When there is a suspicious figure reuse case in science, research integrity investigators often find it difficult to rebut authors claiming that "it happened by chance". In other words, when there is a "collision" of image features, it is difficult to justify whether it appears rarely or not. In this article, we provide a method to predict the rarity of an image feature by statistically estimating the chance of it randomly occurring across all scientific imagery. Our method is based on high-dimensional density estimation of ORB features using 7+ million images in the PubMed Open Access Subset dataset. We show that this method can lead to meaningful feedback during research integrity investigations by providing a null hypothesis for scientific image reuse and thus a p-value during deliberations. We apply the model to a sample of increasingly complex imagery and confirm that it produces decreasingly smaller p-values as expected. We discuss applications to research integrity investigations as well as future work.

In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estimation of two-parameter Weibull distribution conditionally to the features. To achieve this result, we describe a neural network architecture and the associated loss functions that takes into account the right-censored data. We extend the approach to a finite mixture of two-parameter Weibull distributions. We first validate that our model is able to precisely estimate the right parameters of the conditional Weibull distribution on synthetic datasets. In numerical experiments on two real-word datasets (METABRIC and SEER), our model outperforms the state-of-the-art methods. We also demonstrate that our approach can consider any survival time horizon.

Addressing the European Parliament, commissioner Thierry Breton introduced a new initiative called'European Strategy for Europe - Fit for the Digital Age', which includes a whitepaper on artificial intelligence and the European Strategy for Data which will work to inform a new legislative framework. The overarching theme of Breton's delivery focused on developing an EU strategy such that the deep-rooted transformations of digital technologies work to serve European citizens and not the other way around. Breton added that these technologies must be secure and must not be sold off at a reduced price During his address Breton explained that the Big Tech giants have changed the way we communicate and have even come to shape the way we form democracies. There is a gigantic wave of data coming over us, the Commissioner explained and "by 2030, we think there will be 500 billion connected items over the planet and great dataset will emerge". Given Europe's developed industrial base, Breton says that the data that will be extracted from a raft of industry sectors will enable the EU to leverage and monetise this significant asset.

In the past, KDnuggets has covered collections of key terms, including those for machine learning, deep learning, big data, natural language processing, and more. As we get into a new year, and as we have not published any collections of key terms in the recent past, we thought it would be a good idea to highlight some AI, data science, and machine learning terms that we should all now be familiar with in the constantly evolving landscape. As such, these terms are a combination of some more recently-emerging concepts, as well as existing concepts which may be of perceived increased importance of late. The definitions for these are a combined effort from the KDnuggets team, including Gregory Piatetsky, Asel Mendis, Matthew Dearing, and myself, Matthew Mayo. And so without any further ado, here are the first 10 terms you need to know, with the second 10 coming next week, giving us a total of 20 terms to know for 2020. Automated machine learning (AutoML) spans the fairly wide chasm of tasks which could reasonably be thought of as being included within a machine learning pipeline.

CEIPI is pleased to announce the offering of the 3rd edition of the Advanced Training Program on "Artificial Intelligence and Intellectual Property" that will take place in Strasbourg from 23 to 25 April 2020. This new training follows the very successful editions of past years, gathering a high number of professionals coming from almost all the European countries, and as far as Brazil, Canada, United States, China, India, Malaysia and Japan, and including senior officials from renowned institutions. Artificial Intelligence (AI) and robots have been the subject of science fiction for some time. That fictional future is now a present reality. The regulation of AI's activities is set to become a primary policy issue.

Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. Still, the tech is already making important decisions about your life and potentially ruling over which political advertisements you see, how your application to your dream job is screened, how police officers are deployed in your neighborhood, and even predicting your home's risk of fire. But these systems can be biased based on who builds them, how they're developed, and how they're ultimately used. This is commonly known as algorithmic bias. It's tough to figure out exactly how systems might be susceptible to algorithmic bias, especially since this technology often operates in a corporate black box.

Tesla CEO Elon Musk thinks that organizations developing article intelligence should be regulated, including his own companies. Musk tweeted his thoughts on A.I. on Monday night, February 17, in response to an article written about research company OpenAI, which was once backed by Musk himself. "OpenA.I. should be more open imo," Musk tweeted. "All orgs developing advanced A.I. should be regulated, including Tesla." Musk also said that both individual governments and global organizations should handle the regulation of A.I.

The European Union is set to release new regulations for artificial intelligence that are expected to focus on transparency and oversight as the region seeks to differentiate its approach from those of the United States and China. On Wednesday, EU technology chief Margrethe Vestager will unveil a wide-ranging plan designed to bolster the region's competitiveness. While transformative technologies such as AI have been labeled critical to economic survival, Europe is perceived as slipping behind the U.S., where development is being led by tech giants with deep pockets, and China, where the central government is leading the push. Europe has in recent years sought to emphasize fairness and ethics when it comes to tech policy. These systems would require human oversight and audits, according to a widely leaked draft of the new rules.

Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensitive information, and data analysis methods must provably comply with regulatory guidelines. Although federated learning prevents sharing raw data, it is still possible to launch privacy attacks on the model parameters that are exposed during the training process, or on the generated machine learning model. In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning. Unlike the state-of-the-art differential privacy-based frameworks, our approach aims to maximize utility or model performance, while supporting a defensible level of privacy, as demanded by GDPR and HIPAA. We perform a comprehensive empirical evaluation on two important problems in the healthcare domain, using real-world electronic health data of 1 million patients. The results demonstrate the effectiveness of our approach in achieving high model performance, while offering the desired level of privacy. Through comparative studies, we also show that, for varying datasets, experimental setups, and privacy budgets, our approach offers higher model performance than differential privacy-based techniques in federated learning.

Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justice theory, three different fairness types (distributive, procedural, interactional), and the frameworks for redressing instances of unfairness (retributive justice, restorative justice). Second, we review the design literature that specifically focuses on issues of AI fairness in organizations. Third, we propose a design agenda for AI fairness in organizations that applies each of the fairness types to organizational scenarios. Then, the paper concludes with implications for future research.