The complexity and increasingly tight coupling of supply chains poses a major logistical challenge for leading companies. Another challenge is that leading companies -- under pressure from consumers, a critical public and legislative measures such as supply chain laws -- have to take more responsibility than before for their suppliers' labour standards. In this paper, we discuss a new approach that leading companies are using to try to address these challenges: algorithmic prediction of business risks, but also environmental and social risks. We describe the technical and cultural conditions for algorithmic prediction and explain how -- from the perspective of leading companies -- it helps to address both challenges. We then develop scenarios on how and with what kind of social consequences algorithmic prediction can be used by leading companies. From the scenarios, we derive policy options for different stakeholder groups to help develop algorithmic prediction towards improving labour standards and worker voice. -- Die Komplexit\"at und zunehmend enge Kopplung vieler Lieferketten stellt eine gro{\ss}e logistische Herausforderung f\"ur Leitunternehmen dar. Eine weitere Herausforderung besteht darin, dass Leitunternehmen -- gedr\"angt durch Konsument:innen, eine kritische \"Offentlichkeit und gesetzgeberische Ma{\ss}nahmen wie die Lieferkettengesetze -- st\"arker als bisher Verantwortung f\"ur Arbeitsstandards in ihren Zulieferbetrieben \"ubernehmen m\"ussen. In diesem Beitrag diskutieren wir einen neuen Ansatz, mit dem Leitunternehmen versuchen, diese Herausforderungen zu bearbeiten: die algorithmische Vorhersage von betriebswirtschaftlichen, aber auch \"okologischen und sozialen Risiken. Wir beschreiben die technischen und kulturellen Bedingungen f\"ur algorithmische Vorhersage und erkl\"aren, wie diese -- aus Perspektive von Leitunternehmen -- bei der Bearbeitung beider Herausforderungen hilft. Anschlie{\ss}end entwickeln wir Szenarien, wie und mit welchen sozialen Konsequenzen algorithmische Vorhersage durch Leitunternehmen eingesetzt werden kann. Aus den Szenarien leiten wir Handlungsoptionen f\"ur verschiedene Stakeholder-Gruppen ab, die dabei helfen sollen, algorithmische Vorhersage im Sinne einer Verbesserung von Arbeitsstandards und Workers' Voice weiterzuentwickeln.

Digitisation, automation and datafication permeate policing and justice more and more each year -- from predictive policing methods through recidivism prediction to automated biometric identification at the border. The sociotechnical issues surrounding the use of such systems raise questions and reveal problems, both old and new. Our article reviews contemporary issues surrounding automation in policing and the legal system, finds common issues and themes in various different examples, introduces the distinction between human "retail bias" and algorithmic "wholesale bias", and argues for shifting the viewpoint on the debate to focus on both workers' rights and organisational responsibility as well as fundamental rights and the right to an effective remedy.

Nowhere could the application of machine learning prove more important -- nor more risky -- than in law enforcement and national security. In this article, I'll review this area and then cover six perplexing and pressing ethical quandaries that arise. Predictive policing introduces a scientific element to law enforcement decisions, such as whether to investigate or detain, how long to sentence, and whether to parole. In making such decisions, judges and officers take into consideration the probability a suspect or defendant will be convicted for a crime in the future -- which is commonly the dependent variable for a predictive policing model. These independent variables may include prior convictions, income level, employment status, family background, neighborhood, education level, and the behavior of family and friends.

Predictive policing is a data-based, predictive analytical technique used in law enforcement. In this paper, we give an overview of the current situation in Austria and discuss technical, sociopolitical and legal questions raised by the use of PP, such as the lack of awareness of discriminatory structures in society, the biases in data underlying PP and the lack of reflection on the basic premises and feedback mechanisms of PP. Violations of fundamental rights without cause are not allowed by the Austrian Code of Criminal Procedure (Strafproze{\ss}ordnung, StPO), the Security Police Act (Sicherheitspolizeigesetz, SPG) or the Act concerning Police Protection of the State (Polizeiliches Staatsschutzgesetz, PStSG); the principle of allowing police intervention only on the basis of concrete threats or suspicion must remain absolute. Considering the numerous problems (not least from the point of view of legal policy), we conclude that the use of PP should be eschewed and that resources and planning should instead be focussed on solving the social problems which actually cause crime. ----- Predictive Policing ist ein datenbasiertes und prognosegetriebenes Modell f\"ur Polizeiarbeit. Wir geben in diesem Artikel einen \"Uberblick \"uber den aktuellen Stand in \"Osterreich und diskutieren technische, politisch-gesellschaftliche und rechtliche Probleme, die sich daraus ergeben -- etwa das mangelhafte Bewusstsein f\"ur Prozesse gesellschaftlicher Diskriminierung, die verzerrte Datenbasis, die PP zugrundeliegt, und fehlende Reflexion \"uber zugrundeliegende Annahmen und R\"uckkopplungseffekte. Anlasslose Grundrechtseingriffe sind weder durch die StPO noch das SPG oder das PStSG gedeckt; dem Grundgedanken, dass Polizei erst bei konkreter Gefahrenlage oder Tatverdacht t\"atig werden darf, muss weiterhin Rechnung getragen werden. Aus unserer Sicht sollte angesichts der zahlreichen Probleme (und auch aus rechtspolitischen Erw\"agungen) auf PP verzichtet werden und stattdessen Ressourcen und \"Uberlegung in die L\"osung jener gesellschaftlicher Probleme investiert werden, die zu Kriminalit\"at f\"uhren.

Artificial intelligence (AI) has taken the world by storm, becoming a marketing buzzword and hotly commented subject in the press. Over the last few years there have been several important milestones in AI, in particular in terms of image, pattern and speech recognition, language comprehension and autonomous vehicles. Advancements such as these have prompted the healthcare, automotive, financial, communications and many more industries to adopt AI in pursuit of its transformative potential. How can AI benefit law enforcement and why might this be dangerous? Law enforcement is an information-based activity.

Originally published by Scientific American. Law enforcement's use of predictive analytics recently came under fire again. Dartmouth researchers made waves reporting that simple predictive models--as well as nonexpert humans--predict crime just as well as the leading proprietary analytics software. That the leading software achieves (only) human-level performance might not actually be a deadly blow, but a flurry of press from dozens of news outlets has quickly followed. In any case, even as this disclosure raises questions about one software tool's credibility, a more enduring, inherent quandary continues to plague predictive policing.

In the first part of this three-article series about artificial intelligence in science fiction vs. real-life AI, we talked about robots -- both real and as depicted in sci-fi starting in the early 20th century. Then, we visited advanced AI like the mutinous shipboard computer HAL 9000. When does it start getting real? We'll conclude with a brief look at today's robot police and computer-assisted crime prediction. It turns out RoboCop is real...sort of.

Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which \emph{reported} incidents of crime (those reported by residents) and \emph{discovered} incidents of crime (i.e. those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest.

There's a new kind of software that claims to help law enforcement agencies reduce crime, by using algorithms to predict where crimes will happen and directing more officers to those areas. It's called "predictive policing," and it's already being used by dozens of police departments all over the country, including the Los Angeles, Chicago and Atlanta Police Departments. Aside from the obvious "Minority Report" pre-crime allusions, there has been a tremendous amount of speculation about what the future of predictive policing might hold. Could people be locked up just because a computer model says that they are likely to commit a crime? Could all crime end altogether, because an artificial intelligence gets so good at predicting when crimes will occur?