Description: The ‘non-inclusivity’ in the context of artificial intelligence (AI) refers to the lack of representation and consideration of diverse groups during the development and training of AI systems. This deficiency can lead to algorithms that perpetuate biases and discrimination, negatively impacting communities that are not adequately represented. Non-inclusivity can manifest in various forms, such as the underrepresentation of ethnic minorities, genders, or differently-abled individuals in the datasets used to train AI models. This is problematic because AI systems, when trained on biased data, may make decisions that reinforce stereotypes or are unfair to users. Ethics in AI demands that developers be aware of diversity and inclusion, not only to avoid biases but also to ensure that systems are fair and equitable. The lack of inclusivity not only affects the quality of the results generated by AI but can also have significant social repercussions, such as perpetuating inequalities and distrust in technology. Therefore, addressing non-inclusivity is crucial for the development of responsible and ethical AI systems that benefit society as a whole.
