EFSA is requested to assess the safety of a broad range of biological agents in the context of notification for market authorisation as sources of food and feed additives, food enzymes and plant protection products. The qualified presumption of safety (QPS) assessment was developed to provide a harmonised generic pre-assessment to support safety risk assessments performed by EFSA's scientific Panels. The safety of unambiguously defined biological agents (at the highest taxonomic unit appropriate for the purpose for which an application is intended), and the completeness of the body of knowledge are assessed. Identified safety concerns for a taxonomic unit are, where possible and reasonable in number, reflected as ‘qualifications’ in connection with a recommendation for a QPS status. The list of QPS recommended biological agents was reviewed and updated in the current opinion and therefore becomes the valid list. The 2016 update reviews previously assessed microorganisms including bacteria, yeasts and viruses used for plant protection purposes following an Extensive Literature Search strategy. The taxonomic units related to the new notifications received since the 2013 QPS opinion, were periodically evaluated for a QPS status and the results published as Statements of the BIOHAZ Panel. Carnobacterium divergens, Lactobacillus diolivorans, Microbacterium imperiale, Pasteuria nishizawae, Pediococcus parvulus, Bacillus flexus, Bacillus smithii, Xanthomonas campestris and Candida cylindracea were recommended for the QPS list. All taxonomic units previously recommended for the 2013 QPS list had their status reconfirmed as well their qualifications with the exception of Pasteuria nishizawae for which the qualification was removed. The exclusion of filamentous fungi and enterococci from the QPS evaluations was reconsidered but monitoring will be maintained and the status will be re-evaluated in the next QPS Opinion update. Evaluation of bacteriophages should remain as a case-by-case procedure and should not be considered for QPS status.

https://efsa.onlinelibrary.wiley.com/doi/pdf/10.2903/j.efsa.2020.5966

Scientific Opinion on the update of the list of QPS-recommended biological agents intentionally added to food or feed as notified to EFSA

This paper presents a comprehensive survey on acquisition, tracking, and pointing (ATP) mechanisms used in free-space optical (FSO) communications systems. ATP mechanisms are a critical component for a wide variety of use cases of mobile FSO communications. ATP mechanisms are used to align an FSO transmitter and receiver to attain line-of-sight, which is required for effective operation of FSO communications. Transceiver motion is not only associated to mobile stations but also to temporary displacements experienced by stationary FSO terminals, such as in building-to-building FSO communications. This survey categorizes ATP mechanisms according to their working principles, use cases, and implementation technology. This paper also discusses advantages and disadvantages of the surveyed ATP mechanisms, and presents a discussion on challenges and future research.

A Survey on Acquisition, Tracking, and Pointing Mechanisms for Mobile Free-Space Optical Communications

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.

Algorithmic Fairness

The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many ways that advertisers can target---or exclude---particular groups of users seeing their ads, comparatively little attention has been paid to the implications of the platform's ad delivery process, comprised of the platform's choices about which users see which ads. 
It has been hypothesized that this process can "skew" ad delivery in ways that the advertisers do not intend, making some users less likely than others to see particular ads based on their demographic characteristics. In this paper, we demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platform's own predictions about the "relevance" of ads to different groups of users. We find that both the advertiser's budget and the content of the ad each significantly contribute to the skew of Facebook's ad delivery. Critically, we observe significant skew in delivery along gender and racial lines for "real" ads for employment and housing opportunities despite neutral targeting parameters. 
Our results demonstrate previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive. This underscores the need for policymakers and platforms to carefully consider the role of the ad delivery optimization run by ad platforms themselves---and not just the targeting choices of advertisers---in preventing discrimination in digital advertising.

Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes

Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.

Racial categories in machine learning

Recently, online targeted advertising platforms like Facebook have been criticized for allowing advertisers to discriminate against users belonging to sensitive groups, i.e., to exclude users belonging to a certain race or gender from receiving their ads. Such criticisms have led, for instance, Facebook to disallow the use of attributes such as ethnic affinity from being used by advertisers when targeting ads related to housing or employment or financial services. In this paper, we show that such measures are far from sufficient and that the problem of discrimination in targeted advertising is much more pernicious. We argue that discrimination measures should be based on the targeted population and not on the attributes used for targeting. We systematically investigate the different targeting methods offered by Facebook for their ability to enable discriminatory advertising. We show that a malicious advertiser can create highly discriminatory ads without using sensitive attributes. Our findings call for exploring fundamentally new methods for mitigating discrimination in online targeted advertising.

