Using Explainability to Help Children UnderstandGender Bias in AI

Abstract

Machine learning systems have become ubiquitous into our society. This has raised concerns about the potential discrimination that these systems might exert due to unconscious bias present in the data, for example regarding gender and race. Whilst this issue has been proposed as an essential subject to be included in the new AI curricula for schools, research has shown that it is a difficult topic to grasp by students. We propose an educational platform tailored to raise the awareness of gender bias in supervised learning, with the novelty of using Grad-CAM as an explainability technique that enables the classifier to visually explain its own predictions. Our study demonstrates that preadolescents (N=78, age 10-14) significantly improve their understanding of the concept of bias in terms of gender discrimination, increasing their ability to recognize biased predictions when they interact with the interpretable model, highlighting its suitability for educational programs.

AI literacy curriculum and its relation to children's perceptions of robots and attitudes towards engineering and science: An intervention study in early childhood education

“…While AI can bring many benefits, it can also have negative effects on children. For example, exposure to AI algorithms that perpetuate biases can lead to children internalizing and perpetuating those biases (Melsi on et al, 2021).…”

“…While AI can bring many benefits, it can also have negative effects on children. For example, exposure to AI algorithms that perpetuate biases can lead to children internalizing and perpetuating those biases (Melsión et al, 2021). Additionally, excessive screen time and dependence on technology can negatively impact children's social and emotional development (Radesky & Christakis, 2016).…”

Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review

“…Very few studies have simultaneously considered the primary and secondary school levels. For example, three studies [9,36,37] targeted students in both upper primary and lower secondary schools, whereas only one included students from lower primary to lower secondary school levels. Although AI education is traditionally considered to be suitable for older adolescents, numerous teaching units are available for the primary and even preschool levels.…”

“…One teaching unit in Finland is implemented as a part of regular curricular activities [40][41][42]; one teaching unit in Spain involves AI education in regular middle school classes [44]; and one teaching unit in Denmark critically emphasizes the social impact of AI and is taught in regular social studies classes [43]. Five studies involved AI education conducted wholly online or in a blended mode in response to the COVID-19 pandemic [34,36,[45][46][47]. None of the studies employed asynchronous online teaching.…”

'Don't make assumptions about me!': Understanding Children's Perception of Datafication Online

“…In fact, only recently have efforts at digital literacy begun to explore AI/algorithmic literacy [49]. However, such efforts were still mainly focused on broader concepts, such as social robotics [9,41], AI [58] and machine intelligence [22]. Children's perceptions of agents powered by AI algorithms have been shown to be strongly influenced by their previous interactions with them [80].…”