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As the influence and use of artificial intelligence (AI) have grown As the influence and use of artificial intelligence (AI) have grown and its transformative potential has become more apparent, many and its transformative potential has become more apparent [32, 54], questions have been raised regarding the economic, political, social, many questions have been raised regarding the economic, political, and ethical implications of its use. Public opinion plays an important social, and ethical implications of its use [27]. The development role in these discussions, influencing product adoption, commercial and application of AI increasingly features in media, academic, development, research funding, and regulation. In this paper we industrial, regulatory, and public discussions [18, 23, 28], with active present results of an in-depth survey of public opinion of artificial debate on wide-ranging issues such as the impact of automation intelligence conducted with 10,005 respondents spanning eight on the future of work [8, 50, 52], the interaction of AI with human countries and six continents. We report widespread perception rights issues such as privacy and discrimination [1, 4, 10, 16], the that AI will have significant impact on society, accompanied by ethics of autonomous weapons [53, 59], and the development and strong support for the responsible development and use of AI, and availability of dual-use technologies such as synthetic media that also characterize the public's sentiment towards AI with four key may be used for either benevolent or nefarious purposes [48].

Modern machine learning techniques based on artificial neural networks (ANNs) are implemented through algebraic manipulations of vectors, matrices, and tensors in high-dimensional spaces. While ANNs have an impressive ability to process data to find patterns, they do not typically model high-level cognition. Furthermore, ANNs are usually models of only a single task. Otherwise, when an ANN is trained to learn a series of tasks, catastrophic interference occurs, with each new task causing the ANN to forget all previously learned tasks [8, 21, 22]. On the other hand, symbolic cognitive architectures, such as the widely used ACT-R [1, 31], can capture the complexities of high-level cognition but scale poorly to the naturalistic, non-symbolic data of sensory perception, e.g., images, or to big data sets necessary for modelling learning over a lifetime, e.g., corpora with hundreds of millions of words.

As has been argued in [Rod91] and [FOP92b], there is a growing demand for information systems, not only allowing for changes of their information base, but also for modifications in their underlying structure (conceptual schema and specification of dynamic aspects). In case of snapshot databases, structure modifications will lead to costly data conversions and reprogramming. The intention of an evolving information system ([FOP92a], [OPF94]) is to be able to handle updates of all components of the so-called application model, containing the information structure, the constraints on this structure, the population conforming to this structure and the possible operations. The theory of such systems should, however, be independent of whatever modelling technique is used to describe the application model. In this paper, we discuss a general theory for the evolution of application models. However, only conceptual aspects are considered, focus is on what evolution is, rather than on how to implement evolution in a database manegement system. In [PW93], an informal introduction to this theory is provided.

In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences. Building and maintaining public trust in AI has been identified as the key to successful and sustainable innovation. This chapter discusses the challenges related to operationalizing ethical AI principles and presents an integrated view that covers high-level ethical AI principles, the general notion of trust/trustworthiness, and product/process support in the context of responsible AI, which helps improve both trust and trustworthiness of AI for a wider set of stakeholders.

Convex clustering is an attractive clustering algorithm with favorable properties such as efficiency and optimality owing to its convex formulation. It is thought to generalize both k-means clustering and agglomerative clustering. However, it is not known whether convex clustering preserves desirable properties of these algorithms. A common expectation is that convex clustering may learn difficult cluster types such as non-convex ones. Current understanding of convex clustering is limited to only consistency results on well-separated clusters. We show new understanding of its solutions. We prove that convex clustering can only learn convex clusters. We then show that the clusters have disjoint bounding balls with significant gaps. We further characterize the solutions, regularization hyperparameters, inclusterable cases and consistency.

The graph represents a network of 3,600 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 14 May 2021 at 12:08 UTC. The requested start date was Friday, 14 May 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 5-day, 18-hour, 4-minute period from Saturday, 08 May 2021 at 05:55 UTC to Friday, 14 May 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Considering processes of a business in a recommender system is highly advantageous. Although most studies in the business process analysis domain are of descriptive and predictive nature, the feasibility of constructing a process-aware recommender system is assessed in a few works. One reason can be the lack of knowledge on process mining potential for recommendation problems. Therefore, this paper aims to identify and analyze the published studies on process-aware recommender system techniques in business process management and process mining domain. A systematic review was conducted on 33 academic articles published between 2008 and 2020 according to several aspects. In this regard, we provide a state-of-the-art review with critical details and researchers with a better perception of which path to pursue in this field. Moreover, based on a knowledge base and holistic perspective, we discuss some research gaps and open challenges in this field.

Recent research has shown that numerous human-interpretable directions exist in the latent space of GANs. In this paper, we develop an automatic procedure for finding directions that lead to foreground-background image separation, and we use these directions to train an image segmentation model without human supervision. Our method is generator-agnostic, producing strong segmentation results with a wide range of different GAN architectures. Furthermore, by leveraging GANs pretrained on large datasets such as ImageNet, we are able to segment images from a range of domains without further training or finetuning. Evaluating our method on image segmentation benchmarks, we compare favorably to prior work while using neither human supervision nor access to the training data. Broadly, our results demonstrate that automatically extracting foreground-background structure from pretrained deep generative models can serve as a remarkably effective substitute for human supervision.

Transparency is an essential requirement of machine learning based decision making systems that are deployed in real world. Often, transparency of a given system is achieved by providing explanations of the behaviour and predictions of the given system. Counterfactual explanations are a prominent instance of particular intuitive explanations of decision making systems. While a lot of different methods for computing counterfactual explanations exist, only very few work (apart from work from the causality domain) considers feature dependencies as well as plausibility which might limit the set of possible counterfactual explanations. In this work we enhance our previous work on convex modeling for computing counterfactual explanations by a mechanism for ensuring actionability and plausibility of the resulting counterfactual explanations.