At a time when the European Union and its Member States are constantly adopting measures to combat serious crime and terrorism, particularly through the prism of data protection rules, the CJEU is acting as a bulwark by imposing compliance with strict conditions, thereby encroaching on national rules of criminal procedure, which are initially the responsibility of the Member States. In this contribution, we will examine how and on what basis the Ligue des Droits Humains was able to get the CJEU to rule on the Passenger Name Records Directive, and to what extent this action was indeed “strategic.”

We address several issues that are raised by Bentler and Tanaka's [1983] discussion of Rubin and Thayer [1982]. Our conclusions are: standard methods do not completely monitor the possible existence of multiple local maxima; summarizing inferential precision by the standard output based on second derivatives of the log likelihood at a maximum can be inappropriate, even if there exists a unique local maximum; EM and LISREL can be viewed as complementary, albeit not entirely adequate, tools for factor analysis.

A general approach for fitting a model to a data matrix by weighted least squares (WLS) is studied. This approach consists of iteratively performing (steps of) existing algorithms for ordinary least squares (OLS) fitting of the same model. The approach is based on minimizing a function that majorizes the WLS loss function. The generality of the approach implies that, for every model for which an OLS fitting algorithm is available, the present approach yields a WLS fitting algorithm. In the special case where the WLS weight matrix is binary, the approach reduces to missing data imputation.

We study the problem of fitting a piecewise affine (PWA) function to input–output data. Our algorithm divides the input domain into finitely many regions whose shapes are specified by a user-provided template and such that the input–output data in each region are fit by an affine function within a user-provided error tolerance. We first prove that this problem is NP-hard. Then, we present a top-down algorithmic approach for solving the problem. The algorithm considers subsets of the data points in a systematic manner, trying to fit an affine function for each subset using linear regression. If regression fails on a subset, the algorithm extracts a minimal set of points from the subset (an unsatisfiable core) that is responsible for the failure. The identified core is then used to split the current subset into smaller ones. By combining this top-down scheme with a set-covering algorithm, we derive an overall approach that provides optimal PWA models for a given error tolerance, where optimality refers to minimizing the number of pieces of the PWA model. We demonstrate our approach on three numerical examples that include PWA approximations of a widely used nonlinear insulin–glucose regulation model and a double inverted pendulum with soft contacts.

While governments have long discussed the promise of delegating important decisions to machines, actual use often lags. Consequently, we know little about the variation in the deployment of such delegations in large numbers of similar governmental organizations. Using data from crime laboratories in the United States, we examine the uneven distribution over time of a specific, well-known expert system for ballistics imaging for a large sample of local and regional public agencies; an expert system is an inference engine joined with a knowledge base. Our statistical model is informed by the push-pull-capability theory of innovation in the public sector. We test hypotheses about the probability of deployment and provide evidence that the use of this expert system varies with the pull of agency task environments and the enabling support of organizational resources—and that the impacts of those factors have changed over time. Within this context, we also present evidence that general knowledge of the use of expert systems has supported the use of this specific expert system in many agencies. This empirical case and this theory of innovation provide broad evidence about the historical utilization of expert systems as algorithms in public sector applications.

Within Holocaust studies, there has been an increasingly uncritical acceptance that by engaging with social media, Holocaust memory has shifted from the ‘era of the witness’ to the ‘era of the user’ (Hogervorst 2020). This paper starts by problematising this proposition. This claim to a paradigmatic shift implies that (1) the user somehow replaces the witness as an authority of memory, which neglects the wealth of digital recordings of witnesses now circulating in digital spaces and (2) agency online is solely human-centric, a position that ignores the complex negotiations between corporations, individuals, and computational logics that shape our digital experiences. This article proposes instead that we take a posthumanist approach to understanding Holocaust memory on, and with, social media. Adapting Barad's (2007) work on entanglement to memory studies, we analyse two case studies on TikTok: the #WeRemember campaign and the docuseries How To: Never Forget to demonstrate: (1) the usefulness of reading Holocaust memory on social media through the lens of entanglement which offers a methodology that accounts for the complex network of human and non-human actants involved in the production of this phenomenon which are simultaneously being shaped by it. (2) That professional memory institutions and organisations are increasingly acknowledging the use of social media for the sake of Holocaust memory. Nevertheless, we observe that in practice the significance of technical actancy is still undervalued in this context.

