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This article aims to introduce a degree of technological and ethical realism to the framing of autonomous vehicle perception and decisionality. The objective is to move the socioethical dialog surrounding autonomous vehicle decisionality from the dominance of "trolley framings" to more pressing ethical issues. The article argues that more realistic ethical framings of autonomous vehicle technologies should focus on the matters of HMI, machine perception, classification, and data privacy, which are some distance from the decisionality framing premise of the MIT Moral Machine experiment. To support this claim the article appeals to state-of-the-art technologies and emerging technologies concerning autonomous vehicle perception and decisionality, as a means to inform and frame ethical contexts. This is further supported by considering a context specific ethical framing for each time phase we anticipate regarding emerging autonomous vehicle technology.

Our social relations are changing, we are now not just talking to each other, but we are now also talking to artificial intelligence (AI) assistants. We claim AI assistants present a new form of digital connectivity risk and a key aspect of this risk phenomenon relates to user risk awareness (or lack of) regarding AI assistant functionality. AI assistants present a significant societal risk phenomenon amplified by the global scale of the products and the increasing use in healthcare, education, business, and service industry. However, there appears to be little research concerning the need to not only understand the risks of AI assistant technologies but also how to frame and communicate the risks to users. How can users assess the risks without fully understanding the complexity of the technology? This is a challenging and unwelcome scenario. AI assistant technologies consists of a complex eco-system and demands explicit and precise communication in terms of communicating and contextualising the new digital risk phenomenon. The paper then agues for the need to examine how to best to explain and support both domestic and commercial user risk awareness regarding AI assistants. To this end, we propose the method of creating a risk narrative which is focused on temporal points of changing societal connectivity and contextualised in terms of risk. We claim the connectivity risk narrative provides an effective medium in capturing, communicating, and contextualising the risks of AI assistants in a medium that can support explainability as a risk mitigation mechanism.

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The introduction of connected and autonomous vehicles (CAVs) to the road transport ecosystem will change the manner of collisions. CAVs are expected to optimize the safety of road users and the wider environment, while alleviating traffic congestion and maximizing occupant comfort. The net result is a reduction in the frequency of motor vehicle collisions, and a reduction in the number of injuries currently seen as "preventable." A changing risk ecosystem will introduce new challenges and opportunities for primary insurers. Prior studies have highlighted the economic benefit provided by reductions in the frequency of hazardous events. This economic benefit, however, will be offset by the economic detriment incurred by emerging risks and the increased scrutiny placed on existing risks. We posit four plausible scenarios detailing how an introduction of these technologies could result in a larger relative rate of injury claims currently characterized as tail-risk events. In such a scenario, the culmination of these losses will present as a second "hump" in actuarial loss models. We discuss how CAV risk factors and traffic dynamics may combine to make © 2021 The American Risk and Insurance Association a second "hump" a plausible reality, and discuss a number of opportunities that may arise for primary insurers from a changing road environment. 1 | INTRODUCTION The introduction of connected and autonomous vehicles (CAVs) 1 is expected to have a profound impact on the landscape of road transport risk. These vehicles are expected to introduce tiered reductions in the frequency and severity of motor vehicle collisions. Each tiered reduction represents the additional safety benefits provided by increased levels of vehicle automation (Table 1). A set of projections for expected availability of CAVs, according to the vehicles' own manufacturers, detail that highly automated vehicles are expected to be available by 2030 (Grace & Ping, 2018). Current literature 2 on CAV safety detail how tiered reductions will occur through risk-mitigating advanced driver assistance systems (ADASs) 3 and wireless communication software. The latter is otherwise known as V2X 4 communication. In contrast to conventional vehicles, which require full navigational input from human drivers, vehicles equipped with ADAS technologies can improve driving efficiencies and avoid oncoming safety hazards (Scanlon et al., 2015). With the availability of a suite of ADAS technologies, navigation software, and V2X communication software, CAVs are expected to reduce collision rates. More importantly, CAVs are expected to the reduce the frequency of injuries stemming from motor vehicle collisions (Bareiss et al., 2019). This expectation is due to their ability to predict and react to oncoming hazards at a level that human drivers cannot attain, while remaining free of human fallibilities such as distracted or impaired driving behavior (Fagnant & Kockelman, 2015). Furthermore, in the event that collisions do transpire, safety-optimized vehicle design and ADAS tech...

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