Characteristics that Influence Perceived Intelligence in AI Design
| Autor: | Karen M. Feigh, Samantha Krening |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Teaching method 05 social sciences 050109 social psychology 02 engineering and technology Human physical appearance computer.software_genre Medical Terminology Intelligent agent Action (philosophy) Human–computer interaction 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences computer Advice (complexity) Natural language Medical Assisting and Transcription |
| Zdroj: | Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 62:1637-1641 |
| ISSN: | 1071-1813 2169-5067 |
| DOI: | 10.1177/1541931218621371 |
| Popis: | A goal of interactive machine learning (IML) is to create robots or intelligent agents that can be easily taught how to perform tasks by individuals with no specialized training. To achieve that goal, researchers and designers must understand how certain design decisions impact the human’s experience of teaching the agent, such as influencing the agent’s perceived intelligence. We posit that the type of feedback a robot can learn from affects the perceived intelligence of the robot, similar to its physical appearance. This study investigated two methods of natural language instruction: critique and action advice. We conducted a human-in-the-loop experiment in which people trained two agents with different teaching methods but, unknown to each participant, the same underlying machine learning algorithm. The results show an agent that learns from binary good/bad critique is perceived as less intelligent than an agent that can learn from action instructions, even if the underlying machine learning agent is the same. In addition to the complexity of the input, other design characteristics we found that influence the agent’s perceived intelligence are: compliance, responsiveness, effort, transparency, and robustness. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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