In recent years, computer vision has made significant strides in enabling machines to perform a wide range of tasks, from image classification and segmentation to image generation and video analysis. It is a rapidly evolving field that aims to enable machines to interpret and understand visual information from the environment. One key task in computer vision is image classification, where algorithms identify and categorize objects in images based on their visual features. Image classification has a wide range of applications, from image search and recommendation systems to autonomous driving and medical diagnosis. However, recent research has highlighted the presence of bias in image classification algorithms, particularly with respect to human-sensitive attributes such as gender, race, and ethnicity. Some examples are computer programmers being predicted better in the context of men in images compared to women, and the accuracy of the algorithm being better on greyscale images compared to colored images. This discrepancy in identifying objects is developed through correlation the algorithm learns from the objects in context known as contextual bias. This bias can result in inaccurate decisions, with potential consequences in areas such as hiring, healthcare, and security. In this paper, we conduct an empirical study to investigate bias in the image classification domain based on sensitive attribute gender using deep convolutional neural networks (CNN) through transfer learning and minimize bias within the image context using data augmentation to improve overall model performance. In addition, cross-data generalization experiments are conducted to evaluate model robustness across popular open-source image datasets.
Explainable Artificial Intelligence (XAI) is the capability of explaining the reasoning behind the choices made by the machine learning (ML) algorithm which can help understand and maintain the transparency of the decision-making capability of the ML algorithm. Humans make thousands of decisions every day in their lives. Every decision an individual makes, they can explain the reasons behind why they made the choices that they made. Nonetheless, it is not the same in the case of ML and AI systems. Furthermore, XAI was not wideley researched until suddenly the topic was brought forward and has been one of the most relevant topics in AI for trustworthy and transparent outcomes. XAI tries to provide maximum transparency to a ML algorithm by answering questions about how models effectively came up with the output. ML models with XAI will have the ability to explain the rationale behind the results, understand the weaknesses and strengths the learning models, and be able to see how the models will behave in the future. In this paper, we investigate XAI for algorithmic trustworthiness and transparency. We evaluate XAI using some example use cases and by using SHAP (SHapley Additive exPlanations) library and visualizing the effect of features individually and cumulatively in the prediction process.
Machine learning algorithms are being widely used in different fields such as image recognition, speech recognition, traffic and weather prediction, recommendations, spam filtering, self-driving cars, stock market prediction, medical diagnosis, and more. The ability of machines to feed in years of data and predict the outcome has helped humans in unimaginable ways. Machine learning has automated half of human work requiring very little human intervention and saving time and energy. However, sometimes individuals end up paying the price and falling victim to the unfair and biased outcome of machine learning algorithms. Machines learn through data what they are provided with, but the data that machines learn from does not come free from human biases. Human biases based on race, sex, ethnicity, skin color, and other sensitive attributes are reflected in the dataset which, when fed to the machine, results in a similar biased prediction. The years of data represent the bias that has been present in society, and the machine learning model simply mimics the pattern. There has been constant research and experiments being done on how to prevent these biases from reflecting on the prediction. In this paper, we will investigate if there is any bias present in the benchmark Statlog “Australian Credit Approval” dataset and take necessary measures to mitigate the bias present in the data. The paper shows how the AIF360 tool can identify and mitigate bias in the data and eventually in the learning algorithms.
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