Dimitar Kostadinov applied for a 6-year Master's program in Bulgarian and European Law at the University of Ruse, and was enrolled in 2002 following high school. He obtained a Master degree in 2009. From 2008-2012, Dimitar held a job as data entry & research for the American company Law Seminars International and its Bulgarian-Slovenian business partner DATA LAB. In 2011, he was admitted Law and Politics of International Security to Vrije Universiteit Amsterdam, the Netherlands, graduating in August of 2012. Dimitar also holds an LL.M. diploma in Intellectual Property Rights & ICT Law from KU Leuven (Brussels, Belgium).

In the context of the digital economy, data is often referred to as the new fuel driving the economy. This comparison has its origin in the title of a May 2017 report by The Economist entitled "The world's most valuable resource is no longer oil, but data". The saying "data is a new oil" became a kind of cliché. While this comparison is highly evocative, it doesn't fully reflect reality. There are a number of differences between how oil and data affect the economy.

We present a unifying framework encompassing a plethora of social choice settings. Viewing each social choice setting as voting in a suitable metric space, we offer a general model of social choice over metric spaces, in which--similarly to the spatial model of elections--each voter specifies an ideal element of the metric space. The ideal element acts as a vote, where each voter prefers elements that are closer to her ideal element. But it also acts as a proposal, thus making all participants equal not only as voters but also as proposers. We consider Condorcet aggregation and a continuum of solution concepts, ranging from minimizing the sum of distances to minimizing the maximum distance. We study applications of our abstract model to various social choice settings, including single-winner elections, committee elections, participatory budgeting, and participatory legislation. For each setting, we compare each solution concept to known voting rules and study various properties of the resulting voting rules. Our framework provides expressive aggregation for a broad range of social choice settings while remaining simple for voters; and may enable a unified and integrated implementation for all these settings, as well as unified extensions such as sybil-resiliency, proxy voting, and deliberative decision making.

On April 21, the EU Commission is expected to adopt a proposal for a regulation (the AI Regulation) of "artificial intelligence systems" (AI systems), imposing new obligations that will impact businesses across many, if not all, sectors of the economy. The AI Regulation is ambitious and will prove controversial, touching off a legislative battle lasting at least until 2022. The proposed AI Regulation will join other ambitious EU initiatives in the digital sector, such as the Data Governance Act, Digital Services Act and Digital Markets Act, all currently working their way through the EU legislative process, as well as ongoing reform of EU antitrust policy. Other jurisdictions will likely examine all of these measures closely as potential models for similar legislation. A leaked draft of the AI Regulation (the Draft) illustrates the potential scope and impact of the new law.

Dr Kate Darling is a research specialist in human-robot interaction, robot ethics and intellectual property theory and policy at the Massachusetts Institute of Technology (MIT) Media Lab. In her new book, The New Breed, she argues that we would be better prepared for the future if we started thinking about robots and artificial intelligence (AI) like animals. What is wrong with the way we think about robots? So often we subconsciously compare robots to humans and AI to human intelligence. The comparison limits our imagination.

Europe is already the world's tech privacy cop. Now it might become the AI cop too. Companies using artificial intelligence in the EU could soon be required to get audited first, under new rules set to be proposed by the European Union as soon as next week. The regulations were partly sketched out in an EU white paper last year and aim to ensure the responsible application of AI in high-stakes situations like autonomous driving, remote surgery or predictive policing. Officials want to ensure that such systems are trained on privacy-protecting and diverse data sets.

We introduce distributed NLI, a new NLU task with a goal to predict the distribution of human judgements for natural language inference. We show that models can capture human judgement distribution by applying additional distribution estimation methods, namely, Monte Carlo (MC) Dropout, Deep Ensemble, Re-Calibration, and Distribution Distillation. All four of these methods substantially outperform the softmax baseline. We show that MC Dropout is able to achieve decent performance without any distribution annotations while Re-Calibration can further give substantial improvements when extra distribution annotations are provided, suggesting the value of multiple annotations for the example in modeling the distribution of human judgements. Moreover, MC Dropout and Re-Calibration can achieve decent transfer performance on out-of-domain data. Despite these improvements, the best results are still far below estimated human upper-bound, indicating that the task of predicting the distribution of human judgements is still an open, challenging problem with large room for future improvements. We showcase the common errors for MC Dropout and Re-Calibration. Finally, we give guidelines on the usage of these methods with different levels of data availability and encourage future work on modeling the human opinion distribution for language reasoning.

Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context. Discriminative tasks are limiting because they fail to adequately evaluate the model's ability to reason and explain predictions with underlying commonsense knowledge. They also allow such models to use reasoning shortcuts and not be "right for the right reasons". In this work, we present ExplaGraphs, a new generative and structured commonsense-reasoning task (and an associated dataset) of explanation graph generation for stance prediction. Specifically, given a belief and an argument, a model has to predict whether the argument supports or counters the belief and also generate a commonsense-augmented graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. The explanation graphs for our dataset are collected via crowdsourcing through a novel Collect-Judge-And-Refine graph collection framework that improves the graph quality via multiple rounds of verification and refinement. A significant 83% of our graphs contain external commonsense nodes with diverse structures and reasoning depths. We also propose a multi-level evaluation framework that checks for the structural and semantic correctness of the generated graphs and their plausibility with human-written graphs. We experiment with state-of-the-art text generation models like BART and T5 to generate explanation graphs and observe that there is a large gap with human performance, thereby encouraging useful future work for this new commonsense graph-based explanation generation task.

Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.

More and more, when you apply for a job, ask for a raise, or wait for your work schedule, AI is choosing your fate. Alarmingly, many job applicants never realize that they are being evaluated by a computer, and they have almost no recourse when the software is biased, makes a mistake, or fails to accommodate a disability. While New York City has taken the important step of trying to address the threat of AI bias, the problem is that the rules pending before the City Council are bad, really bad, and we should listen to the activists speaking out before it's too late. Some advocates are calling for amendments to this legislation, such as expanding definitions of discrimination beyond race and gender, increasing transparency, and covering the use of AI tools in hiring, not just their sale. But many more problems plague the current bill, which is why a ban on the technology is presently preferable to a bill that sounds better than it actually is.