As machine learning models are increasingly used for high-stakes decision making, scholars have sought to intervene to ensure that such models do not encode undesirable social and political values. However, little attention thus far has been given to how values influence the machine learning discipline as a whole. How do values influence what the discipline focuses on and the way it develops? If undesirable values are at play at the level of the discipline, then intervening on particular models will not suffice to address the problem. Instead, interventions at the disciplinary-level are required. This paper analyzes the discipline of machine learning through the lens of philosophy of science. We develop a conceptual framework to evaluate the process through which types of machine learning models (e.g. neural networks, support vector machines, graphical models) become predominant. The rise and fall of model-types is often framed as objective progress. However, such disciplinary shifts are more nuanced. First, we argue that the rise of a model-type is self-reinforcing--it influences the way model-types are evaluated. For example, the rise of deep learning was entangled with a greater focus on evaluations in compute-rich and data-rich environments. Second, the way model-types are evaluated encodes loaded social and political values. For example, a greater focus on evaluations in compute-rich and data-rich environments encodes values about centralization of power, privacy, and environmental concerns.

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Bayesian Networks (BNs) and investigate the factors associated with childhood diarrhoea. We make use of freeware tools to learn the graphical structure of the DHS data with score-based, constraint-based, and hybrid structure learning algorithms. We investigate the effect of missing values, sample size, and knowledge-based constraints on each of the structure learning algorithms and assess their accuracy with multiple scoring functions. Weaknesses in the survey methodology and data available, as well as the variability in the BNs generated, mean that is not possible to learn a definitive causal BN from data. However, knowledge-based constraints are found to be useful in reducing the variation in the graphs produced by the different algorithms, and produce graphs which are more reflective of the likely influential relationships in the data. Furthermore, valuable insights are gained into the performance and characteristics of the structure learning algorithms. Two score-based algorithms in particular, TABU and FGES, demonstrate many desirable qualities; a) with sufficient data, they produce a graph which is similar to the reference graph, b) they are relatively insensitive to missing values, and c) behave well with knowledge-based constraints. The results provide a basis for further investigation of the DHS data and for a deeper understanding of the behaviour of the structure learning algorithms when applied to real-world settings.

--Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach--referred to as GeoTrackNet--for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks, then uses a contrario detection to detect abnormal events. The neural network helps us capture complex and heterogeneous patterns in vessels' behaviors, while the a contrario detection takes into account the fact that the learned distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method. Nowadays, about 90% of the world trade is carried by maritime traffic, and it is growing consistently [2]. Maritime surveillance and Maritime Situational A wareness (MSA) are vital demands. In this context, anomaly detection is one of the most important tasks, because anomalies may involve accidents (loss of navigation, damages in engine, etc.) or illegal activities (smuggling, illegal transshipment, etc.). Initially designed for collision avoidance, the Automatic Identification System (AIS) has quickly become the main source of information for maritime surveillance thanks to its information richness. This paper is an extension of the MultitaskAIS presented in [1]. While [1] presents the ability of handling noisy and irregularly sampled data as well as the computational benefit of this architecture for multiple tasks in maritime surveillance, this paper focuses on detailing the most important task: anomaly detection.

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword - referred to as the expectation of the distribution - and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach it-eratively leverages the crowd to estimate both keyword-specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%. 1 Introduction Event detection on microblogging platforms such as Twitter aims to detect events preemptively.

JAKARTA: Indonesian President Joko Widodo on Thursday (Nov 28) ordered government agencies to remove two ranks of public servants in 2020 and replace their roles with artificial intelligence, in a bid to cut red tape hampering investment. Widodo made the remarks in a room full of leaders of big companies as he laid out a second-term agenda aimed at changing the structure of Southeast Asia's largest economy by reducing its reliance on natural resources. The president, whose new five-year term began last month after winning an election in April, said Indonesia should transition to higher-end manufacturing, such as electric vehicles and use raw materials like coal and bauxite in such industries, not just exports. Such transformation would require foreign investment and Widodo said he would improve the business climate by fixing dozens of overlapping rules and cutting red tape. To reduce bureaucracy, Widodo said the current top four tiers in government agencies would be flattened to two next year. "I have ordered my minister (of administrative and bureaucratic reform) to replace them with AI.

"To boldly go where no one has gone before." The series followed the voyages of the starship USS Enterprise as she explored "strange new worlds, to seek out new life and new civilizations." I think we are all on a similar voyage right now. And we don't have to engage a "warp drive" to experience new life and new civilizations. Life -- here on Earth -- is changing rapidly due to technological developments.

Artificial Intelligence is currently playing a significant role in our everyday life. Whether we're trying to classify plant species, Netflix viewing preferences or mortgage suitability, we depend on AI to handle it all for us. While having AI make everyday decisions for us is somewhat acceptable, using machines to determine guilt or innocence in court may seem a step too far. But the Estonian government doesn't think so. According to Wired, the Estonian Ministry of Justice has officially asked the country's chief data officer, Ott Velsberg, to design a robot judge.

Don't look now, but artificial intelligence is watching you. Artificial intelligence has tremendous power to enhance spying, and both authoritarian governments and democracies are adopting the technology as a tool of political and social control. The potential of AI surveillance is the subject of the third installment of the Sleepwalkers podcast. The episode examines how AI consolidates power and control, and asks if we can limit this troubling trend. Data collected from apps and websites already help optimize ads and social feeds.

Artificial intelligence is set to improve cybersecurity in 2020, and you're about to find out how. As a result, businesses must consciously remain updated on legal requirements like CCPA and GDPR. Also, it's essential to stay on top of the latest industry trends now more than ever. Some major cybersecurity trends in 2019 include increased data privacy regulation, phishing attacks, IoT ransomware, among others. But, the most significant trend this year may be the increased investment in artificial intelligence.

Alan Turing's seminal 1950 paper "Computing Machinery and Intelligence", posed the question "Can machines think?" Since then machine learning (ML) has found its way into numerous processes; seeking to simplify our lives by making processes smarter, better and faster. Financial risk management is an industry that is rife with opportunities for ML to disrupt in the coming years, one of the most obvious areas being credit scoring. In this blog we explore some of the main findings of the recently published Bank of England survey on ML, this is followed by our views on the challenges and potential solutions of implementing ML within a credit risk scoring framework. When we talk about the rise of ML in credit risk, we quite often forget that one of the earliest real life use cases for ML was within this very industry.