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Addressing Bias in AI

Explores bias in AI and strategies for educators to teach about these issues.

Addressing Bias in AI

The emergence of AI technology has brought about groundbreaking changes in various sectors, including education. However, one critical issue that remains is the bias inherent in AI systems. This article delves into the limitations of current training datasets, the types of bias present in generative AI models, and strategies for educators to teach students about managing biased outputs.

Limitations of Today’s Training Datasets

Modern AI training datasets are predominantly trained on Western datasets, which means the cultures, languages, and perspectives lead to a lack of diversity in the AI's understanding and outputs. Such datasets may not adequately represent global cultural and linguistic diversity, which can skew AI-generated content and decision-making processes.

Types of Bias in Generative AI Models

Generative AI models can display various biases, which are often a reflection of the biases present in their training data. These biases can manifest in different ways, influencing the AI's language, content generation, and decision-making processes.

  • Racial Bias: One significant concern is racial bias. AI systems trained on datasets that lack racial diversity or are skewed towards certain racial groups can perpetuate stereotypes and unequal representation. This can lead to biased content generation (particularly from image generators) and decision-making that favours certain racial groups over others.

  • Gender Bias: Similarly, gender bias is a prevalent issue. If AI training data is biased towards a particular gender, it can result in AI systems that reinforce gender stereotypes and inequalities. This can manifest in language use, content recommendations, and even job screening processes conducted by AI.

For further understanding, watch this video published by the London Interdisciplinary School: 

The Potential Harm of Bias in AI

Bias in AI is not just a technical problem; it has real-world implications. Biased AI can reinforce societal inequalities, perpetuate stereotypes, and lead to unfair outcomes in various domains like employment, legal decisions, and social interactions. It’s particularly harmful as it can give a false veneer of objectivity to biased viewpoints.

Teaching Students about Managing Biased Outputs

Educators have a crucial role in teaching students how to manage and critically evaluate biased outputs from AI systems:

  • Critical Thinking: Encourage students to question AI outputs and consider whether they might be biased.

  • Diversity Awareness: Teach the importance of diversity and representation in data and how its absence can lead to biased AI.

  • Active Exploration: Engage students in activities that expose AI biases, such as comparing AI responses for different demographics.

  • Ethical Considerations: Discuss the ethical implications of AI bias and the importance of developing AI responsibly.


As AI continues to evolve and integrate into our daily lives, it's essential to address the issue of bias head-on. Educators are uniquely positioned to raise awareness and educate the next generation on recognising and managing bias in AI. By doing so, they can contribute to the development of more equitable and responsible AI systems in the future.

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