On-screen Representation Evaluation Structure: Part 2
In a groundbreaking development, a conceptual framework for measuring on-screen representation has been presented, focusing on diversity aspects like presence, prominence, and portrayal. This framework is not only relevant for Equality Act protected characteristics, such as race and gender, but also extends to non-protected characteristics like socioeconomic diversity.
The framework suggests that moving from presence to prominence and portrayal can bring new value and prompt new questions in media analysis. To capture these aspects, computer vision can be effectively used.
For instance, visual saliency detection techniques can help determine which characters are most prominent on screen by identifying the most visually appealing or noticeable regions. Object detection and tracking methods can provide insights into character prominence and interaction by identifying specific characters or objects and tracking their movement over frames.
In terms of portrayal, computer vision can analyze facial expressions to understand the emotional portrayal of characters, providing insights into how they are represented. Body pose analysis can help in understanding physical portrayal, such as how characters are positioned or how they interact with their environment.
However, it is crucial to address potential biases and ethical considerations when using computer vision for media analysis. To ensure fairness, training datasets should be diverse and representative, and algorithms should be regularly audited for biases. Contextual understanding is essential to ensure that computer vision methods are applied within a context that respects the narratives being analyzed. Transparency and disclosure are also key, with the use of AI clearly disclosed and results presented in a way that respects the original intent and context of the on-screen representation.
Initiatives like "The Bigger Picture" aim to reimagine AI imagery, promoting a participatory approach to democratize the conversation around AI representation and ensuring that representations are inclusive and ethical.
Technologies like Microsoft Copilot Vision AI demonstrate the potential for real-time analysis of visual data, which can be adapted to study on-screen representations by analyzing visual narratives as data. Innovations in video representation, such as the GSVR method for efficient video processing, could enable faster analysis of large video datasets, facilitating a more comprehensive understanding of prominence and portrayal.
In the longer term, technical recommendations and data standards specific to representation metrics can be developed. It is also important to acknowledge the need for insights into the intersectional dynamics of underrepresented groups. Much more research is needed on when face detections are missed and the causes of this, as well as the factors that cause different faces to be mistaken as the same face.
The framework aims to compare different data compilation methods and complement qualitative discussions with quantitative analysis, contributing to a more nuanced understanding of on-screen representation. However, it is essential to remember that computer vision should not be used to infer demographic attributes. Instead, it can be used to speed up the identification of character occurrences in media analysis.
- The framework for measuring on-screen representation emphasizes the value of moving from character presence to prominence and portrayal, and this transition can be effectively tracked using skills in data-and-cloud-computing and technology like computer vision.
- Visual saliency detection and object detection methods are helpful in determining character prominence and interaction by providing evidence of specific characters or objects and their movement across frames.
- In terms of character portrayal, computer vision can analyze facial expressions and body pose to present evidence of emotional and physical portrayals, respectively.
- To ensure fair and ethical use of computer vision in media analysis, it's crucial to address potential biases by diversifying and representing training datasets, auditing algorithms for any biases, and providing a contextual understanding.
- Transparency, disclosure, and respectful presentation of results are essential for AI-driven media analysis, as demonstrated by initiatives like "The Bigger Picture" that promote inclusive and ethical AI representation.
- Technological advancements, such as Microsoft Copilot Vision AI, demonstrate the potential for real-time visual data analysis, which can be adapted for studying on-screen representations.
- As we look ahead, developing data standards specific to representation metrics, examining intersectional dimensions of underrepresented groups, and conducting more research on face detections and their errors will contribute to a more comprehensive understanding of on-screen representation in creative industries, enhancing education-and-self-development and overall learning.