As governments increasingly adopt algorithms and artificial intelligence (AAI), we still know comparatively little about citizens’ support for algorithmic government. In this paper, we analyze how many and what kind of reasons for government use of AAI citizens support. We use a sample of 17,000 respondents from 16 OECD countries and find that opinions on algorithmic government are divided. A narrow majority of people (55.6%) support a majority of reasons for using algorithmic government, and this is relatively consistent across countries. Results from multilevel models suggest that most of the cross-country variation is explained by individual-level characteristics, including age, education, gender, and income. Older and more educated respondents are more accepting of algorithmic government, while female and low-income respondents are less supportive. Finally, we classify the reasons for using algorithmic government into two types, “fairness” and “efficiency,” and find that support for them varies based on individuals’ political attitudes.

Services offered by genealogy companies are increasingly underpinned by computational remediation and algorithmic power. Users are encouraged to employ a variety of mobile web and app plug-ins to create progressively more sophisticated forms of synthetic media featuring their (often deceased) ancestors. As the promotion of deepfake and voice-synthesizing technologies intensifies within genealogical contexts – aggrandised as mechanisms for ‘bringing people back to life’ – we argue it is crucial that we critically examine these processes and the socio-technical infrastructures that underpin them, as well as their mnemonic impacts. In this article, we present a study of two AI-enabled services released by the genealogy company MyHeritage: Deep Nostalgia (launched 2020), and DeepStory (2022). We carry out a close critical reading of these services and the outputs they produce which we understand as examples of ‘remediated memory’ (Kidd and Nieto McAvoy 2023) shaped by corporate interests. We examine the distribution of agency where the promotion by these platforms of unique and personalised experiences comes into tension with the propensity of algorithms to homogenise. The analysis intersects with nascent ethical debates about the exploitative and extractive qualities machine learning. Our research unpacks the social and (techno-)material implications of these technologies, demonstrating an enduring individual and collective need to connect with our past(s), and to test and extend our memories and recollections through increasingly intense and proximate new media formats.

Public decision-makers incorporate algorithm decision aids, often developed by private businesses, into the policy process, in part, as a method for justifying difficult decisions. Ethicists have worried that over-trust in algorithm advice and concerns about punishment if departing from an algorithm’s recommendation will result in over-reliance and harm democratic accountability. We test these concerns in a set of two pre-registered survey experiments in the judicial context conducted on three representative U.S. samples. The results show no support for the hypothesized blame dynamics, regardless of whether the judge agrees or disagrees with the algorithm. Algorithms, moreover, do not have a significant impact relative to other sources of advice. Respondents who are generally more trusting of elites assign greater blame to the decision-maker when they disagree with the algorithm, and they assign more blame when they think the decision-maker is abdicating their responsibility by agreeing with an algorithm.

The gradual digitization of EU migration policies is turning external borders into AI-driven filters that limit access to fundamental rights for people from third countries according to risk indicators. An unshakeable confidence in the reliability of technological devices and their ability to predict the future behaviour of incoming foreigners is leading towards the datafication of EU external frontiers. What happens if the supposedly infallible algorithms are wrong? The article aims to understand the consequences of algorithmic errors on the lives of migrants, refugees and asylum seekers arriving in the European Union. This contribution investigates the socio-political implications of deploying data-driven solutions at the borders in an attempt to problematize the techno-solutionist approach of EU migratory policies and its fundamental rights impact on affected individuals.

We outline a theory of algorithmic attention rents in digital aggregator platforms. We explore the way that as platforms grow, they become increasingly capable of extracting rents from a variety of actors in their ecosystems—users, suppliers, and advertisers—through their algorithmic control over user attention. We focus our analysis on advertising business models, in which attention harvested from users is monetized by reselling the attention to suppliers or other advertisers, though we believe the theory has relevance to other online business models as well. We argue that regulations should mandate the disclosure of the operating metrics that platforms use to allocate user attention and shape the “free” side of their marketplace, as well as details on how that attention is monetized.