Vasileios Iosifidis

TL;DR: A broad multidisciplinary overview of the area of bias in AI systems is provided, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame.

TL;DR: In this article, a fairness-aware classifier based on AdaBoost is proposed, which further updates the weights of the instances in each boosting round taking into account a cumulative notion of fairness based upon all current ensemble members, while explicitly tackling class-imbalance by optimizing the number of ensemble members for balanced classification error.

TL;DR: The proposed FAE (Fairness-Aware Ensemble) framework combines fairness-related interventions at both pre-and post-processing steps of the data analysis process, tackling the problems of under-representation of the protected group and of class-imbalance by generating balanced training samples.

TL;DR: Wang et al. as discussed by the authors used a LSTM based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames, which outperformed the state-of-the-art auto regressive based model by 18.5% in precision and 15.4% in recall.

TL;DR: This paper describes Tweets KB, a publicly available corpus of currently more than 1.5 billion tweets, spanning almost 5 years (Jan’13–Nov’17), and presents use cases to illustrate scenarios for entity-centric information exploration, data integration and knowledge discovery facilitated by TweetsKB.