Fig. 1. Mock examples of notification messages with manipulative, rude, or tactless framing The Computers Are Social Actors paradigm suggests people exhibit social/anthropomorphic biases in their treatment of technology.Such insights have encouraged the design of interfaces that interact with users in more social (chatty or even friend-like) ways.However, in typical 'dark pattern' fashion, social-emotional responses to systems as (seemingly sentient) agents can be harnessed to manipulate user behaviour. An increasingly common example is app notifications that assume person-like tones to persuade or pressure users into compliance. Regardless of being manipulative, difficulties meeting contextual social expectations can make automated social acting seem rude, invasive, tactless, and even disrespectful-constituting 'social' anti-patterns. This paper explores ways to improve how automated systems treat people in interactions. We mixed four qualitative methods to elicit user experiences and preferences regarding how automated systems "talk" to/at them. We identify an emerging 'social' class of dark and anti-patterns, and propose guidelines for helping (social) interfaces treat users in more respectful, tactful, and autonomy-supportive ways.CCS Concepts: • Human-centered computing → Natural language interfaces; Empirical studies in interaction design; User centered design; Contextual design.
This article considers the remaining hindrances for natural language processing technologies in achieving open and natural (human-like) interaction between humans and computers. Although artificially intelligent (AI) systems have been making great strides in this field, particularly with the development of deep learning architectures that carry surface-level statistical methods to greater levels of sophistication, these systems are yet incapable of deep semantic analysis, reliable translation, and generating rich answers to open-ended questions. I consider how the process may be facilitated from our side, first, by altering some of our existing language conventions (which may occur naturally) if we are to proceed with statistical approaches, and secondly, by considering possibilities in using a formalised artificial language as an auxiliary medium, as it may avoid many of the inherent ambiguities and irregularities that make natural language difficult to process using rule-based methods. As current systems have been predominantly English-based, I argue that a formal auxiliary language would not only be a simpler and more reliable medium for computer processing, but may also offer a more neutral, easy-to-learn lingua franca for uniting people from different linguistic backgrounds with none necessarily having the upper hand.
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