How the ELSA Lab works with innovations (use cases)

Within the ELSA Lab, innovations are explored through concrete use cases that serve as real‑world contexts for studying and shaping responsible AI. These use cases enable the lab to integrate ethical, legal, and societal aspects (ELSA) throughout the entire lifecycle of AI development and deployment. By working closely with technology developers, users, companies, policymakers, and societal actors, the ELSA Lab examines how AI applications function in practice and how values such as transparency, fairness, accountability, and sustainability can be embedded in their design and use. In this way, the use cases act not only as testbeds for AI innovation, but also as learning environments that generate actionable insights for responsible, context‑specific AI implementation.

Each case undertakes an ELSA Scan and one or more Quadruple Helix (QH) workshops throughout the ELSA lab cycle in which ELSA aspects are first identified with the AI developers. Next, by involving additional stakeholders during a workshop, using the ELSA Impact tool, new ELSA aspects and improvement suggestions may be found for a redesign of the technology as well as changes to the context of the use case, such as legal and organisational adjustments.

AI for dairy farmers

Mastitis is an inflammatory disease, and it is the most frequently occurring infection in dairy farms. The disease has significant negative impacts on profitability of the business, and heavily weighs on animal welfare. AI can help with the early identification, classification, prediction, and prevention of this disease. By including ethical, legal and social aspects in the recommendations themselves, a livestock farmer or veterinarian can then decide how to act, for example about the use of antibiotics.

Watch this video about the ELSA research related to the recommendation system for treating mastitis disease in dairy cows.

AI in dietary advise

Healthy diet advice, desired for residents of nursing homes for example, begins by monitoring a person’s food intake. But keeping track of this is a demanding and time-consuming job for care personnel. AI can help measuring food intake, including eating behaviour, food type preferences, and recognize nutrimental intake. This use case is being researched on how ELSA aspects need to be organised, such as ethically sound data collection of facial recognition data.

AI for tree nursery

In tree nurseries, digital applications are increasingly being used to support cultivation processes in a more precise, sustainable, and efficient way. Using imaging technologies such as satellite or drone imagery and in‑field camera systems, information is collected on crop growth, vitality, and spatial variation within plots. These images are analysed to reveal patterns that are difficult to detect with the naked eye, such as plant stress, growth differences, or developmental anomalies.

Face Reader

Deze use case betreft een AI innovatie die gegevens kan verzamelen en interpreteren, waaronder gezichtsuitdrukkingen. FaceReader kan in verschillende contexten worden gebruikt, zoals bij onderzoek naar consumentengedrag. Een onderzoek met FaceReader vindt doorgaans online plaats of in een living lab in de supermarkt of restaurant. In het ELSA-lab onderzoeken we gezamenlijk het potentieel om consumentengedrag aan te sturen in de richting van duurzamere en gezondere voedingskeuzes door een ELSA Scan en mogelijk ook stakeholder workshops.

Digital Farm of the Future

The Digital Farm of the Future (DFoF) at Wageningen University & Research is an experimental and practice‑oriented learning environment in which digital technologies such as sensors, data analytics, robotics, and artificial intelligence (AI) are used to make agriculture more sustainable, efficient, and resilient.

Fully documented Fisheries

In the fisheries sector, digital applications are increasingly being used to contribute to a more transparent, sustainable, and better‑regulated industry. For example, camera and sensor technologies on board vessels, combined with data analysis and artificial intelligence (AI), enable the registration and monitoring of catches. These applications support compliance with catch quotas, help reduce bycatch, and contribute to the conservation of fish populations, for instance by enabling the release of bycatch and improving insight into fish stocks.

Pixel Farming

Pixel farming involves addressing weed control by training AI to recognise individual “pixels” within a field, after which a robot can carry out weed control using high‑power laser technology. The laser effectively heats the plant from the inside, disabling it. This enables weed management without the use of chemical agents and without disturbing surrounding crops or the soil. This innovation illustrates how AI technologies are used to specify decisions and interventions down to the level of individual plants, and how AI can contribute to more sustainable agricultural practices.