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Social machines are systems formed by material and human elements interacting in a structured way. The use of digital platforms as mediators allows large numbers of humans to participate in such machines, which have interconnected AI and human components operating as a single system capable of highly sophisticated behaviour. Under certain conditions, such systems can be understood as autonomous goal-driven agents. Many popular online platforms can be regarded as instances of this class of agent. We argue that autonomous social machines provide a new paradigm for the design of intelligent systems, marking a new phase in AI. After describing the characteristics of goal-driven social machines, we discuss the consequences of their adoption, for the practice of artificial intelligence as well as for its regulation. [ABSTRACT FROM AUTHOR]

Nass’ and Reeves’ media equation paradigm within human-computer interaction (HCI) challenges long-held assumptions about how users approach computers. Given a rudimentary set of cues present in the system’s design, users are said to unconsciously treat computers as genuine interactants—extending rules of politeness, biases and human interactive conventions to machines. Since the results have wide-ranging implications for HCI research methods, interface design and user experiences, researchers are hard-pressed to experimentally verify the paradigm. This paper focuses on the methodology of attributing the necessary social cues to the agent, a core aspect of the experimental design of studies dealing with the media equation. A typology of experimental anthropomorphisms is developed, allowing an assessment of how the differing axiomatic assumptions affect the relevance of the results for an evaluation of the paradigm. The paper concludes with a series of arguments in favour of one particular anthropomorphism type for researching the media equation. [ABSTRACT FROM AUTHOR]

Some of the concerns people have about A1 are: its misuses, effect on unemployment, and its potential for dehurnanising. Contrary to what most people believe and fear, GIcan lead to respect for the enormous power and complexity of the human mind. It is potentially very dangerous for users in the public domain to impute much more inferential power to computer systems, which look common-sensical, than they actually have. No matter how impressive A1 programs may be, we must be aware of their limitations and should not abrogate human responsibility to such programs. [ABSTRACT FROM AUTHOR]