AI ethics in October 2022

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and acc

In the past few decades, major public health advances have happened in Europe, with drastic decreases in premature mortality and a life expectancy increase of almost 9 years since 1980. European countries have some of the best health-care systems in the world. However,
\nEurope is challenged with unprecedented and overlapping crises that are detrimental to human health and livelihoods and threaten adaptive capacity, including the COVID-19 pandemic, the Russian invasion of Ukraine, the fastes

Abstract Artificial intelligence (AI) holds significant implications for higher education; however, references to AI in the literature are often vague and open to debate. In order to understand how to progress AI-related research and analysis, this critical review systematically searched top higher education journals for references to the term ‘artificial intelligence’. We reviewed definitions and conducted a discourse analysis of included texts. Our findings identify few, confusing definitions

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups

Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stake

Abstract In light of fast‐growing popular, political and professional discourses around AI in education, this article outlines five broad areas of contention that merit closer attention in future discussion and decision‐making. These include: (1) taking care to focus on issues relating to 'actually existing' AI rather than the overselling of speculative AI technologies; (2) clearly foregrounding the limitations of AI in terms of modelling social contexts, and simulating human intelligence, recko

The advancement of artificial intelligence in education (AIED) has the potential to transform the educational landscape and influence the role of all involved stakeholders. In recent years, the applications of AIED have been gradually adopted to progress our understanding of students' learning and enhance learning performance and experience. However, the adoption of AIED has led to increasing ethical risks and concerns regarding several aspects such as personal data and learner autonomy. Despite

Artificial Intelligence (AI) implementation in public administration is gaining momentum heralded by the hope of smart public services that are personalised, lean, and efficient. However, the use of AI in public administration is riddled with ethical tensions of fairness, transparency, privacy, and human rights. We call these AI tensions. The current literature lacks a contextual and processual understanding of AI adoption and diffusion in public administration to be able to explore such tension

Integrating neurons into digital systems may enable performance infeasible with silicon alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins are integrated with in silico computing via a high-density multielectrode array. Through electrophysiological stimulation and recording, cultures are embedded in a simulated game-world, mimicking the arcade game "Pong." Apply

The declaration of the COVID-19 pandemic forced humanity to rethink how we teach and learn. The metaverse, a 3D digital space mixed with the real world and the virtual world, has been heralded as a trend of future education with great potential. However, as an emerging item, rarely did the existing study discuss the metaverse from the perspective of education. In this paper, we first introduce the visions of the metaverse, including its origin, definitions, and shared features. Then, the metaver

Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum: the increased attention comes partly with the view that participation opens the gateway to an inclusive, equitable, robust, responsible and trustworthy AI. Among other benefits, participatory approaches are essential to understanding and adequately representing the needs, desires and perspectives of historically marginalized communities. However, there currently exists lack of clarity on what

Chatbot implementation for assisting customers as a virtual agent can be seen as a tool in helping an organisation to serve better customer service. Malaysia is among the countries forging ahead with the Fourth Industrial Revolution. One of the core technologies mentioned is adopting artificial intelligence tools such as chatbots. In the last few years, there has been a growing interest in AI-based chatbot adoption in the non-HEI context. However, most higher-education institutions (HEIs) are re

Digitization, artificial intelligence, and service robots carry serious ethical, privacy, and fairness risks. Using the lens of corporate digital responsibility (CDR), we examine these risks and their mitigation in service firms and make five contributions. First, we show that CDR is critical in service contexts because of the vast streams of customer data involved and digital service technology’s omnipresence, opacity, and complexity. Second, we synthesize the ethics, privacy, and fairness lite

We describe a publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT datasets (501 studies of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) and 513 studies without PET-positive malignant lesions (negative controls)) acquired between 2014 and 2018 were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner. The imaging protocol cons