Interactive machine learning: experimental evidence for the human in the algorithmic loop

Abstract

Recent advances in automatic machine learning (aML) allow solving problems without any human intervention. However, sometimes a human-in-the-loop can be beneficial in solving computationally hard problems. In this paper we provide new experimental insights on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, we used the Ant Colony Optimization (ACO) framework, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX-MIN Ant System (MMAS) in the background. We extended the MMAS-Algorithm in a way, that the human can directly interact and influence the ants. This is done by "traveling" with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant's behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind.

Causability and explainability of artificial intelligence in medicine

“…Ante‐hoc systems are interpretable by design towards glass‐box approaches (Holzinger et al, , ); typical examples include linear regression, decision trees and fuzzy inference systems. The latter have a long tradition and can be designed from expert knowledge or from data and provides—from the viewpoint of Human‐AI interaction—a good framework for the interaction between human expert knowledge and hidden knowledge in the data (Guillaume, ).…”

Verbinden von Natürlicher und Künstlicher Intelligenz: eine experimentelle Testumgebung für Explainable AI (xAI)

“…Die Nachvollziehbarkeit, Interpretation und Verstehbarkeit von Ergebnissen maschineller Lernalgorithmen und Möglichkeiten die Qualität von KI zu evaluieren rücken damit in das Zentrum des Interesses von Wissenschaft und Wirtschaft. Durch die schwächen so-genannter "Black-Box" Algorithmen sowie sozialer und ethischer Verantwortlichkeit ist man, zumindest in Domänen wie der Medizin, wieder stärker motiviert an einer Verbindung von menschlicher Intelligenz und maschineller Intelligenz ("augmenting human intelligence with artificial intelligence") zu arbeiten und die Stärken von KI und menschlicher Intelligenz zu verbinden (Holzinger 2016a;Holzinger et al 2019b).…”

The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure

“…by application of interactive ML (iML) methods [92,93], which also enable to explain why a machine decision has been reached [94,95]. Technically, this will be done by applying algorithms which enable an integration of a human into the algorithmic loop, who can then provide his/her expertise to find the underlying explanatory factors [96].…”