Introduction Social media has created opportunities for children to gather social support online (Blackwell et al., 2016; Gonzales, 2017; Jackson, Bailey, & Foucault Welles, 2018; Khasawneh, Rogers, Bertrand, Madathil, & Gramopadhye, 2019; Ponathil, Agnisarman, Khasawneh, Narasimha, & Madathil, 2017). However, social media also has the potential to expose children and adolescents to undesirable behaviors. Research showed that social media can be used to harass, discriminate (Fritz & Gonzales, 2018), dox (Wood, Rose, & Thompson, 2018), and socially disenfranchise children (Page, Wisniewski, Knijnenburg, & Namara, 2018). Other research proposes that social media use might be correlated to the significant increase in suicide rates and depressive symptoms among children and adolescents in the past ten years (Mitchell, Wells, Priebe, & Ybarra, 2014). Evidence based research suggests that suicidal and unwanted behaviors can be promulgated through social contagion effects, which model, normalize, and reinforce self-harming behavior (Hilton, 2017). These harmful behaviors and social contagion effects may occur more frequently through repetitive exposure and modelling via social media, especially when such content goes “viral” (Hilton, 2017). One example of viral self-harming behavior that has generated significant media attention is the Blue Whale Challenge (BWC). The hearsay about this challenge is that individuals at allmore »
Modelling Ethical Algorithms in Autonomous Vehicles Using Crash Data
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.
- Award ID(s):
- 1734521
- Publication Date:
- NSF-PAR ID:
- 10257290
- Journal Name:
- IEEE Transactions on Intelligent Transportation Systems
- Page Range or eLocation-ID:
- 1 to 10
- ISSN:
- 1524-9050
- Sponsoring Org:
- National Science Foundation
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