a b s t r a c tThe Smart TV is becoming increasingly popular amongst consumers. Many consumers use a Smart TV to gain quick access to the Internet including video on demand, social networking and instant messaging. Most Smart TVs also provide capabilities to connect with external devices such as a USB flash drive, a mobile phone etc. All of these features make a Smart TV a potentially rich source of information for forensic purposes. With increasing utilisation, it is also easier for malicious users to abuse a Smart TV. Therefore a digital forensics study on the field of Smart TV is imperative. This paper proposes new procedures for acquiring, analysing and investigating a Smart TV.
The number of studies on Autonomous Vehicle (AV) ethics discussing decision-making algorithms has increased rapidly, especially since 2017. Many of these studies handle AV ethics through the eye of the trolley problem regarding various moral values, regulations, and matters of law. However, the literature of this field lacks an approach to weighting and prioritizing necessary parameters that need to be considered while making a moral decision to provide insights about AVs’ decision-making algorithms and related legislations as far as we know. This paper bridges the gap in the literature and prioritizes some main criteria indicated by the literature by employing the best–worst method in interval type-2 fuzzy sets based on the evaluations of five experts from different disciplines of philosophy, philosophy of law, and transportation. The criteria included in the weighting were selected according to expert opinions and to the qualitative analysis carried out by coding past studies. The weighing process includes a comparison of four different approaches to the best–worst method. The paper’s findings reveal that social status is the most important criterion, while gender is the least important one. This paper is expected to provide valuable practical insights for Autonomous Vehicle (AV) software developers in addition to its theoretical contribution.
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