Greedy PIG: Adaptive Integrated Gradients

Deep learning has become the standard approach for most machine learning tasks. While its impact is undeniable, interpreting the predictions of deep learning models from a human perspective remains a challenge. In contrast to model training, model interpretability is harder to quantify and pose as an explicit optimization problem. Inspired by the AUC softmax information curve (AUC SIC) metric for evaluating feature attribution methods, we propose a unified discrete optimization framework for feature attribution and feature selection based on subset selection. This leads to a natural adaptive generalization of the path integrated gradients (PIG) method for feature attribution, which we call Greedy PIG. We demonstrate the success of Greedy PIG on a wide variety of tasks, including image feature attribution, graph compression/explanation, and post-hoc feature selection on tabular data. Our results show that introducing adaptivity is a powerful and versatile method for making attribution methods more powerful

Factors Influencing the Effectiveness of Managing Human–Robot Teams

Certain factors can influence the capabilities of a robot–human team by affecting their social and behavioral dynamics in a work environment. But these factors were not known due to the progressive nature of human–robot partnerships and a lack of peer-reviewed literature on the topic. This e-Delphi study aimed to identify and understand these unknown influential factors based on the participants’ insights. The overarching research question asked about the need to determine factors that might influence the effectiveness of managing human-robot teams. The basis for the conceptual framework for this study was the theory of communication used in organizational management. Twelve participants with backgrounds in management, software engineering, robotics, or a combination answered open-ended and closed-ended questions in three data rounds through SurveyMonkey. Excel and Python were used to analyze the data. Eight factors, and 10 subfactors emerged from the analysis and showed a relationship to the influential dynamics in communication, trust, sociostructural entanglement, and decision-making, which are integral to organizational and human–robot workforce management. Human–robot workforce management is a new paradigm in organization management. The results of this study may engender positive social change by augmenting human capabilities, such as assisting vulnerable or challenged individuals who require continuous assistance, performing activities detrimental to human life, and performing lifesaving measures, such as search and rescue