Decision Making with Sustainable, Fair and Trustworthy AI

Permanent URI for this collectionhttps://hdl.handle.net/10125/107426

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    Classifying Imbalanced Data: The Relevance of Accuracy and Feature Importance
    (2024-01-03) Widmann, Torben
    The use of AI and ML algorithms can only contribute successfully to data-driven decision making if the underlying data is of sufficiently good quality. However, the effort of ensuring good data quality (DQ) must be proportionate to the potential impact of poor DQ. In this work, we therefore investigate the impact of DQ defects on the common and challenging task of classifying imbalanced data. We contribute to theory and practice by being the first to investigate the impact of DQ according to the particular DQ dimension accuracy and by examining the relevance of the importance of attributes with respect to the classification. Underpinning the significance of DQ, our experiments show that already few inaccuracies can lead to a considerably worse classification, that efficient data cleaning can be limited to a few attributes, and that distance-based algorithms are more affected by defects in less important attributes.
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    A Preliminary Look at Generative AI for the Creation of Abstract Verbal-to-visual Analogies
    (2024-01-03) Combs, Kara; Bihl, Trevor
    Generative artificial intelligence (GAI) appears useful in the creation of new data, which assists in the expansion of small, limited datasets in fields such as analogical reasoning (AR). This multidisciplinary study expands the number of AR visual datasets within the field of visual question answering. We introduce the first visual analogy dataset that includes abstract concepts by leveraging three text-to-image GAI generators, Text2Img, Craiyon, and Midjourney, to produce images for antonym and synonym analogies. Our visual dataset achieves up to 70% accuracy and performs better 84.6% of the time compared to the same evaluation on only textual information. Interestingly, results also imply that paid GAI services produce higher accuracy. This work shows the potential for GAI to aid in the development of abstract visual analogy datasets, which allows for a better understanding and incorporation of AR into cognitive-inspired AI models capable of analogy-based information fusion.
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