Inadequacies in Empirical Foundations
In the realm of UK television broadcasting, the pursuit of diversity and inclusion has been a consistent focus for initiatives such as the Creative Diversity Network's Project Diamond and Ofcom's annual diversity reports. These efforts aim to collect and analyse diversity data to foster a more representative and inclusive media landscape.
However, challenges persist, particularly in the collection of data on disability and certain demographic groups. A significant issue is the possibility of reporting bias due to low response rates in data collection exercises. To address these gaps, computer vision technology offers a promising solution.
By leveraging advanced visual recognition, localization, and vision-language models, computer vision can evaluate on-screen representation not just by presence (whether a demographic group appears), but by prominence (how central or visually emphasized that group is). This shift allows for a more nuanced measurement of diversity and representation in media content.
One key aspect of this approach is the localization and quantification of on-screen visual elements. Advanced computer vision techniques, such as object recognition and visual grounding, enable precise detection and localization of persons or UI elements within complex visual environments. This localization is crucial to measure prominence, going beyond simple presence.
Another challenge is the limited availability of large, diverse, annotated datasets. Enhancing datasets with varied demographics and lighting conditions can help generalize visual models and reduce bias, making automated prominence evaluation fairer and more accurate.
Computer vision models can also capture subtle visual cues that indicate prominence, such as face size, brightness, focus, occlusion, and screen position. Explanatory tools like Grad-CAM can highlight exactly what visual features influence prominence classification, improving trust and methodological transparency in diversity studies.
In terms of scalability, efficient models such as FastVLM enable on-device, real-time recognition and analysis of high-resolution screen content, necessary for analysing prominence in dynamic video or user interface media in scalable diversity audits.
This approach addresses longstanding issues in diversity evaluation, shifting the focus from whether representation exists to how prominently it is featured. The employment of fine-tuned vision-language models for precise element grounding and prominence scoring, the expansion and diversification of training datasets, the utilisation of explainability to validate prominence measures, and the leveraging of efficient vision encoders for real-time, scalable analysis are all integral parts of this transformation.
As the world of AI continues to evolve, there is room for socially-minded researchers to build systems that apply computer vision to the domain responsibly. The UK's departure from the EU has also brought about changes in the way British firms trade and work with European counterparts, as detailed in a report on post-Brexit migration and accessing foreign talent in the Creative Industries.
The new scoping study by the BFI focuses on the economic consequences and potential market failures of overseas mergers and acquisitions in the UK video games industry. Meanwhile, the BFI's evidence review found that sexual orientation and religion and belief were seldom explored in detail in research publications about workforce diversity. There is a need for more lead roles and ownership of narrative for underrepresented groups for film to be properly representative.
In conclusion, the application of computer vision technology in UK diversity initiatives has the potential to revolutionise the way we measure and understand on-screen representation, moving beyond presence to prominence. By filling data gaps and improving methodological rigor, computer vision can provide a more comprehensive and nuanced understanding of diversity in the media landscape.
- To address data gaps in UK television broadcasting, particularly concerning disability and certain demographic groups, computer vision technology could offer a promising solution, leveraging advanced visual recognition and prominence evaluation.
- By using sophisticated models like FastVLM for on-device, real-time recognition and analysis of high-resolution screen content, computer vision can provide scalable diversity audits, focusing not just on presence but also on prominence.
- Localization and quantification of on-screen visual elements, achieved through advanced computer vision techniques such as object recognition and visual grounding, can help measure prominence beyond simple presence, providing more nuanced diversity measurements.
- Research focused on the BFI's economic consequences and potential market failures of overseas mergers and acquisitions in the UK video games industry reveals an untold story about underrepresented groups in the creative industries and the need for more lead roles and ownership of narratives for such groups.
- In the field of data-and-cloud-computing and artificial intelligence, socially-minded researchers can build systems that apply computer vision to diversity initiatives responsibly, filling data gaps and providing a more comprehensive understanding of the media landscape.
- Addressing the lack of detailed research on sexual orientation and religion and belief in workforce diversity, more evidence is needed to ensure proper representation in media content, especially for underrepresented groups in the creative arts and education industries.
