While, for the time being, there are no robots on any couches talking to people about their traumata, concern arises about the well-being of their human interaction partners, as people and robots experience artificial intelligence differently. This perception differential raises a question how it should affect robot design to increase acceptance by humans amid fears of domination by technology. As these things go, it is always helpful to start with the argumentum e contrario and figure out how not to use robots so that they are not perceived as a threat but may be safely experienced as an enrichment. This presupposes research on optimal interfaces between man and machine to facilitate their coexistence, starting with classical usability research to make user interfaces intuitive, but quickly progressing to social fears of certain uses of robots, the role of a user’s personality, and a subjective sense of security in communication that involves, say, autonomous vehicles and pedestrians.
In 1970, Japanese roboticist Masahiro Mori coined the term “uncanny valley” for a graph that shows the nexus between a robot’s human-like qualities and the emotional reaction to it by humans. While a robot may have head and eyes, it remains essential for acceptance that it be clearly distinguishable as a machine. Once silicone skin and artificial hair further obfuscate distinction, robots are perceived almost universally as sinister. New studies indicate that the same might be true of disembodied AI, suggesting an “uncanny valley of the mind” whenever online chatbots adopt human-like behavior by simulating emotionality or by making independent decisions.
Over time, people do get used to humanoid robots through habituation effects. Exploring those effects would require long-term studies which have clearly not had occasion to be carried out. But the fundamental question arises whether humans generally want to work and live together with humanoid robots. Thus far, polls show attitudes of major rejection the more the impression is solidified that robot and people become gradually less distinguishable. This apparent similarity tends to cause heavy and permanent insecurities on the part of humans as it confuses their expectations. While the perceived risk is still far from a contemporary reality, the problem is far more immediate for software bots that already communicate with people online and simulate interaction. Research areas increasingly identified as android science and affective computing expect technologies that surround us to become emotion-aware over the course of the next five years, creating a vision of artificial emotional intelligence.
Current autonomous machines have not to date assumed a human form. Thinking of the example of driverless vehicles, fear of losing control to networked technology could also take hold of pedestrians, so it may be helpful for an autonomous machine to communicate with its surroundings and to make highly transparent what steps the machine will take next.
Does it increase the safety of people crossing the trajectory of an autonomous vehicle to be told by the robot that it saw them? Traditionally, we make eye contact with a human driver in such situations. Exploring if and how this can be translated into technology may hold promise for increasing not only safety but also efficiency of human-robot interactions. In one example, light signals increased predictability of a vehicle’s actions. Pedestrians became significantly faster and moved more confidently. Most effective were visual signals perceived only peripherally, but still increasing a human’s sense of security, for example by mounting on the car’s grille lights targeting signals in the direction of the pedestrian. Possibilities to convey signals will evolve, but they also involve a risk of sensory or information overload. Perhaps in a decade or two, car bodies will serve as large displays, but this in turn increases the complexity of visual information processing required of pedestrians.
Predictability is equally important in industrial robotics. Here, too, machines need to be able to signal to humans who need to anticipate, for example, as early as possible the spot and access path where a robot may wish to reach. Machine movement must not simply take the shortest distance from A to B if curved movements could indicate intentions early. What may be inefficient from a technical viewpoint may considerably increase predictability and workplace safety, improving legibility of robot movements and accelerating performance of overall team of man and machine.
As for the cultural acceptance of robotics, research data is not clear. Some studies suggest that people in Japan are more open to autonomously acting machines – in part also to ease the country’s labor shortage – while others see no difference or even show the opposite result. Explanations for favorable findings may be routed in the religious tradition of animism and Shinto with their concepts of a soulful object, while no such thought exists in the Judeo-Christian tradition. Media socialization is also frequently mentioned to explain acceptance of autonomous machines. In popular anime and manga, robots are cooperation partners or even act as saviors of humanity. This is in stark contrast to the Western vision of a "Terminator" that puts an end to humanity.
Most developers of robotics are men. They also impose their male perspective on technology. In fact, the entire tech industry is dominated by younger white males. It is difficult to investigate which distortions or biases this creates. As an example, a video circulated widely on social media showed a black man who could not seem to manage to get soap out of a soap dispenser. Only when he held a white sheet of paper under the dispenser, the soap came out. This illustrates what can go wrong if development teams are not diverse enough. As another example, service robots are typically designed with traditional female stereotypes in mind, including a cleaning robot with a female figure and an apron, at least in part realizing a gynoid fantasy. There is ample evidence that robots adopt prejudices and stereotypes from their creators since, at the present stage, AI is above all machine learning. Programs process man-made content data and draw their conclusions by pattern intelligence. Therefore, it is not reasonable to expect value-neutral results, even if this is intuitively expected or presumed. Studies show that machine learning systems adopt gender stereotypes and show females as close to their families while males are viewed as more career-oriented.
Popular culture has further shaped technological spins and biases through sci-fi books and movies that cause people to overestimate the state of technology. Many promptly associate with the term “robot” the anthropomorphous androids they know from Star Trek or I, Robot. Developers who currently work hard on resolving challenges of robot stability on two legs must be amused by such perceptions. It might not hurt the cause of realism to show actual contemporary robots in action more frequently – say, their applications in autonomous transport or cleaning systems. Such reality check promises to be one of the safest methods to banish premature fear of the man-machine’s interfacing coexistence.
 Polish scientist Maciej Koszowski produced a remarkable body of logico-legal algorithmic and interpretive research on aspects of analogy. See Maciej Koszowski, Multiple Function of Analogical Reasoning in Science and Everyday Life, 197 Polish Soc. Rev., no. 1, 2017, at 3; Maciej Koszowski, The Scope of Application of Analogical Reasoning in Statutory Law, 7 Am. Int’l J. Contemp. Res., no. 1, March 2017, at 16; Maciej Koszowski, Why Is Analogy in Empirical Science and Everyday Life Different from Analogy in Law? 25 Studia Iuridica Lublinensia, no. 2, 2016, at 127; Maciej Koszowski, Perelman and Olbrechts-Tyteca’s Account of Analogy Applied to Law: The Proportional Model of Analogical Legal Reasoning, 13 Archiwum Filozofii Prawa i Filozofii Społecznej, no. 2, 2016, at 5; Maciej Koszowski, The Scope of Application of Analogical Reasoning in Precedential Law, 37 Liverpool L. Rev., no. 1-2, 2016, at 19. See also Richard A. Posner, Legal Reason: The Use of Analogy in Legal Argument, 91 Cornell L. Rev. 761 (2006) and Chaim Perelman & Lucie Olbrechts-Tyteca, The New Rhetoric: A Treatise on Argumentation (1971).