Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control; interactive machine learning, in which there is a closer interaction between users and learning systems; and machine teaching, where human domain experts have control over the learning process. Aside from control, humans can also be involved in the learning process in other ways. In curriculum learning human domain experts try to impose some structure on the examples presented to improve the learning; in explainable AI the focus is on the ability of the model to explain to humans why a given solution was chosen. This collaboration between AI models and humans should not be limited only to the learning process; if we go further, we can see other terms that arise such as Usable and Useful AI. In this paper we review the state of the art of the techniques involved in the new forms of relationship between humans and ML algorithms. Our contribution is not merely listing the different approaches, but to provide definitions clarifying confusing, varied and sometimes contradictory terms; to elucidate and determine the boundaries between the different methods; and to correlate all the techniques searching for the connections and influences between them.

Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges.

At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance. Particularly, Machine Learning (ML) systems could greatly benefit from their expertise or knowledge. Thus, there is an increasing interest around how humans interact with those systems to obtain the best performance for both the AI system and the humans involved. Several approaches have been studied and proposed in the literature that can be gathered under the umbrella term of Human-in-the-Loop Machine Learning. The application of those techniques to the health informatics environment could provide a great value on prognosis and diagnosis tasks contributing to develop a better health service for Cancer related diseases.

This paper presents a broad study on the evaluation of explanatory capabilities of machine learning models, with a focus on Decision Trees, Random Forest, and XGBoost using a pancreatic cancer data set. We use Human-in-the-Loop-related techniques and medical guidelines as a source of domain knowledge to establish the importance of the different features that are relevant to select a pancreatic cancer treatment. These features are not only used as a dimensionality reduction approach for the machine learning models but also as a way to evaluate the explainability capabilities of the different models using agnostic and non-agnostic explainability techniques. To facilitate the interpretation of explanatory results, we propose the use of similarity measures such as the Weighted Jaccard Similarity coefficient. The goal is to select not only the best performing model but also the one that can best explain its conclusions and better aligns with human domain knowledge.

The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models on a diagnosis scenario with the goal to decide whether a patient should be prescribed with a chemotherapy treatment. To validate the diagnosis results we explore different explainability approaches: Decision Tree, Random Forest, and model agnostic ad-hoc models, and compare them against a standard Pancreatic Cancer treatment set of rules. The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models. To validate the diagnostic results we explore different explainability approaches: Decision Tree based approach, Random Forest based approach, and different model agnostic ad-hoc approaches, and we compare them against a standard Pancreatic Cancer treatment set of rules

Congrats for the hard work! Please, find my comments below: Introduction The reference Billman et al. 2006 seems to be far from the current technological situation, and thus, I am not sure it could be grouped in the same category of the others (Daug...