AI Without Math: Making AI and ML Comprehensible

If we want nontechnical stakeholders to respond to artificial intelligence developments in an informed way, we must help them acquire a more-than-superficial understanding of artificial intelligence (AI) and machine learning (ML). Explanations involving formal mathematical notation will not reach most people who need to make informed decisions about AI. We believe it is possible to teach many AI and ML concepts without slipping into mathematical notation

Do you comply with AI? — Personalized explanations of learning algorithms and their impact on employees\u27 compliance behavior

Machine learning algorithms are technological key enablers for artificial intelligence (AI). Due to the inherent complexity, these learning algorithms represent black boxes and are dif icult to comprehend, therefore influencing compliance behavior. Hence, compliance with the recommendations of such artifacts, which can impact employees’ task performance significantly, is still subject to research—and personalization of AI explanations seems to be a promising concept in this regard. In our work, we hypothesize that, based on varying backgrounds like training, domain knowledge and demographic characteristics, individuals have dif erent understandings and hence mental models about the learning algorithm. Personalization of AI explanations, related to the individuals’ mental models, may thus be an instrument to af ect compliance and therefore employee task performance. Our preliminary results already indicate the importance of personalized explanations in industry settings and emphasize the importance of this research endeavor

AI Without Math: Making AI and ML Comprehensible

If we want nontechnical stakeholders to respond to artificial intelligence developments in an informed way, we must help them acquire a more-than-superficial understanding of artificial intelligence (AI) and machine learning (ML). Explanations involving formal mathematical notation will not reach most people who need to make informed decisions about AI. We believe it is possible to teach many AI and ML concepts without slipping into mathematical notation