Mission and Domains

Digital Collective (DC)

Digitalisation is not just a technological phenomenon. It concerns an ever shifting relation between digital technologies and societal developments that leaves neither of them untouched. To understand digitalisation, we should take into account the pace of development and its ubiquitous nature. The Digital Collective (DC) therefore examines digital technology innovations so as to evaluate societal impact (benefits and drawbacks) of the relevant systems and advise on key policies and pathways for system improvement and change. The platform draws on qualitative and quantitative data and analysis, and offers interdisciplinary perspectives, including philosophy and ethics, economic, behavioural and ethnographic research, and connecting "challenge-based" with "model-based" teaching. 

mission

• To foster a hub that functions simultaneously as a platform for connecting people within UT, including within BMS, but also as a space in which to gather external stakeholders. In this way we seek to contribute to UT’s social and outreach missions, including as a ‘people-first’ university of technology and to ensure technologies match society’s needs.
• To analyse key research domains and themes of the platform, especially as they align to the UT’s strategic mission, including within BMS and in conjunction with stakeholders.
• To integrate teaching, learning, and student training on topics and themes arising from and related to digitalisation, broadly conceived, and to contribute to challenge-based learning initiatives in these areas.
• To highlight and analyse problems related to digitalisation, promote solution-based approaches, and explore the scope for reframing solutions including by engaging diverse stakeholders across industry and society. In this way we contribute to the UT’s strategic mission to listen to and collaborate with societal partners concerning challenges that call for an academic contribution.
• To support social and political actors in the anticipation of digitalization impacts, including promotion of responsible innovations that take into account ethical and societal challenges and the broad needs of diverse stakeholders.

Domains

The platform focuses on four central domains and their multiple intersections:

1. Healthcare
2. Higher education
3. Sustainability
4. AI and automation

1. Healthcare

Many healthcare practices require access to significant volumes of data including for the assessment, diagnosis, prognosis and treatment of ill health and disease, as well as patient information that is shared for the purposes of those practices as well as for research. Digitalisation allows for quick and efficient access to data, as well as providing larger and potentially more reliable datasets on which digital tools, such as automation and machine learning systems rely. There are, however, some ethical and practical issues to consider as these developments progress. These include: 

• Use of big data in healthcare digitalisation.
• Increasing role of AI, machine learning and other automated computational systems as tools in healthcare, including for assessment and diagnostic processes in physical and mental health. 
• Tensions between privacy and transparency with regard to patient data.
• Creation of new forms of (especially personal) data as a result of digitalisation, including the use of brain data for the diagnosis of disease. 
• Incorporating participatory research methods in the development of digital health technologies.

2. Higher education

When people think of the digitalisation of higher education, teaching and learning in an online environment might spring to mind first. The rise of MOOCS over the past decade and the turn to online recent COVID pandemic stand out as two clear instantiations of the digital era in academia. The digitalisation of higher education, however, goes way beyond this. It covers a set of data-driven ideals and practices with pervasive but diffuse effects: the construction of a digital infrastructure to measure and monitor academic performance; the emergence of educational analytics that draw on that infrastructure to gain insight into student development; the integration of research metrics about scientific and societal impact in science and science policy. Taken in that broader sense, the digital era in academia raises a number of profound issues:

• Issues related to the construction data infrastructures and their effects of daily practices.
• Issues of power that come with quantification and governing by numbers.
• Issues related to inclusion and exclusion that algorithmic decision-making could lead to.
• Issues related to the use of generative AI in educational contexts.

3. Sustainability

While digitalisation and sustainability are often framed as “twin transitions,” they have a complex relationship.  For instance, digitalisation is offered as a solution to environmental sustainability issues by increasing efficiencies and facilitating new forms of data collection and monitoring, yet simultaneously places new pressures on energy systems and resources to build, power, and maintain underlying infrastructures. In relation to social sustainability, digitalisation leads to new (and perpetuates existing) forms of social exclusion, bias, and injustice, but may also facilitate new social connections and forms of organising. Likewise, digitalization can lead to new forms of economic exploitation and precarity, while also facilitating alternative economic models and practices. Some issues to consider include:

• Socio-environmental impacts of data centre development
• Mineral mining and e-waste from the electronics industry and their effects on communities and environments, especially in the Global South. 
• New forms of digital environmental monitoring, including through smart city, smart forest, and related initiatives. 
• Social justice and equity in AI and algorithmic systems. 
• Cooperative and commons-based digital economies. 

4. Artificial intelligence and automation

Artificial intelligence (AI) is a main driver of digitalisation. AI relies on data and models, not only for mapping but for communication of results and potential applications. It is also connected to automation and “smart” technologies and infrastructure. AI as a term tends to include related approaches such as machine learning (ML) and semi autonomous (closed or open loop) systems, and so it can be considered an umbrella term for a range of related and complementary systems. Some issues to consider include:

• Use of data in AI and the impact of bias.
• Role of models, simulations, and scaling, with AI used for prediction, for analysis (for good and for bad), and ‘automating’ decision-making. Or as used in surveillance technology, in education, in drones, in self-driving cars, as well as for digital health and care innovations.
• Use of AI in contemporary political and educational spheres that rely on digital platforms, e.g. use of Deepfakes and social media manipulation.
• Increasing complexity and ‘black box’ in AI impacts on scope for surveyability as well as for transparency, which can further limit understanding and knowledge of how AI functions, including among users.