How diverse is the ACII community? Analysing gender, geographical and business diversity of Affective Computing research

• URL: http://arxiv.org/abs/2109.07907v1
• Date: Sun, 12 Sep 2021 18:30:36 GMT
• Title: How diverse is the ACII community? Analysing gender, geographical and business diversity of Affective Computing research
• Authors: Isabelle Hupont and Song\"ul Tolan and Ana Freire and Lorenzo Porcaro and Sara Estevez and Emilia G\'omez
• Abstract summary: ACII is the premier international forum for presenting the latest research on affective computing. We measure diversity in terms of gender, geographic location and academia vs research centres vs industry, and consider three different actors: authors, keynote speakers and organizers. Results raise awareness on the limited diversity in the field, in all studied facets, and compared to other AI conferences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: ACII is the premier international forum for presenting the latest research on affective computing. In this work, we monitor, quantify and reflect on the diversity in ACII conference across time by computing a set of indexes. We measure diversity in terms of gender, geographic location and academia vs research centres vs industry, and consider three different actors: authors, keynote speakers and organizers. Results raise awareness on the limited diversity in the field, in all studied facets, and compared to other AI conferences. While gender diversity is relatively high, equality is far from being reached. The community is dominated by European, Asian and North American researchers, leading the rest of continents under-represented. There is also a strong absence of companies and research centres focusing on applied research and products. This study fosters discussion in the community on the need for diversity and related challenges in terms of minimizing potential biases of the developed systems to the represented groups. We intend our paper to contribute with a first analysis to consider as a monitoring tool when implementing diversity initiatives. The data collected for this study are publicly released through the European divinAI initiative.

Related papers

• The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention [61.80236015147771]
  We quantify the trade-off between using diversity interventions and preserving demographic factuality in T2I models. Experiments on DoFaiR reveal that diversity-oriented instructions increase the number of different gender and racial groups. We propose Fact-Augmented Intervention (FAI), which instructs a Large Language Model (LLM) to reflect on verbalized or retrieved factual information about gender and racial compositions of generation subjects in history.
  arXiv  Detail & Related papers  (2024-06-29T09:09:42Z)
• Diversity in Software Engineering Conferences and Journals [1.0432302605566328]
  We study the ethnic, gender, and geographical diversity of the authors published in Software Engineering conferences and journals. Across participants and committee members, there are some communities that are significantly lower in representation. There is no conclusive evidence that papers with White authors or male authors were more likely to be cited.
  arXiv  Detail & Related papers  (2023-10-24T19:17:30Z)
• Getting aligned on representational alignment [89.81370730647467]
  We study the study of representational alignment in cognitive science, neuroscience, and machine learning. There is limited knowledge transfer between research communities interested in representational alignment. We propose a unifying framework that can serve as a common language between researchers studying representational alignment.
  arXiv  Detail & Related papers  (2023-10-18T17:47:58Z)
• Stable Bias: Analyzing Societal Representations in Diffusion Models [72.27121528451528]
  We propose a new method for exploring the social biases in Text-to-Image (TTI) systems. Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts. We leverage this method to analyze images generated by 3 popular TTI systems and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents.
  arXiv  Detail & Related papers  (2023-03-20T19:32:49Z)
• Diversity dilemmas: uncovering gender and nationality biases in graduate admissions across top North American computer science programs [0.0]
  We study whether there is a preference for students' gender and nationality in the admission processes. Our findings show that there is no gender bias in the admission of graduate students to research groups, but we observed bias based on students' nationality.
  arXiv  Detail & Related papers  (2023-02-01T17:02:08Z)
• The Double-Edged Sword of Diversity: How Diversity, Conflict, and Psychological Safety Impact Software Teams [6.190511747986327]
  Team diversity can be seen as a double-edged sword, bringing cognitive resources to teams at the risk of increased conflict. This study views diversity through the lens of the categorization-elaboration model (CEM) We investigated how diversity in gender, age, role, and cultural background impacts team effectiveness and conflict.
  arXiv  Detail & Related papers  (2023-01-30T14:54:44Z)
• Investigating Participation Mechanisms in EU Code Week [68.8204255655161]
  Digital competence (DC) is a broad set of skills, attitudes, and knowledge for confident, critical and use of digital technologies. The aim of the manuscript is to offer a detailed and comprehensive statistical description of Code Week's participation in the EU Member States.
  arXiv  Detail & Related papers  (2022-05-29T19:16:03Z)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)
• Including Everyone, Everywhere: Understanding Opportunities and Challenges of Geographic Gender-Inclusion in OSS [15.757897147034873]
  This study presents a multi-region geographical analysis of gender inclusion on GitHub. Gender diversity is low across all parts of the world, with no substantial difference across regions. There has been statistically significant improvement in diversity worldwide since 2014, with certain regions such as Africa improving at faster pace.
  arXiv  Detail & Related papers  (2020-10-02T07:40:43Z)
• Simultaneous Relevance and Diversity: A New Recommendation Inference Approach [81.44167398308979]
  We propose a new approach, which extends the general collaborative filtering (CF) by introducing a new way of CF inference, negative-to-positive. Our approach is applicable to a wide range of recommendation scenarios/use-cases at various sophistication levels. Our analysis and experiments on public datasets and real-world production data show that our approach outperforms existing methods on relevance and diversity simultaneously.
  arXiv  Detail & Related papers  (2020-09-27T22:20:12Z)
• Measuring Diversity of Artificial Intelligence Conferences [0.0]
  We propose a small set of diversity indicators (i.e. indexes) of AI scientific events. These indicators are designed to quantify the diversity of the AI field and monitor its evolution. We evaluate the proposed indexes for a set of recent major AI conferences and we discuss their values and limitations.
  arXiv  Detail & Related papers  (2020-01-20T10:09:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.