We have an exciting and rare opportunity to join the prestigious Horizon Europe Doctoral Training Network, Data2Action, as a Doctoral Fellow. The successful candidate will be registered as a doctoral student at TU Dortmund University and enrolled in the Data2Action Doctoral Network, with secondment opportunities in German public sector and non-profit organizations.

Data2Action will train 13 Doctoral Fellows as the next generation of transdisciplinary social innovation leaders who are capable of ethically and responsibly developing state-of-the-art data science and artificial intelligence (AI) for social good. The network will bring together leading data science and AI researchers and experts with practitioners and experts in social innovation to conduct empirical research and cascading skill-building with the following objectives: 

Objective 1: Establish a research roadmap and framework grounded in empirical knowledge and best practices for data science/AI to power social innovation (Data/AI for Social Good). 

Objective 2: Demonstrate the potential and benefits across sectors of implementing social innovations powered by data science and AI through five demonstrator projects that showcase the impact on critical societal issues: Climate, Social justice, Democracy, and Health and Ageing.

Objective 3: Build capacity, Data/AI tools and methods for social innovators and entrepreneurs through an integrated open cascade training programme. 

Objective 4: Create new career pathways for data science and AI dedicated to social innovation. 

Objective 5: Provide guidance and support for practice about data science and AI for social innovation to stakeholders who contribute to social innovation: policymakers, funders, and governmental organisations and to data science and AI technology development. 

The Doctoral Candidate in the project Novel participation opportunities and social inclusion enabled through social innovation, data science and artificial intelligence in the welfare sector will play a central role in the network and will:

1) conduct a systematic review from a sociological perspective, analysing literature in different disciplines, from practice, actor and technological viewpoints, on social innovation in the welfare sector, focusing on how new social practices evolve under AI's influence and the roles of various stakeholders in AI development and implementation. Expected result: synthesis of the existing literature in different disciplines on social innovation in the welfare sector as a knowledge base for future research and practice

2) develop real-life, (quasi-) experimental case studies, informed by the review from task 1, to construct a novel theoretical approach on social innovation, and participatory processes in particular, linked to AI use and provision, thereby also uncovering challenges and opportunities offered through data science and AI for social innovation in the welfare sector, with a possible emphasis on participation opportunities and social inclusion. Expected result: real-life case studies of using AI in social innovation projects, processes or strategies in the welfare sector to generate a theoretically grounded perspective on participation opportunities and social inclusion through social innovation in connection with the use of data science and AI in the welfare sector

3) develop recommendations as a “handbook” of how to promote social inclusion and novel participation opportunities through social innovation and AI. Expected result: a handbook for promoting social inclusion and novel participation opportunities, providing practical guidance for practitioners in the welfare sector

Apart from that, the fellow will undertake two secondments at 1) the Platform for Social Innovations and Social Enterprises at Social Impact gGmbH to experience social innovation activities from a non-profit, welfare perspective including a focus on AI for good, and 2) Economic Development Agency Dortmund to experience project-oriented work specifically focused on social innovation in a public agency.

The Doctoral Candidate will be expected to:

• Report on findings by publishing scientific articles, resulting in a doctoral dissertation and oral dissertation defence
• Present findings at (inter)national meetings/conferences
• Contribute to the wider work of the Data2Action project
• Contribute to educational activities of the department and within the consortium.