Search for: All records

Though general awareness around it may be low, space cyberattacks are an increasingly urgent problem given the vital role that space systems play in the modern world. Open-source or public discussions about it typically revolve around only a couple generic scenarios, namely satellite hacking and signals jamming or spoofing. But there are so many more possibilities. The report offers a scenario-prompt generator—a taxonomy of sorts, called the ICARUS matrix—that can create more than 4 million unique scenario-prompts. We will offer a starting set of 42 scenarios, briefly describing each one, to begin priming the imagination-pump so that many more researchers can bring their diverse expertise and perspectives to bear on the problem. A failure to imagine novel scenarios is a major risk in being taken by surprise and severely harmed by threat actors who are constantly devising new ways, inventive and resourceful ways, to breach the digital systems that control our wired world. To stay vigilant, defenders likewise need to be imaginative to keep up in this adversarial dance between hunter and prey in cybersecurity. More than offering novel scenarios, we will also explore the drivers of the space cybersecurity problem, which include at least seven factors we have identified. For instance, the shared threat of space debris would seem to push rational states and actors to avoid kinetic conflicts in orbit, which weighs in favor of cyberoperations as the dominant form of space conflicts. Outer space is the next frontier for cybersecurity. To guard against space cyberattacks, we need to understand and anticipate them, and imagination is at the very heart of both cybersecurity and frontiers. 

While the social and ethical risks of PAPM have been widely discussed, little guidance has been provided to police departments, community advocates, or to developers of place-based algorithmic patrol management systems (PAPM systems) about how to mitigate those risks. The framework outlined in this report aims to fill that gap. This document proposes best practices for the development and deployment of PAPM systems that are ethically informed and empirically grounded. Given that the use of place-based policing is here to stay, it is imperative to provide useful guidance to police departments, community advocates, and developers so that they can address the social risks associated with PAPM. We strive to develop recommendations that are concrete, practical, and forward-looking. Our goal is to parry critiques of PAPM into practical recommendations to guide the ethically sensitive design and use of data-driven policing technologies. 

In this paper, we outline a new method for evaluating the human impact of machine-learning (ML) applications. In partnership with Underwriters Laboratories Inc., we have developed a framework to evaluate the impacts of a particular use of machine learning that is based on the goals and values of the domain in which that application is deployed. By examining the use of artificial intelligence (AI) in particular domains, such as journalism, criminal justice, or law, we can develop more nuanced and practically relevant understandings of key ethical guidelines for artificial intelligence. By decoupling the extraction of the facts of the matter from the evaluation of the impact of the resulting systems, we create a framework for the process of assessing impact that has two distinctly different phases. 

Few sectors are more affected by COVID-19 than higher education. There is growing recognition that reopening the densely populated communities of higher education will require surveillance technologies, but many of these technologies pose threats to the privacy of the very students, faculty, and staff they are meant to protect. The authors have a history of working with our institution’s governing bodies to provide ethical guidance on the use of technologies, especially including those with significant implications for privacy. Here, we draw on that experience to provide guidelines for using surveillance technologies to reopen college campuses safely and responsibly, even under the specter of covid. We aim to generalize our recommendations, so they are sensitive to the practical realities and constraints that universities face. 

In this paper we provide a proof of principle of a new method for addressing the ethics of autonomous vehicles (AVs), the Data-Theories Method, in which vehicle crash data is combined with philosophical ethical theory to provide a guide to action for AV algorithm design. We use this method to model three scenarios in which an AV is exposed to risk on the road, and determine possible actions for the AV. We then examine how different philosophical perspectives on agent partiality, or the degree to which one can act in one’s own self-interest, might address each scenario. This method shows why modelling the ethics of AVs using data is essential. First, AVs may sometimes have options that human drivers do not, and designing AVs to mimic the most ethical human driver would not ensure that they do the right thing. Second, while ethical theories can often disagree about what should be done, disagreement can be reduced and compromises found with a more complete understanding of the AV’s choices and their consequences. Finally, framing problems around thought experiments may elicit preferences that are divergent with what individuals might prefer once they are provided with information about the real risks for a scenario. Our method provides a principled and empirical approach to productively address these problems and offers guidance on AV algorithm design.