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This document serves as a preface to a special issue of the International Journal on Artificial Intelligence Tools (IJAIT). The articles in this issue build upon papers presented at the 36th International Florida AI Research Society Conferences in May 2023. The lead article introduces the theme of the special issue, which is explainable, fair, and Trustworthy AI. Other articles in the issue cover topics such as improving the bounds of a neuron's Boolean function, ethical explanations and transparency in AI systems, fairness measurement and mitigation in deep learning systems, and the dynamics of human-agent collaboration in ad hoc teams. The paper emphasizes the importance of positive and negative contrastive explanations in enhancing motivation and performance. It also addresses the challenges of fairness and reliability in predictive policing systems, with a focus on reducing bias related to race, age, and sex. The authors propose tailoring predictive models to specific causal queries and utilizing causal structure instead of relying solely on pre-trained models. The paper also explores how machine learning models can balance accuracy and understandability, as well as ensure fairness. The findings indicate that more complex models tend to outperform current guidelines, but fair models have lower complexity. The paper concludes by expressing gratitude to the reviewers for their contributions and support. [Extracted from the article]

Retail demand forecasting is a highly intricate and multilevel problem as it may include numerous products, under multiple categories, and potentially sold in many different stores. Forecasts may also process products in isolation or collectively so as to detect mutual correlations. In technical terms, retail demand forecasting may be broken down to a multivariate time series forecasting problem where the time axis is prevalent and introduces added complexity which necessitates proper usage of advanced methodologies with machine/deep learning backgrounds. Popular boosting regressors, such as XGBoost and LightGBM, are excellent machine learning candidates with extensive bibliographic backgrounds covering multiple scientific fields and applications including generic multivariate time series forecasts. The Temporal Convolutional Network and the Temporal Fusion Transformer are recently proposed deep neural network architectures that found success in specific multivariate scenarios but are yet to be tested in a variety of related fields such as retail demand forecasting. Therefore, within this paper, these novel in-the-field models are compared to the aforementioned boosting approaches alongside popular statistical univariate methods, namely Exponential Smoothing and SARIMA. To properly compare the selected models and attempt to generalize their usability, two separate datasets are analyzed and forecasted; an item sales dataset with 500 time series and a category sales dataset with 540 time series. The findings suggest that the deep learning solutions are the better predictors, with the Transformer model surpassing the Boosting solutions by up to 16.8% sMAPE decrease and the statistical approaches by up to 28.6% decrease, further substantiating the notion that deep learning techniques are exceptionally promising in handling large-scale, non-linear and outlier data whilst suggesting that retail demand forecasting does benefit from multivariate approaches and the application of advanced machine learning methods. [ABSTRACT FROM AUTHOR]