King's College London

The main objectives are to:

• explore the critical factors that impact how people engage with bottom-up quality journalism, science education and digital citizenship;
• define a participatory, inclusive innovation programme, leveraging impulses from multiple disciplines, as well as synergies between entrepreneurs and creatives;
• organise a competition addressing pressing technical, economic and societal challenges in the media value chain to identify promising digital entrepreneurs, creatives and data-empowered solutions;
• provide data and experimentation facilities for the winners of this competition to test and nurture their ideas;
• support 51 businesses and 43 artists by solving common concerns around funding and access to mentoring in technical, legal, business, media and sustainability matters; and
• create toolkits and best practices for innovators, creatives, and other stakeholders to achieve greater traction for their citizen-centric initiatives, and empower them to communicate through data in inspiring, informative and engaging ways.

MediaFutures not only supports artists and start-ups in their individual and combined efforts to tackle misinformation; we also seek to derive insight from their experiences in doing so, both within and beyond our support programme. And what would be better to gather this insight, than to actually talk to our participants? Our team is currently in the process of speaking to all of our artists and start-ups, discussing their experience in our programme, the highlights and lowlights of their engagement, what they learned, and what surprised them.

We learned that our participants came into the programme with very different expectations, primarily depending on which of the tracks they were in: Our Artists for Media were prepared for an artistic residency, and positively surprised by the organisational structure we provided; our start-ups for citizens came prepared for a classic accelerator, and enjoyed the learning and networking opportunities afforded by the programme. Our start-up meets artists teams were very open to the new experience, and highlighted the importance of talking through plans and the different perspectives throughout the project, to make sure that all sides of the collaboration can contribute based on their expertise.

A specific focus in our conversations lies on the role of interdisciplinarity: What kinds of disciplines do our participants touch upon, how do they bridge gaps in knowledge or language in order to create fully rounded artworks or products. So far, we found that our artists especially have extensive networks that allow them to source a lot of insight from within their community. External expertise that they bring in tends to be on specific subject areas, such as philosophy, rather than around technical skills.

Another area we are especially interested in is our participants' use of data, and how this evolved throughout their work with MediaFutures. Most of our interviewees were excited to learn more about data ethics, as well as legal aspects of data, both in terms of personal data and GDPR, but also around intellectual property rights and ownership models. They highly valued the training provided by MediaFutures in these areas.

But our research is not only focused on qualitative insights: we also work on a study investigating ways to model the factors that can predict the success of applications in innovation programmes like MediaFutures. The growing availability of administrative data from open innovation programmes is creating new opportunities for enhancing and auditing their selection practices. Previous research has explored the use of past data to build predictive models for shortlisting applicants and allocating resources more efficiently during review. Another avenue of research relates to interpreting the model parameters to ensure that the trends detected in past data align with the intended objectives of public funders.

The whitepaper the team is working on presents a quantitative investigation of open call applications and selection decisions from three EU-funded data incubators that operated between the years of 2016 to 2021. Using data from 725 applications received by the DMS Accelerator, DataPitch and ODINE programmes, it presents a methodological approach for quantifying unstructured aspects of application texts and team characteristics, and for measuring their explanatory power in predicting the acceptance or rejection of an applicant. It also discusses tools for obtaining demographic metrics from applicants’ names where explicit data on equality, diversity and inclusion (EDI) are unavailable.

Based on our analysis, we find that the use of past data for predictive modelling is not yet a viable option. We attribute this to the difficulty of quantifying subjective selection criteria and the shortage of data that capture successful applicants. Nonetheless, we were able to detect statistically significant trends in the existing data and to audit the parameters that contributed to companies’ chances of being selected. We found that disciplinary diversity and longer application answers consistently contributed to acceptance decisions. This suggests a strong preference for diverse expertise within teams and companies that make the effort to submit informative responses at application stage.

Other metrics such as the linguistic content of applications, team size and demographic diversity did not appear to be associated with selection decisions. Of these, demographic diversity is worthy of further attention. Although we were not able to detect any overt biases against teams containing women and non-European ethnicities, we were also unable to prove a selective preference for teams that were inclusive of this kind of diversity. We estimate that only 19% of individuals named inside applications were women, and that 19% came from ethnic minorities (11% Asian, 8% African). Our findings underscore the European Commission’s ongoing concern with pursuing better representation of women and ethnic minorities in industries affiliated with data and AI.

We conclude the paper with a summary of methodological implications and recommendations for other innovation programmes and researchers who may be considering the use of machine learning approaches for predictive or auditing purposes in the selection process.

In practical terms, this work has contributed to the design of MediaFutures' second open call, and the inclusion of EDI details in application forms.

We will combine all of these insights, and the expertise of our whole consortium, in the MediaFutures toolkit, the first version of which will be published next year.