PERSONALIZED EXPLANATION FOR MACHINE LEARNING: A CONCEPTUALIZATION

Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personalized explanation by defining and structuring the problem based on prior work on machine learning explanation, personalization (in machine learning) and concepts and techniques from other domains such as privacy and knowledge elicitation. We perform a categorization of explainee data used in the process of personalization as well as describing means to collect this data. We also identify three key explanation properties that are amendable to personalization: complexity, decision information and presentation. We also enhance existing work on explanation by introducing additional desiderata and measures to quantify the quality of personalized explanations. Keywords: Explainable artificial intelligence, Interpretable machine learning, Personalization, Customization, Interactive machine learnin

Exploring the Use of Backgrounds in Web-conferencing with Image and Text Analysis

The COVID-19 pandemic accelerated the implementation and adoption of new features in web-conferencing systems (WCSs), such as custom backgrounds (CBs) that mask the real physical background with a custom, i.e., user chosen, background. In this work, we explore what types of backgrounds are selected and why they are used by analyzing text and images from Twitter. We find that different types of CBs allow users to satisfy psychological needs in virtual collaboration and identify emerging practices regarding the selection of backgrounds, in general, and with respect to gender differences. Our analysis reveals that CBs showing real objects get commonly replaced or augmented by artificial, non-photorealistic content such as cartoon style memes, scenes of computer games or company logos. By leveraging novel image analysis techniques, we also contribute methodologically to social media analytics

INDUSTRY DEMAND FOR ANALYTICS: A LONGITUDINAL STUDY

Data analytics has become an important part of companies in industry, leading to an increase in the demand for analytics experts. Data analytics is also a central aspect of trends such as “Big Data”, “Data Science”, “Artificial Intelligence”. As such, for potential analytics professionals to have relevant job skills, educational institutions need to have an up-to-date picture of the demanded job skills. We studied the recent past to better anticipate future developments of analytics job skills through a study based on text mining using job advertisements from 2014 and 2019 summing up to a total of 17,282 advertisements. We investigated how these trends evolved by looking at meaning of words using associated words as well as topics obtained from topic modeling. Our longitudinal study reveals a shift in prevalence and meaning of analytics trends in the industry. It shows a small shift from business oriented job skills towards analytical and technical ones. This finding is accompanied by increasing popularity of “Big Data”, in contrast with the reduced popularity of “Business Intelligence”. Additionally, we observed how “Machine Learning” is becoming more strongly associated with the field of data science itself