The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

From the innovation, Artificial Intelligence (AI) materialized as one of the noticeable research
areas in various technologies and has almost expanded into every aspect of modern human …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

Executive summary In the light of the recent advances in artificial intelligence (AI), the
challenges posed by its use in an everincreasing number of areas have serious implications …

The increasing use of data-driven decision support systems in industry and governments is
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …

Errors are prevalent in time series data, which is particularly common in the industrial field.
Data with errors could not be stored in the database, which results in the loss of data assets …

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research
and healthcare. This position paper shares the results of the international conference “Fair …

Artificial intelligence (AI) offers much promise for improving healthcare. However, it runs the
looming risk of causing individual and societal harms; for instance, exacerbating inequalities …

HCI engages with data science through many topics and themes. Researchers have
addressed biased dataset problems, arguing that bad data can cause innocent software to …

Data integration has been extensively studied by the data management community and is a
core task in the data pre-processing step of ML pipelines. When the integrated data is used …