Fairness and Bias in Algorithmic Hiring

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders

The Effect of Steroid Treatment on Lipocortin Immunoreactivity of Rat Brain

Lipocortin-1, lipocortin-2 and lipocortin-5 were immunohistochemically assessed in rats. Apart from animals receiving no treatment, other animals received pretreatment with methylprednisolone, or the 21-aminosteroid U-74389F. Whereas Hpocortin immunoreactivity was absent in the greater part of the brain in animals not pretreated with steroid (except in sporadic microglial cells and choroid plexus), there was obvious immunostaining of parenchymatous elements in steroid pretreated animals. In the steroid pretreated animals lipocortin immunoreactivity of the brain tissue may indicate local formation of lipocortin under the influence of steroids that had entered the tissue. The cellular elements which showed immunostaining included meningeal cells, neurones, ependyma, oligodendroglia and capillary endotheHum

My friends, editors, algorithms, and I: Examining audience attitudes to news selection

Prompted by the ongoing development of content personalization by social networks and mainstream news brands, and recent debates about balancing algorithmic and editorial selection, this study explores what audiences think about news selection mechanisms and why. Analysing data from a 26-country survey (N=53,314), we report the extent to which audiences believe story selection by editors and story selection by algorithms are good ways to get news online and, using multi-level models, explore the relationships that exist between individuals’ characteristics and those beliefs. The results show that, collectively, audiences believe algorithmic selection guided by a user’s past consumption behaviour is a better way to get news than editorial curation. There are, however, significant variations in these beliefs at the individual level. Age, trust in news, concerns about privacy, mobile news access, paying for news, and six other variables had effects. Our results are partly in line with current general theory on algorithmic appreciation, but diverge in our findings on the relative appreciation of algorithms and experts, and in how the appreciation of algorithms can differ according to the data that drive them. We believe this divergence is partly due to our study’s focus on news, showing algorithmic appreciation has context-specific characteristics

Serration pattern analysis for differentiating epidermolysis bullosa acquisita from other pemphigoid diseases

Background: Direct immunofluorescence (DIF) microscopy of a skin biopsy specimen is the reference standard for the diagnosis of pemphigoid diseases (PDs). Serration pattern analysis enables the differentiation of epidermolysis bullosa acquisita (EBA) from other PDs using DIF microscopy alone. However, practice gaps need to be addressed in order to implement this technique in the routine diagnostic procedure. Objective: We sought to determine and optimize the technical requirements for serration pattern analysis of DIF microscopy and determine interrater conformity of serration pattern analysis. Methods: We compared serration pattern analysis of routine DIF microscopy from laboratories in Groningen, The Netherlands and Lubeck, Germany with 4 blinded observers. Skin biopsy specimens from 20 patients with EBA and other PDs were exchanged and analyzed. Various factors were evaluated, including section thickness, transport medium, and biopsy specimen processing. Results: The interrater conformity of our 4 observers was 95.7%. Recognition of serration patterns was comparable in samples transported in saline and in Michel's medium and with section thicknesses of 4, 6, and 8 mu m. Limitations: Limitations include our small sample size and the availability of 20 samples that were compared retrospectively. Conclusion: DIF serration pattern analysis is not restricted by variation in laboratory procedures, transport medium, or experience of observers. This learnable technique can be implemented as a routine diagnostic method as an extension of DIF microscopy for subtyping PD. (J Am Acad Dermatol 2018;78:754-9.

Tackling the Algorithmic Control Crisis -the Technical, Legal, and Ethical Challenges of Research into Algorithmic Agents

Algorithmic agents permeate every instant of our online existence. Based on our digital profiles built from the massive surveillance of our digital existence, algorithmic agents rank search results, filter our emails, hide and show news items on social networks feeds, try to guess what products we might buy next for ourselves and for others, what movies we want to watch, and when we might be pregnant. Algorithmic agents select, filter, and recommend products, information, and people; they increasingly customize our physical environments, including the temperature and the mood