Persuasion Meets AI: Ethical Considerations for the Design of Social Engineering Countermeasures

• URL: http://arxiv.org/abs/2009.12853v1
• Date: Sun, 27 Sep 2020 14:24:29 GMT
• Title: Persuasion Meets AI: Ethical Considerations for the Design of Social Engineering Countermeasures
• Authors: Nicolas E. D\'iaz Ferreyra, Esma A\"imeur, Hicham Hage, Maritta Heisel and Catherine Garc\'ia van Hoogstraten
• Abstract summary: Privacy in Social Network Sites (SNSs) like Facebook or Instagram is closely related to people's self-disclosure decisions. Online privacy decisions are often based on spurious risk judgements that make people liable to reveal sensitive data to untrusted recipients. This paper elaborates on the ethical challenges that nudging mechanisms can introduce to the development of AI-based countermeasures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Privacy in Social Network Sites (SNSs) like Facebook or Instagram is closely related to people's self-disclosure decisions and their ability to foresee the consequences of sharing personal information with large and diverse audiences. Nonetheless, online privacy decisions are often based on spurious risk judgements that make people liable to reveal sensitive data to untrusted recipients and become victims of social engineering attacks. Artificial Intelligence (AI) in combination with persuasive mechanisms like nudging is a promising approach for promoting preventative privacy behaviour among the users of SNSs. Nevertheless, combining behavioural interventions with high levels of personalization can be a potential threat to people's agency and autonomy even when applied to the design of social engineering countermeasures. This paper elaborates on the ethical challenges that nudging mechanisms can introduce to the development of AI-based countermeasures, particularly to those addressing unsafe self-disclosure practices in SNSs. Overall, it endorses the elaboration of personalized risk awareness solutions as i) an ethical approach to counteract social engineering, and ii) as an effective means for promoting reflective privacy decisions.

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