As concerns about unfairness and discrimination in "black box" machine learning systems rise, a legal "right to an explanation" has emerged as a compellingly attractive approach for challenge and redress. We outline recent debates on the limited provisions in European data protection law, and introduce and analyze newer explanation rights in French administrative law and the draft modernized Council of Europe Convention 108. While individual rights can be useful, in privacy law they have historically unreasonably burdened the average data subject. "Meaningful information" about algorithmic logics is more technically possible than commonly thought, but this exacerbates a new "transparency fallacy"---an illusion of remedy rather than anything substantively helpful. While rights-based approaches deserve a firm place in the toolbox, other forms of governance, such as impact assessments, "soft law," judicial review, and model repositories deserve more attention, alongside catalyzing agencies acting for users to control algorithmic system design.

Fairness is a critical trait in decision making. As machine-learning models are increasingly being used in sensitive application domains (e.g. education and employment) for decision making, it is crucial that the decisions computed by such models are free of unintended bias. But how can we automatically validate the fairness of arbitrary machine-learning models? For a given machine-learning model and a set of sensitive input parameters, our AEQUITAS approach automatically discovers discriminatory inputs that highlight fairness violation. At the core of AEQUITAS are three novel strategies to employ probabilistic search over the input space with the objective of uncovering fairness violation. Our AEQUITAS approach leverages inherent robustness property in common machine-learning models to design and implement scalable test generation methodologies. An appealing feature of our generated test inputs is that they can be systematically added to the training set of the underlying model and improve its fairness. To this end, we design a fully automated module that guarantees to improve the fairness of the underlying model. We implemented AEQUITAS and we have evaluated it on six state-of-the-art classifiers, including a classifier that was designed with fairness constraints. We show that AEQUITAS effectively generates inputs to uncover fairness violation in all the subject classifiers and systematically improves the fairness of the respective models using the generated test inputs. In our evaluation, AEQUITAS generates up to 70% discriminatory inputs (w.r.t. the total number of inputs generated) and leverages these inputs to improve the fairness up to 94%.

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches, recent methods in (deep) representation learning has shown promising results for specialized tasks such as knowledge base completion. These approaches abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare representation learning and relational learning on various relational classification and clustering tasks, and analyse the complexity of the rules used implicitly by these approaches. Preliminary results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.

The problem of planning a set of paths for the coalition of robots (agents) with different capabilities is considered in the paper. Some agents can modify the environment by destructing the obstacles thus allowing the other ones to shorten their paths to the goal. As a result the mutual solution of lower cost, e.g. time to completion, may be acquired. We suggest an original procedure to identify the obstacles for further removal that can be embedded into almost any heuristic search planner (we use Theta*) and evaluate it empirically. Results of the evaluation show that time-to-complete the mission can be decreased up to 9-12 % by utilizing the proposed technique.

Noname manuscript No. (will be inserted by the editor) Abstract Routing questions in Community Question Answer services (CQAs) such as Stack Exchange sites is a well-studied problem. Yet, cold-start - a phenomena observed when a new question is posted is not well addressed by existing approaches. Additionally, cold questions posted by new askers present significant challenges to state-of-the-art approaches. We propose ColdRoute to address these challenges. ColdRoute is able to handle the task of routing cold questions posted by new or existing askers to matching experts. Specifically, we use Factorization Machines on the one-hot encoding of critical features such as question tags and compare our approach to well-studied techniques such as CQARank and semantic matching (LDA, BoW, and Doc2Vec). Using data from eight stack exchange sites, we are able to improve upon the routing metrics (Precision@1, Accuracy, MRR) over the state-of-the-art models such as semantic matching by 159.5%,31.84%, Keywords question routing · expert finding · cold-start problem · question answering services 1 Introduction Nowadays, the Community-based question answering sites (CQAs) such as Stack Overflow, Stack Exchange Sites, and Quora, which enable people to post questions and answers in various domains [Yang et al., 2013] have accumulated millions Aniket Chakrabarti Microsoft (work done while at The Ohio State University) Email: chakrabarti.14@osu.edu 2 Jiankai Sun et al. One important task in CQAs is to make recommendations for new questions (routing questions), that fall in three scenarios: 1) find experts. In this paper, we focus on the problem of expert finding [Xu et al., 2012,Zhao et al., 2013,Yang et al., 2013, Fang et al., 2016,Zhao et al., 2016,Zhao et al., 2017], which is to choose the right experts for answering questions posted by users in Stack Exchange, which is a network of question-and-answer (Q&A) websites containing topics in various fields. Each Stack Exchange site covers a specific topic. Usually there are two types of questions in CQAs - resolved (questions with answers) and newly posted questions (questions that have not received any answers).

We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems. In particular, the dataset is meant as a tool which allows to easily assess the sanity of deep neural network models that aim to encode, reconstruct or predict video frame sequences. The datasets each consist of 90000 videos. The Simulated Flying Shapes dataset comprises scenes showing two objects of equal shape (rectangle, triangle and circle) and size in which one object approaches its counterpart. The Simulated Planar Manipulator shows a 3-DOF planar manipulator that executes a pick-and-place task in which it has to place a size-varying circle on a squared platform. Different from other widely used datasets such as moving MNIST [1], [2], the two presented datasets involve goal-oriented tasks (e.g. the manipulator grasping an object and placing it on a platform), rather than showing random movements. This makes our datasets more suitable for testing prediction capabilities and the learning of sophisticated motions by a machine learning model. This technical document aims at providing an introduction into the usage of both datasets.

Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical and emergent biases, which often arise as context-specific artifacts of implementation. This position paper interprets technical bias as an epistemological problem and emergent bias as a dynamical feedback phenomenon. In order to stimulate debate on how to change machine learning practice to effectively address these issues, we explore this broader view on bias, stress the need to reflect on epistemology, and point to value-sensitive design methodologies to revisit the design and implementation process of automated decision-making systems.

Sonos might not be primarily known for TV sound: it is famous for its internet-connected speakers, which can stream music from just about anywhere in lush audio. But that works just as well when stuck under your television, bringing great sound and all the same smarts to your films and shows. It came first with the Playbar, a long rectangle of speakers that was the first Sonos speaker to plug into your TV; that was followed by the Playbase, which sat under the television rather than in front of it. Now Sonos has brought the Beam, which takes the best of everything it has done so far and shrinks it down. Until now, bringing Sonos into your living room meant some big – literally – compromises. The Playbar and Playbase are both very large, meaning that small TVs or living rooms can make them look out of place.

The rise of artificial intelligence (AI) is transforming the way things are done. Intelligent home assistants can play your favourite music or control your TV. Machines are being used to detect and decode medical conditions. Industries across the world are feeling the impact of rising technology. Along with the advantages, comes a fair share of scare--the risk of losing jobs to machines.

The world's first Artificial Intelligence enabled cardboard robot that you build yourself. Smartibot works with your smartphone, meaning you can use your mobile as a remote control, or by attaching it to your robot, as it's brain. The Smartibot app contains a powerful A.I. called YOLO which can recognise objects Smartibot sees such as people, cars, dogs and bicycles. Without any programming you can tell your robot to follow you around with a cup of tea, chase your dog or your cat out of your bedroom or even follow your toy car. As well as the A.I. robot, Smartibot comes with cardboard parts to make two additional robots!