Dr. Qianlong Wang Profile

Dr. Qianlong Wang

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Proceedings Article | 7 June 2024 Presentation + Paper

A comparison of causal discovery and explainable AI (XAI) for image datasets

Atul Rawal, Adrienne Raglin, Qianlong Wang, Ziying Tang

Proceedings Volume 13051, 130510H (2024) https://doi.org/10.1117/12.3013193

KEYWORDS: Machine learning, Artificial intelligence, Image classification, Deep learning

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The recent push for fair, trustworthy, and responsible Artificial Intelligence (AI) and Machine Learning (ML) systems have pushed for more explainable systems that are capable of explaining their predictions/decisions and inner workings. This led to the field of Explainable AI (XAI) going through an exponential growth in the past few years. XAI has been crucial in making AI/ML systems more comprehensible. However, XAI is limited to the model that it is being applied to, for both post-hoc or transparent models. Even though XAI can explain the decisions being made by the ML systems, these decisions are based on correlation and not causation. For applications such as tumor classification in the medical field, this can have serious consequences as people’s lives are affected. A potential solution for this challenge is the application of causal learning, which goes beyond the limitations of correlation for ML systems. Causal learning can generate analysis based on cause-and-effect relations within the data. This study compares the results of explanations given by post-hoc XAI systems to the causal features derived from causal graphs via causal discover for image datasets. We investigate how well XAI explanations/interpretations are able to identify the pertinent features within images. Causal graphs are generated for image datasets to extract the causal features that have a direct cause- and-effect relation with the label. These features are then compared to the features highlighted by XAI via feature relevance. The addition of causal learning for image datasets can aide in achieving fairness, bias detection, and mitigation to provide a robust and trustworthy system. We highlight the limitation of XAI tools such as LIME to make predictions based on physical features from images, whereas causal discovery can go beyond the simple pixel based perturbations to identify causal relations from image attributes.

Proceedings Article | 7 June 2024 Presentation + Paper

Causal discovery for image datasets via feature extraction

Atul Rawal, Adrienne Raglin, Qianlong Wang, Ziying Tang

Proceedings Volume 13051, 130510J (2024) https://doi.org/10.1117/12.3013191

KEYWORDS: Machine learning, Feature extraction, Artificial intelligence

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Artificial reasoning systems via Artificial Intelligence (AI) and Machine Learning (ML) have made tremendous progress within the past decade. AI/ML systems have been able to reach unprecedented new levels of autonomy for a multitude of applications ranging from autonomous vehicles to biomedical imaging. This new level of intelligence and freedom for AI/ML systems requires them to have a degree of human-like intelligence in terms of causation beyond the correlation. This, however, has remained a major challenge for investigators when combining causality with AI/ML systems. AI/ML systems that are capable of generating cause and effect relationships are still in their infancy, as the literature highlights. The lack of investigations for causal reasoning systems that are capable of using datasets other than tabular data is well highlighted within literature. Causal learning for image, audio, video, radio-frequency, and other modalities still remain a major challenge. While there are open-source tools available for causal learning with tabular data, there is a lack of tools for other modalities. To this extent, this study proposes a causal learning method with image datasets by using existing tools and methodologies. Specifically, we propose to use existing causal discovery toolboxes for investigating causal relations within image datasets by converting image datasets into tabular form with feature extraction using tools such as auto-encoders and deep neural networks. The converted dataset can then be used to generate causal graphs by using tools such as the Causal Discovery Toolbox to highlight the specific cause and effect relations within the data. For AI/ML systems using causal learning for image datasets via existing tools and methodologies can provide an extra layer of robustness to ensure fairness and trustworthiness.

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