Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for algorithmic fairness should therefore be approached through a sociotechnical lens. We advance the discourse on algorithmic fairness as a sociotechnical phenomenon. Our research objective is to embed AF in the sociotechnical view of IS. Specifically, we elaborate on why outcomes of a system that uses algorithmic means to assure fairness depends on mutual influences between technical and social structures. This perspective can generate new insights that integrate knowledge from both technical fields and social studies. Further, it spurs new directions for IS debates. We contribute as follows: First, we problematize fundamental assumptions in the current discourse on algorithmic fairness based on a systematic analysis of 310 articles. Second, we respond to these assumptions by theorizing algorithmic fairness as a sociotechnical construct. Third, we propose directions for IS researchers to enhance their impacts by pursuing a unique understanding of sociotechnical algorithmic fairness. We call for and undertake a holistic approach to AF. A sociotechnical perspective on algorithmic fairness can yield holistic solutions to systemic biases and discrimination.

While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population when the target population is not well-represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a class of novel conditional cross-design synthesis estimators that combine randomized and observational data, while addressing their respective biases. The estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. We apply these methods to estimate the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City.

While deep neural networks have achieved great success on the graph analysis, recent works have shown that they are also vulnerable to adversarial attacks where fraudulent users can fool the model with a limited number of queries. Compared with adversarial attacks on image classification, performing adversarial attack on graphs is challenging because of the discrete and non-differential nature of a graph. To address these issues, we proposed Cluster Attack, a novel adversarial attack by introducing a set of fake nodes to the original graph which can mislead the classification on certain victim nodes. Specifically, we query the victim model for each victim node to acquire their most adversarial feature, which is related to how the fake node's feature will affect the victim nodes. We further cluster the victim nodes into several subgroups according to their most adversarial features such that we can reduce the searching space. Moreover, our attack is performed in a practical and unnoticeable manner: (1) We protect the predicted labels of nodes which we are not aimed for from being changed during attack. (2) We attack by introducing fake nodes into the original graph without changing existing links and features. (3) We attack with only partial information about the attacked graph, i.e., by leveraging the information of victim nodes along with their neighbors within $k$-hop instead of the whole graph. (4) We perform attack with a limited number of queries about the predicted scores of the model in a black-box manner, i.e., without model architecture and parameters. Extensive experiments demonstrate the effectiveness of our method in terms of the success rate of attack.

Olaf Scholz, the centre-left Social Democrat (SPD) candidate to succeed Angela Merkel, is often described as boring, but could be on the verge of a sensational upset after Sunday's election. With polls showing the SPD narrowly ahead of Angela Merkel's CDU-CSU conservative alliance, Scholz may have achieved something many would have thought impossible just a year ago. His SPD scored just 20.5 percent in Germany's last election in 2017 and has had a difficult few years in coalition with the CDU-CSU, but looks on course to win at least 25 percent of the vote this time. "It's going to be a long election night, that's for sure," Scholz said after the first estimates were released. "But this is certain: that many citizens have put their crosses next to the SPD because they want there to be a change in government and also because they want the next chancellor to be called Olaf Scholz."

A surge of Haitians is being deported, or let free. As President Joe Biden vacationed at Rehoboth Beach, the disaster at the U.S. southern border continued to metastasize. Anyone who has seen pictures of thousands of people crossing the Rio Grande en masse knows the administration has achieved complete failure. Anyone who has seen the overhead drone footage of more than 12,000 people gathered under one bridge in South Texas knows that massive, historic incompetence is being allowed to flourish. At the same time, it's clear that – despite all the Big Government Socialists in Biden's party who complain about America – we remain the one country people desperately try to get into.

The'Outnumbered' panel reacts to Sunny Hostin and Ana Navarro being pulled from the set moments before the vice president was set to arrive Ana Navarro, one of two co-hosts who were pulled from ABC's "The View" live on air Friday due to positive COVID-19 tests, has since revealed the results that caused the chaos were false positives. Producers informed Navarro and Sunny Hostin in their earpieces halfway through Friday's broadcast that they would have to leave the Hot Topics table, leaving Joy Behar and Sara Haines to conduct the rest of the show on their own. The remaining hosts often struggled to kill time, at one point taking questions from the audience, but often not being able to hear the questions that were muffled by their masks. Friday's drama was even more pronounced considering Navarro and Hostin were pulled just as Vice President Kamala Harris was on her way to the studio for an in-person interview. Even though Harris made it to the building, producers explained her appearance would end up taking place remotely from a separate room out of precaution.

Increasingly, this message is finding a platform and it's beginning to shape AI's development meaningfully. The latest proposed regulations from the European Union, for example, take significant steps in the right direction by defining high-risk use cases, for example. The data science community wants to build models that align to societal values and improve outcomes. This well thought-out proposal from the EU will enable innovation and industry growth by standardising expectations among practitioners.

Last year's Castle Fire in California's Sierra Nevada is estimated to have killed more than 10 percent of the world's giant sequoias, the tallest trees on earth. Sequoias can live through many fires over life spans that last thousands of years; their bark is fire-resistant and they rely on fire to reproduce. But as climate change intensifies, wildfires are growing larger and more intense. According to state officials, six of the seven largest wildfires in California history took place roughly within the past year. To help restore fire-ravaged forests and temper the effects of climate change, a handful of young companies want to scatter seeds from drones.

With the withdrawal of the U.K. from the European Union, MHRA as part of its new Brexit freedoms is moving to update the country's regulations for software and AI as a medical device without the burden of accommodating the regulatory approaches of EU members. "These measures demonstrate the U.K.'s commitment, following our exit from the European Union, to drive innovation in healthcare and improve patient outcomes," states MHRA's announcement. "Regulatory measures will be updated to further protect patient safety and take account of these technological advances." AI and SaMD technologies have the potential for better diagnosing and treating a wide variety of diseases, but FDA has yet to finalize a regulatory framework for machine learning-based software as a medical device. The agency is considering a total product lifecycle-based regulatory framework for adaptive or continuously learning algorithms.

The existential threat of Covid-19 has highlighted an acute need to develop working therapeutics against emerging health concerns. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data need time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Jameel Clinic for Machine Learning in Health to ask: How can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.