/pdf/potential-for-discrimination-in-online-targeted-advertising-1qh0n018gh.pdf

Potential for discrimination in online targeted advertising

We describe a channel sounding measurement campaign for cellular broadband wireless communications with high speed trains that was carried out in the context of the project CORRIDOR. The campaign combines MIMO and carrier aggregation to achieve very high throughputs. We compare two different scenarios, the first one reflects a cellular deployment, where the base station is about 1km away from the railway line. The second scenario corresponds to a railway deployed network, where the base station is located directly next the railway line. We present the general parameters of the measurement campaign and some results of Power Delay Profiles and Doppler Spectra and their evolution over time. Finally we present a simple channel model that captures the main effects observed in the measurements.

/pdf/broadband-wireless-channel-measurements-for-high-speed-1zhrmjwt21.pdf

Broadband wireless channel measurements for high speed trains

Most industrial Saccharomyces cerevisiae strains used in food or biotechnology processes are benign. However, reports of S. cerevisiae infections have emerged and novel strains continue to be developed. In order to develop recommendations for the human health risk assessment of S. cerevisiae strains, we conducted a literature review of current methods used to characterize their pathogenic potential and evaluated their relevance towards risk assessment. These studies revealed that expression of virulence traits in S. cerevisiae is complex and depends on many factors. Given the opportunistic nature of this organism, an approach using multiple lines of evidence is likely necessary for the reasonable prediction of the pathogenic potential of a particular strain. Risk assessment of S. cerevisiae strains would benefit from more research towards the comparison of virulent and non-virulent strains in order to better understand those genotypic and phenotypic traits most likely to be associated with pathogenicity.

Review of current methods for characterizing virulence and pathogenicity potential of industrial Saccharomyces cerevisiae strains towards humans

In this paper, we develop an unsupervised generative clustering framework that combines the Variational Information Bottleneck and the Gaussian Mixture Model. Specifically, in our approach, we use the Variational Information Bottleneck method and model the latent space as a mixture of Gaussians. We derive a bound on the cost function of our model that generalizes the Evidence Lower Bound (ELBO) and provide a variational inference type algorithm that allows computing it. In the algorithm, the coders' mappings are parametrized using neural networks, and the bound is approximated by Monte Carlo sampling and optimized with stochastic gradient descent. Numerical results on real datasets are provided to support the efficiency of our method.

Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding

The rise of algorithmic decision making led to active researches on how to define and guarantee fairness, mostly focusing on one-shot decision making. In several important applications such as hiring, however, decisions are made in multiple stage with additional information at each stage. In such cases, fairness issues remain poorly understood. 
In this paper we study fairness in $k$-stage selection problems where additional features are observed at every stage. We first introduce two fairness notions, local (per stage) and global (final stage) fairness, that extend the classical fairness notions to the $k$-stage setting. We propose a simple model based on a probabilistic formulation and show that the locally and globally fair selections that maximize precision can be computed via a linear program. We then define the price of local fairness to measure the loss of precision induced by local constraints; and investigate theoretically and empirically this quantity. In particular, our experiments show that the price of local fairness is generally smaller when the sensitive attribute is observed at the first stage; but globally fair selections are more locally fair when the sensitive attribute is observed at the second stage---hence in both cases it is often possible to have a selection that has a small price of local fairness and is close to locally fair.

/pdf/the-price-of-local-fairness-in-multistage-selection-g3b6rtv5f5.pdf

George Arvanitakis

Papers

Potential for discrimination in online targeted advertising

Broadband wireless channel measurements for high speed trains

Review of current methods for characterizing virulence and pathogenicity potential of industrial Saccharomyces cerevisiae strains towards humans

Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding

The Price of Local Fairness in Multistage Selection