Prediction Scores as a Window into Classifier Behavior

Most multi-class classifiers make their prediction for a test sample by scoring the classes and selecting the one with the highest score. Analyzing these prediction scores is useful to understand the classifier behavior and to assess its reliability. We present an interactive visualization that facilitates per-class analysis of these scores. Our system, called Classilist, enables relating these scores to the classification correctness and to the underlying samples and their features. We illustrate how such analysis reveals varying behavior of different classifiers. Classilist is available for use online, along with source code, video tutorials, and plugins for R, RapidMiner, and KNIME at https://katehara.github.io/classilist-site/.Comment: Presented at NIPS 2017 Symposium on Interpretable Machine Learnin

Uncertainty-aware estimation of population abundance using machine learning

Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classification based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classification. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is needed. We propose a method that improves classification quality by using limited groundtruth data to extrapolate the po-tential errors in larger datasets. It significantly improves the counting of elements per class. We further propose visualization designs for understanding and evaluating the classification un-certainty. They support end-users in considering the impact of potential misclassifications for interpreting the classification output. This work was developed to address the needs of ecologists studying fish population abundance using computer vision, but generalizes to a larger range of applications. Our method is largely applicable for a variety of Machine Learning technologies, and our visualizations further support their transfer to end-users

Conversational Agents to Address Abusive Online Behaviors

Abusive online behaviors occur at a large scale on all social media, and have dire consequences for their victims. Although the problem is largely acknowledged, technological solutions remain limited to detecting and hiding abusive comments. More can be done to address abusers themselves. We propose to investigate the potential of conversational technologies to dialogue with abusers. In this problem description paper, we outline directions for studying the effectiveness dialogue strategies, e.g., to educate or deter abusers, or keep them busy with chatbots thus limiting the time they spend perpetuating abuses

Designing chatbots for training professionals in child and youth social care

Introduction: Various subjects in child and youth social care, such as sexuality and sexual violence, are quite sensitive, and professionals may experience a certain reluctance to discuss these subjects with their clients (e.g., the young people they work with, as well as their families) and colleagues. An example of such a subject is sexual abuse and unacceptable behavior that may occur with their clients, whether at home, at the youth care institution or somewhere else. It is essential that youth care professionals do not shy away from such a sensitive subject as sexual abuse and know how to talk about it with their clients in a healthy way. Professionals in child and youth social care should dare to educate on this topic, and be trained to deal with the enormous diversity of young people and parents they encounter in their work. Research on application of trained methods shows that receiving training on itself is often not enough to develop strong applicable competences about subjects like sexual abuse and to continue to apply these acquired practical skills in the field in the long term. In order to be able to apply ‘what is learned’ successfully, it is necessary to practice the learned skills in a safe environment, and to regularly refresh those skills. In order to create an opportunity for practicing skills in a safe environment, we have explored the extent to which innovative chatbot technologies can be used to better equip (future) professionals to apply and practice their skills

Climate Communication

Over the past few years the tone of the debate around climate change has shifted from sceptical to soberingly urgent as the global community has prioritised the research into solutions which will mitigate greenhouse gas emissions. So far this research has been insufficient. One of the major problems for driving public and private stakeholders to implement existing solutions and research new ones is how we communicate about climate change (Stoknes, 2014). There seems to be a lack of common language that drives the scientific community away from policymakers and the public. Due to this lack, it is hard to translate findings into viable and sustainable solutions and to adopt new climate-neutral economies and habits

The Role of Interactive Visualization in Fostering Trust in AI

The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.publishe

End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates

Recommender Systems (RS) have proven successful in a wide variety of domains, and the human resources (HR) domain is no exception. RS proved valuable for recommending candidates for a position, although the ethical implications have recently been identified as high-risk by the European Commission. In this study, we apply RS to match candidates with job requests. The RS pipeline includes two fairness gates at two different steps: pre-processing (using GAN-based synthetic candidate generation) and post-processing (with greedily searched candidate re-ranking). While prior research studied fairness at pre- and post-processing steps separately, our approach combines them both in the same pipeline applicable to the HR domain. We show that the combination of gender-balanced synthetic training data with pair re-ranking increased fairness with satisfactory levels of ranking utility. Our findings show that using only the gender-balanced synthetic data for bias mitigation is fairer by a negligible margin when compared to using real data. However, when implemented together with the pair re-ranker, candidate recommendation fairness improved considerably, while maintaining a satisfactory utility score. In contrast, using only the pair re-ranker achieved a similar fairness level, but had a consistently lower utility