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AI in Support of Diversity and Inclusion

Çiçek Güven, Afra Alishahi, Henry Brighton, Gonzalo Nápoles, Juan Sebastian Olier, Marie Šafář, Eric Postma, Dimitar Shterionov, Mirella De Sisto, Eva Vanmassenhove

TL;DR

The paper addresses how AI can support diversity and inclusion by examining transparency, bias, and accessibility in AI systems. It advocates a multidisciplinary approach and highlights concrete projects such as Child Growth Monitor and SignON to demonstrate real-world applications that address malnutrition, communication gaps, and media representation. Through initiatives like monitoring LGBTQ+ disinformation and co-creative sign-language technology development, the work emphasizes inclusive data practices, trustworthy explanations, and collaboration with affected communities. The overall message is that AI should be both effective and socially responsible, requiring diverse data, explainable decisions, and active stakeholder engagement to foster fairer human–machine interactions.

Abstract

In this paper, we elaborate on how AI can support diversity and inclusion and exemplify research projects conducted in that direction. We start by looking at the challenges and progress in making large language models (LLMs) more transparent, inclusive, and aware of social biases. Even though LLMs like ChatGPT have impressive abilities, they struggle to understand different cultural contexts and engage in meaningful, human like conversations. A key issue is that biases in language processing, especially in machine translation, can reinforce inequality. Tackling these biases requires a multidisciplinary approach to ensure AI promotes diversity, fairness, and inclusion. We also highlight AI's role in identifying biased content in media, which is important for improving representation. By detecting unequal portrayals of social groups, AI can help challenge stereotypes and create more inclusive technologies. Transparent AI algorithms, which clearly explain their decisions, are essential for building trust and reducing bias in AI systems. We also stress AI systems need diverse and inclusive training data. Projects like the Child Growth Monitor show how using a wide range of data can help address real world problems like malnutrition and poverty. We present a project that demonstrates how AI can be applied to monitor the role of search engines in spreading disinformation about the LGBTQ+ community. Moreover, we discuss the SignON project as an example of how technology can bridge communication gaps between hearing and deaf people, emphasizing the importance of collaboration and mutual trust in developing inclusive AI. Overall, with this paper, we advocate for AI systems that are not only effective but also socially responsible, promoting fair and inclusive interactions between humans and machines.

AI in Support of Diversity and Inclusion

TL;DR

The paper addresses how AI can support diversity and inclusion by examining transparency, bias, and accessibility in AI systems. It advocates a multidisciplinary approach and highlights concrete projects such as Child Growth Monitor and SignON to demonstrate real-world applications that address malnutrition, communication gaps, and media representation. Through initiatives like monitoring LGBTQ+ disinformation and co-creative sign-language technology development, the work emphasizes inclusive data practices, trustworthy explanations, and collaboration with affected communities. The overall message is that AI should be both effective and socially responsible, requiring diverse data, explainable decisions, and active stakeholder engagement to foster fairer human–machine interactions.

Abstract

In this paper, we elaborate on how AI can support diversity and inclusion and exemplify research projects conducted in that direction. We start by looking at the challenges and progress in making large language models (LLMs) more transparent, inclusive, and aware of social biases. Even though LLMs like ChatGPT have impressive abilities, they struggle to understand different cultural contexts and engage in meaningful, human like conversations. A key issue is that biases in language processing, especially in machine translation, can reinforce inequality. Tackling these biases requires a multidisciplinary approach to ensure AI promotes diversity, fairness, and inclusion. We also highlight AI's role in identifying biased content in media, which is important for improving representation. By detecting unequal portrayals of social groups, AI can help challenge stereotypes and create more inclusive technologies. Transparent AI algorithms, which clearly explain their decisions, are essential for building trust and reducing bias in AI systems. We also stress AI systems need diverse and inclusive training data. Projects like the Child Growth Monitor show how using a wide range of data can help address real world problems like malnutrition and poverty. We present a project that demonstrates how AI can be applied to monitor the role of search engines in spreading disinformation about the LGBTQ+ community. Moreover, we discuss the SignON project as an example of how technology can bridge communication gaps between hearing and deaf people, emphasizing the importance of collaboration and mutual trust in developing inclusive AI. Overall, with this paper, we advocate for AI systems that are not only effective but also socially responsible, promoting fair and inclusive interactions between humans and machines.
Paper Structure (5 sections, 2 figures)

This paper contains 5 sections, 2 figures.

Table of Contents

  1. AI's blessings and threats
  2. Transparancy of AI
  3. Identifying and resolving biases in AI
  4. Facilitating accessibility and empowering diversity
  5. The broader picture

Figures (2)

  • Figure 1: This map shows the location of residential internet connections we used to monitor LGBTQ+ disinformation in The Netherlands, The United Kingdom, Germany, France, Poland, The Czech Republic, Slovakia, and Hungary. The large red circles show the 24 regional locations where we focused our monitoring, while the small blue circles show the locations of the individual internet connections we used. We have perturbed these locations to protect anonymity.
  • Figure 2: Co-creation process