%0 Conference Paper %B Machine Learning and Knowledge Discovery in Databases %D 2020 %T Black Box Explanation by Learning Image Exemplars in the Latent Feature Space %A Riccardo Guidotti %A Anna Monreale %A Matwin, Stan %A Dino Pedreschi %E Brefeld, Ulf %E Fromont, Elisa %E Hotho, Andreas %E Knobbe, Arno %E Maathuis, Marloes %E Robardet, Céline %X We present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by “morphing” into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability. %B Machine Learning and Knowledge Discovery in Databases %I Springer International Publishing %C Cham %8 2020// %@ 978-3-030-46150-8 %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-46150-8_12 %R https://doi.org/10.1007/978-3-030-46150-8_12 %0 Journal Article %J PLoS One %D 2015 %T Participatory Patterns in an International Air Quality Monitoring Initiative. %A Alina Sirbu %A Becker, Martin %A Saverio Caminiti %A De Baets, Bernard %A Elen, Bart %A Francis, Louise %A Pietro Gravino %A Hotho, Andreas %A Ingarra, Stefano %A Vittorio Loreto %A Molino, Andrea %A Mueller, Juergen %A Peters, Jan %A Ricchiuti, Ferdinando %A Saracino, Fabio %A Vito D P Servedio %A Stumme, Gerd %A Theunis, Jan %A Francesca Tria %A Van den Bossche, Joris %X
The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.
%B PLoS One %V 10 %P e0136763 %8 2015 %G eng %R 10.1371/journal.pone.0136763 %0 Journal Article %J PLoS One %D 2013 %T Awareness and learning in participatory noise sensing. %A Becker, Martin %A Saverio Caminiti %A Fiorella, Donato %A Francis, Louise %A Pietro Gravino %A Haklay, Mordechai Muki %A Hotho, Andreas %A Vittorio Loreto %A Mueller, Juergen %A Ricchiuti, Ferdinando %A Vito D P Servedio %A Alina Sirbu %A Francesca Tria %XThe development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.
%B PLoS One %V 8 %P e81638 %8 2013 %G eng %R 10.1371/journal.pone.0081638