines that help us become more capable of caring for the world and for people.
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This piece of resources has been created as part of the AI for Social Change project within TechSoup's Digital Activism Program, with support from Google.org.
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About The Author
Giorgio Metta is an engineer and international researcher, Scientific Director of IIT. He studied at the University of Genoa and MIT, and is among the leading experts in cognitive robotics and AI systems. He oversaw the development of “iCub”, one of Europe’s most advanced humanoid robots, and is a key figure in the dialogue between AI, robotics, and society.