Artificial intelligence (AI) images, like those produced by DALL-E, have seen explosive growth in the past several years and have the potential to disrupt numerous markets. While the technology offers exciting opportunities for creativity and innovation, it also raises important ethical considerations that must be addressed. These ethical implications include issues related to bias and discrimination, privacy, job displacement, and unintended consequences. To mitigate these challenges, a multi-disciplinary approach is needed, including the development of effective regulations and governance frameworks, the creation of unbiased algorithms, responsible data management practices, and educational and training programs. Additionally, encouraging ethical discussions and debates is crucial in ensuring the responsible use of AI-generated images. While AI-generated images offer many benefits, it is important to consider the ethical implications and work towards responsible AI practices to ensure their benefits are realized by society as a whole.

No abstract

Standard classification algorithms often face a challenge of learning from imbalanced datasets. While several approaches have been employed in addressing this problem, methods that involve oversampling of minority samples remain more widely used in comparison to algorithmic modifications. Most variants of oversampling are derived from Synthetic Minority Oversampling Technique (SMOTE), which involves generation of synthetic minority samples along a point in the feature space between two minority class instances. The main reasons these variants produce different results lies in (1) the samples they use as initial selection / base samples and the nearest neighbors. (2) Variation in how they handle minority noises. Therefore, this paper presented different combinations of base and nearest neighbor's samples which never used before to monitor their effect in comparison to the standard oversampling techniques. Six methods; three combinations of Only Danger Oversampling (ODO) techniques, and three combinations of Danger Noise Oversampling (DNO) techniques are proposed. The ODO's and DNO's methods use different groups of samples as base and nearest neighbors. While the three ODO's methods do not consider the minority noises, the three DNO's include the minority noises in both the base and neighbor samples. The performances of the proposed methods are compared to that of several standard oversampling algorithms. We present experimental results demonstrating a significant improvement in the recall metric.