Exploring the Use of Generative Artificial Intelligence for Bias Mitigation
Description
Artificial intelligence (AI) and related topics are growing in popularity across various industries as there is a desire to improve the accuracy and efficiency of decision-making processes. The algorithms at the foundation of such technologies are initially developed and trained on datasets, then implemented in real-world applications that can directly affect humans, such as in healthcare, criminal justice, and finance. However, with this power comes the potential for a lack of fairness, an issue that is becoming a prominent concern in the realm of AI research. While the development of AI technology is on the rise, there is a need to ensure that the algorithms are constructed in a way that eradicates the influence of unfairness and bias, especially when considering the potential of outcomes that negatively impact marginalized communities. One approach that has been considered is the usage of generative AI, such as generative adversarial networks (GANs). The tools can not only be employed to generate data for computer vision problems, especially when data is limited and/or difficult to obtain, but also address concerns surrounding fairness and bias in AI. Additionally, the tools can potentially be implemented in COVID-19 research to better understand the virus’s impact in marginalized communities