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Abstract
Artificial intelligence (AI) is rapidly gaining prominence as a problem-solving approach in a wide variety of disciplines, notably due to deep learning's (DL) recent breakthroughs. AI is the simulation of human intelligence in machines that are programmed to think and act like humans. DL is a subfield of AI that deals with algorithms inspired by the biological structure and functioning of the brain to help machines with intelligence.
This paper introduces a robust framework for tackling biases in AI and DL systems, incorporating synthetic data augmentation and critical theories. Biases deeply embedded in society are often perpetuated by AI systems trained on biased data, highlighting the need for solutions that go beyond statistical properties.
The reinforcement of hegemonic power in the development of DL highlights the amplification of biases within AL By combining synthetic data augmentation and critical theories, this framework advances the responsible and inclusive development of AI technologies. It empowers AI systems to mitigate biases, contributing to fair and equitable outcomes. The proposed framework offers a practical and rigorous approach to address biases in Al, paving the way for a more ethical and unbiased AI landscape.
Introduction
Historically, societal biases have been deeply ingrained in systems and decision-making processes, consciously or subconsciously shaping outcomes and experiences. The creation and implementation of policies, laws, and societal norms have been influenced by these biases, which in turn, have been informed by power dynamics, structural injustices, and historical contexts. Over time, these biases have become embedded in societal structures, creating and perpetuating disparities and systemic inequalities. With the advent of Artificial Intelligence (AI) and machine learning, these biases have found a new medium (Kirkpatrick, 2017). When AI systems are trained on data that reflects these societal biases, they learn to replicate and potentially even amplify these biases (Benjamin, 2020). The challenge of biased AI outcomes is a reflection and continuation of the historical encoding of societal biases into systems and decision-making processes (Howard & Borenstein, 2018).
Addressing the problem of biased data is, therefore, not just a technical challenge, but a socio-technical one. It requires solutions that do not merely focus on the data's numerical or statistical properties but also consider the deeper social and cultural contexts that this data represents (Hall & Ellis,...