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Artificial intelligence assistants and risk: framing a connectivity risk narrative
Cunneen, Martin; Mullins, Martin; Murphy, Finbarr
The full text of this article will not be available in ULIR until the embargo expires on the 07/11/2020 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. peer-reviewed
Keyword(s): connectivity; data monetisation; narratology; risk
Publication Date:
2019
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Citation(s): AI & Society;
https://doi.org/10.1007/s00146-019-00916-9
Publisher(s): Springer
First Indexed: 2020-02-06 07:23:30 Last Updated: 2020-02-06 07:23:30