Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.This article is categorized under:

Conflicting results have been reported for the relationship between traffic exposure and inception of atopy. The effect of traffic on the prevalence of asthma and atopy at school age was investigated in a representative population.Random samples of schoolchildren (n=7,509, response rate 83.7%) were studied using the International Study of Asthma and Allergies in Childhood phase-II protocol with skin-prick tests, measurements of specific immunoglobulin E and lung function. Traffic exposure was assessed via traffic counts and by an emission model which predicted soot, benzene and nitrogen dioxide (NO 2 ).Traffic counts were associated with current asthma, wheeze and cough. In children with tobacco-smoke exposure, traffic volume was additionally associated with a positive skin-prick test. Cough was associated with soot, benzene and NO 2 , current asthma with soot and benzene, and current wheeze with benzene and NO 2 . No pollutant was associated with allergic sensitisation.High vehicle traffic was associated with asthma, cough and wheeze, and in children additionally exposed to environmental tobacco smoke, with allergic sensitisation. However, effects of socioeconomic factors associated with living close to busy roads cannot be ruled out. Eur Respir J 2003; 21: 956-963.

Data sharing, research ethics, and incentives must improve

Homophily can put minority groups at a disadvantage by restricting their ability to establish links with a majority group or to access novel information. Here, we show how this phenomenon can influence the ranking of minorities in examples of real-world networks with various levels of heterophily and homophily ranging from sexual contacts, dating contacts, scientific collaborations, and scientific citations. We devise a social network model with tunable homophily and group sizes, and demonstrate how the degree ranking of nodes from the minority group in a network is a function of (i) relative group sizes and (ii) the presence or absence of homophilic behaviour. We provide analytical insights on how the ranking of the minority can be improved to ensure the representativeness of the group and correct for potential biases. Our work presents a foundation for assessing the impact of homophilic and heterophilic behaviour on minorities in social networks.

Halogenated natural products are widely distributed in nature, some of them showing potent biological activities. Incorporation of halogen atoms in drug leads is a common strategy to modify molecules in order to vary their bioactivities and specificities. Chemical halogenation, however, often requires harsh reaction conditions and results in unwanted byproduct formation. It is thus of great interest to investigate the biosynthesis of halogenated natural products and the biotechnological potential of halogenating enzymes. This review aims to give a comprehensive overview on the current knowledge concerning biological halogenations.

Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world's knowledge from a neutral point of view. Though everyone can participate it is well known that the editor community has a narrow diversity, with a majority of white male editors. While this participatory gender gap has been studied extensively in the literature, this work sets out to assess potential gender inequalities in Wikipedia articles along different dimensions: notability, topical focus, linguistic bias, structural properties, and meta-data presentation.We find that (i) women in Wikipedia are more notable than men, which we interpret as the outcome of a subtle glass ceiling effect; (ii) family-, gender-, and relationship-related topics are more present in biographies about women; (iii) linguistic bias manifests in Wikipedia since abstract terms tend to be used to describe positive aspects in the biographies of men and negative aspects in the biographies of women; and (iv) there are structural differences in terms of meta-data and hyperlinks, which have consequences for information-seeking activities. While some differences are expected, due to historical and social contexts, other differences are attributable to Wikipedia editors. The implications of such differences are discussed having Wikipedia contribution policies in mind. We hope that the present work will contribute to increased awareness about, first, gender issues in the content of Wikipedia, and second, the different levels on which gender biases can manifest on the Web.

People's perceptions about the frequency of attributes in their social networks sometimes show false consensus, or overestimation of the frequency of own attributes, and sometimes false uniqueness, or underestimation of the frequency. Here we show that both perception biases can emerge solely from the structural properties of social networks. Using a generative network model, we show analytically that perception biases depend on the level of homophily and its asymmetric nature, as well as on the size of minority group. Model predictions correspond to empirical data from a cross-cultural survey study and to numerical calculations on six real-world networks. We also show in what circumstances individuals can reduce their biases by relying on perceptions of their neighbors. This study advances our understanding of the impact of network structure on perception biases and offers a quantitative approach for addressing related issues in society. arXiv:1710.08601v4 [physics.soc-ph] 22 Jul 2019 Empirical networksThe first network, Brazilian network, captures sexual contact between sex workers and sex buyers 46 . The network consists of 16, 730 nodes and 39, 044 edges. There are 10, 106 sex workers and 6, 624 sex buyers (minority-group size f a = 0.4). In this network, no edges among members of the same group exist resulting in the Newman's assortativity (q = −1), and consequently, the network is purely heterophilic (h = 0).The second network is an online Swedish dating network from PussOKram.com (POK) 47 . This network contains 29, 341 nodes with strong heterophily (h = 0.17, q = −0.65). Given the high bipartivity of the network, we are able to infer the group of nodes using the max-cut greedy algorithm. The results are in good agreement with the bipartivity reported 48 . Since the group definition is arbitrary, we label the nodes based on their relative group size as minority gender and majority gender. Here, the fraction of the minority in the network is 0.44.The third network is a Facebook network of a university in the United States (USF51) 49 . The network is composed of 6,253 nodes and includes information about individuals' gender. In this network male students are in the minority, occupying 42% of the network, and the network exhibits a small heterophily 49 (q = −0.06, h = 0.48).The fourth network is extracted from the collaborative programming environment GitHub. The network is a snapshot of the community (extracted August 4, 2015) that includes information about the first name and family name of the programmers. We used the first name and family name to infer the gender of the programmers 50 . After we removed ambiguous names, the network consisted of 120, 338 men and 7, 330 women. Here, women belong to the minority group and represent only about 6% of the population. The network displays a moderately symmetric gender homophily of 0.53 (q = 0.07).The fifth network depicts scientific collaborations in computer science and is extracted from Digital Bibliography & Library Project's website (DBLP) 51 . We used a new...

Scienti¯c collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers. Recent studies show that gender inequality is still present in many scienti¯c practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-speci¯c di®erences in collaboration patterns of more than one million computer scientists over the course of 47 years. We explore how these patterns change over years and career ages and how they impact scienti¯c success. Our results highlight that successful male and female scientists reveal the same collaboration patterns: compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, seek innovations as brokers and establish longer-lasting and more repetitive collaborations. However, women are on average less likely to adopt the collaboration patterns that are related with success, more likely to embed into ego networks devoid of structural holes, and they exhibit stronger gender homophily as well as a consistently higher dropout rate than men in all career ages.