Asylum is a legal protection granted by a state to individuals who demonstrate a well-founded fear of persecution or who face real risk of being subjected to torture in their country. However, asylum adjudication often depends on the decision maker's subjective assessment of the applicant's credibility. To investigate potential sources of bias in asylum adjudication practices researchers have used statistics and machine learning models, finding significant sources of variation with respect to a number of extra-legal variables. In this paper, we analyse an original dataset of Danish asylum decisions from the Refugee Appeals Board to understand the variables that explain Danish Adjudication. We train a number of classifiers and, while all classifiers agree that candidate credibility is the single most important variable, we find that performance and variable importance change significantly depending on whether data imbalance and temporality are taken into account. We discuss the implications of our findings with respect to the theory and practice of predicting and explaining asylum adjudication.

The research outlined in this paper is focusing on understanding asylum decision-making and outcome variations across the Nordic countries. As a preliminary study, we extract data from an open dataset1 of decision summaries from the Danish Refugee Appeals Board. The approach we propose, offers points for discussion of how prototyping context and participation can help raise questions about such data and engage stakeholders. Combining the application of Machine Learning (ML) and Natural Language Processing (NLP) with participatory methods (e.g. critically designed artefacts) enable us to 1) move beyond “obvious” ML-application areas, 2) through sensemaking of data with stakeholders, and 3) co-develop approaches to data science from a CSCW-perspective.

Human mobility analysis is a multidisciplinary research subject that has attracted a growing interest over the last decade. A substantial amount of such recent studies is driven by the availability of original sources of real-world information about individual movement patterns. An important task in the analysis of mobility data is reliably distinguishing between the stop locations and movement phases that compose the trajectories of the monitored subjects. The problem is especially challenging when mobility is inferred from mobile phone location data: here, oscillations in the association of mobile devices to base stations lead to apparent user mobility even in absence of actual movement. In this paper, we leverage a unique dataset of spatiotemporal individual trajectories that allows capturing both the user and network operator perspectives in mobile phone location data, and investigate the oscillation phenomenon. We present probabilistic and machine learning approaches for detecting oscillations in mobile phone location data, and a filtering technique for removing those. Our analyses and comparison with state-of-the-art approaches demonstrate the superiority of our solution, both in terms of removed oscillations and of error with respect to ground-truth trajectories.

We consider the problem of finding large scale mobility patterns. A common challenge in mobility tracking systems is that large quantity of data is spread out spatially and temporally across many tracking sensors. We thus devise a spatial sampling and information exchange protocol that provides probabilistic guarantees on detecting prominent patterns. For this purpose, we define a general notion of significant popular paths that can capture many different types of motion. We design a summary sketch for the data at each tracking node, which can be updated efficiently, and then aggregated across devices to reconstruct the prominent paths in the global data. The algorithm is scalable, even with large number of mobile targets. It uses a hierarchic query system that automatically prioritizes important trajectories - those that are long and popular. We show further that this scheme can in fact give good results by sampling relatively few sensors and targets, and works for streaming spatial data. We prove differential privacy guarantees for the randomized algorithm. Extensive experiments on real GPS data show that the method is efficient and accurate, and is useful in predicting motion of travelers even with small samples.

Searching for similar GPS trajectories is a fundamental problem that faces challenges of large data volume and intrinsic complexity of trajectory comparison. In this paper, we present a suite of sketches for trajectory data that drastically reduce the computation costs associated with near neighbor search, distance estimation, clustering and classification, and subtrajectory detection. Apart from summarizing the dataset, our sketches have two uses. First, we obtain simple provable locality sensitive hash families for both the Hausdorff and Fréchet distance measures, useful in near neighbour queries. Second, we build a data structure called MRTS (Multi Resolution Trajectory Sketch), which contains sketches of varying degrees of detail. The MRTS is a user-friendly, compact representation of the dataset that allows to efficiently answer various other types of queries. Moreover, MRTS can be used in a dynamic setting with fast insertions of trajectories into the database.

In this paper, we aim at answering the question "at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information?". Our quest for a response unveils (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. We conduct analyses using fine-grained GPS trajectories of 119 users worldwide. Our findings have potential applications in ubiquitous computing and mobile service design, in terms of energy efficiency, location-based service operations, active probing of subscribers' positions in mobile networks and trajectory data compression.

We describe an online algorithm to simplify large volumes of location and sensor data on the source mobile device, by eliminating redundant data points and saving important ones. Our approach is to use topological persistence to identify large scale sharp features of a data stream.

The environmental context of a mobile device determines where/how it is used, which can be exploited for efficient operation and better usability. In this work we describe a general method using only the lightweight sensors on a smartphone to detect if a device is indoor or outdoor. Using semi-supervised machine learning techniques, our method automatically learns characteristics of new environments and devices, thereby achieves detection accuracy of over 90% even in unfamiliar circumstances. Therefore, it easily outperforms existing indoor-outdoor detection techniques based on static algorithms, or relying on energy hungry and unreliable GPS.

The environmental context of a mobile device determines how it is used and how the device can optimize operations for greater efficiency and usability. We consider the problem of detecting if a device is indoor or outdoor. Towards this end, we present a general method employing semi-supervised machine learning and using only the lightweight sensors on a smartphone. We find that a particular semi-supervised learning method called co-training, when suitably engineered, is most effective. It is able to automatically learn characteristics of new environments and devices, and thereby provides a detection accuracy exceeding 90% even in unfamiliar circumstances. It can learn and adapt online, in real time, at modest computational costs. Thus the method is suitable for on-device learning. Implementation of the indoor-outdoor detection service based on our method is lightweight in energy use -- it can sleep when not in use and does not need to track the device state continuously. It is shown to outperform existing indoor-outdoor detection techniques that rely on static algorithms or GPS, in terms of both accuracy and energy-efficiency.