A high-level overview of AI ethics.

Artificial intelligence (AI) ethics is a field that has emerged as a response to the growing concern regarding the impact of AI. It can be read as a nascent field and as a subset of the wider field of digital ethics, which addresses concerns raised by the development and deployment of new digital technologies, such as AI, big data analytics, and blockchain technologies. The principle aim of this article is to provide a high-level conceptual discussion of the field by way of introducing basic concepts and sketching approaches and central themes in AI ethics. The first part introduces concepts by noting what is being referred to by "AI" and "ethics", etc.; the second part explores some predecessors to AI ethics, namely engineering ethics, philosophy of technology, and science and technology studies; the third part discusses three current approaches to AI ethics namely, principles, processes, and ethical consciousness; and finally, the fourth part discusses central themes in translating ethics in to engineering practice. We conclude by summarizing and noting the inherent interdisciplinary future directions and debates in AI ethics.

Systematizing Audit in Algorithmic Recruitment.

Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview), and CV-analysis (Textio), as well as estimation of psychometric characteristics through image-(Traitify) and game-based assessments (HireVue) and video interviews (Cammio). However, driven by concern that such high-impact technology must be used responsibly due to the potential for unfair hiring to result from the algorithms used by these tools, there is an active effort towards proving mechanisms of governance for such automation. In this article, we apply a systematic algorithm audit framework in the context of the ethically critical industry of algorithmic recruitment systems, exploring how audit assessments on AI-driven systems can be used to assure that such systems are being responsibly deployed in a fair and well-governed manner. We outline sources of risk for the use of algorithmic hiring tools, suggest the most appropriate opportunities for audits to take place, recommend ways to measure bias in algorithms, and discuss the transparency of algorithms.

Floral Diversity in Different Types of Honey

Abstract Many different types of honey are available in the Brazilian market. They vary in color, flavor, smell, thereby increasing interest in honey characterization relating to botanical origin. A total of 155 honey samples belonging to Brazilian flora were examined on the pollen analysis; sampling is made in a span of one year. The preparation followed melisso palynological analysis based on the specific botanical variety. The pollen spectra revealed 60 pollen types belonging to 27 plant families and Myrtaceae, Fabaceae and Asteraceae were the dominant plant families. Few pollen types were found in most samples of honey. The families that showed major richness of pollen types were Fabaceae and Asteraceae. Only six floral sources of pollen plants and three floral sources of nectar plants appeared in the category of predominant pollen. The unifloral honeys were slightly more frequent than polyfloral, and wild floral species dominated most of the honey samples. These floral sources, even in minor parts in the honeys samples, are also part of the biological feature of theses honeys. The honeys from natural fields cannot be completely accounted by the term unifloral honeys.