Bots are dead, long live AI (Part 3/5)
Authenticity, the gateway drug for artificial intelligence
Weizbaums ELIZA opened up the discussion about authentic agents in the 1960s. Thereby, starting the crisis of authenticity of intelligent systems. Hence, ELIZA was nothing more than an interactive diary. Through this intelligent agent the term Darwinian buttons was coined, signaling to be an entity able to build relationships by showing interest into others. Since agents fake the need of a sentience creature that needs nurturing or parenting or caring about its presence. Darwinian buttons include tracking an individuals movement, making eye contact, or gesturing kindly in acknowledge of another persons presence. Sherry Turkle emphasizes that since the advent of computers people search for criteria of authentic relationships, whereas for some people, computer companionship seems normal. Even more, Turkle argues that as robots become a part of everyday life, it is important that these differences are clearly articulated and discussed. For this part, authenticity has mostly been neglected by empirical psychological studies, and therefore not enough measures of authenticity have been developed. However, the research body agrees that authenticity, coupled with integrity and honesty, build the most basic human strengths.
Authenticity reveals the honesty and sincerity of a person with him or herself as well as with others. Authenticity is about establishing cooperation, preventing manipulation and being true to oneself and others. Moreover, having respect, staying true to ones values and playing fair makes a person authentic. Authenticity can even be described as being a Mensch. A Mensch is an upright, admirable, noble person according to the Jewish lexicon. As an initial description, authenticity deriving from the Latin authenticus and the Greek word authentikos means to be trust-worthy, authoritative, or acceptable.
Bridging the gap from artificial intelligence to authenticity, authenticity is gaining in importance for consumers around the world. Phrased by Pine and Gilmore authenticity is becoming the new consumer sensibility. This makes authenticity an important topic in marketing literature as well. Authenticity has been researched in the area of tourism, leadership or learning, where several frameworks from different viewpoints have been developed. However, a research gap is still present for areas such as software development and in particular the development of autonomous algorithms interacting with humans. Therefore, Johnson and Noorman claim that responsibility issues should be addressed when artificial agent technologies are in the early stages of development. This emphasizes the responsibility to identify characteristics that make an intelligent messaging agent authentic.
Retrospectively, people drew hard lines about machines being cognitive. Today, computer culture allows affective computing and sociable machines as well as flesh and machine hybrids. However, computers still should not make humans uncomfortable by coming too close. In order to become authentic, it is necessary to anthropomorphize output as well as the enterprise itself. The gain for business is the perception of real brand or company identity. Since, conversations within every-day life are the most powerful form of consumer seduction, inauthentic conversation creates a perception of phoniness. Gundlach and Neville, Grayson and Martinec as well as Beverland, Lindgreen and Vink developed frameworks for the positive perception of authenticity. However, authenticity is deemed as a social concept. In order to better understand authenticity and the perception upon it within consumers minds, research focuses on consumer-based definitions.
Therefore, agents need to be equipped with internal representations of the available social information. This can be achieved through social networks enhanced by individual beliefs, goals and intentions of the intelligent system. Agent-based social modelling has to look at the specification of the model, have basic assumptions, interrelations or rules, as well as create models and research designs for tests, simulations or experiments. Creating social information networks can be manifold such as structural equations, cellular automata, Bayesian networks and hidden Markov models, system dynamics, and agent-based approaches. However, social computing will allow for the building of intelligent agents capable of acting social, rather than imitating humans.
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This post is the third in a series of five extracts from my Master’s thesis revolving around socially intelligent artificial intelligence. Thanks for reading! :) If you enjoyed this article, hit that ❤ button below. Would mean a lot to me and it helps other people see the story.
