We also introduced a taxonomy to The increasing use of artificial intelligence (AI) systems in navigate the space of explanation methods, not only the ten high stakes domains has been coupled with an increase in societal in the toolkit but also the broader literature on explainable demands for these systems to provide explanations for AI. The taxonomy was intended to be usable by consumers their outputs. This societal demand has already resulted in with varied backgrounds to choose an appropriate explanation new regulations requiring explanations (Goodman and Flaxman method for their application. AIX360 differs from other 2016; Wachter, Mittelstadt, and Floridi 2017; Selbst open source explainability toolkits (see Arya et al. (2020) and Powles 2017; Pasternak 2019). Explanations can allow for a list) in two main ways: 1) its support for a broad and users to gain insight into the system's decision-making process, diverse spectrum of explainability methods, implemented in which is a key component in calibrating appropriate a common architecture, and 2) its educational material as trust and confidence in AI systems (Doshi-Velez and Kim discussed below.

As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed.

Heterogeneous knowledge naturally arises among different agents in cooperative multiagent reinforcement learning. As such, learning can be greatly improved if agents can effectively pass their knowledge on to other agents. Existing work has demonstrated that peer-to-peer knowledge transfer, a process referred to as action advising, improves team-wide learning. In contrast to previous frameworks that advise at the level of primitive actions, we aim to learn high-level teaching policies that decide when and what high-level action (e.g., sub-goal) to advise a teammate. We introduce a new learning to teach framework, called hierarchical multiagent teaching (HMAT). The proposed framework solves difficulties faced by prior work on multiagent teaching when operating in domains with long horizons, delayed rewards, and continuous states/actions by leveraging temporal abstraction and deep function approximation. Our empirical evaluations show that HMAT accelerates team-wide learning progress in difficult environments that are more complex than those explored in previous work. HMAT also learns teaching policies that can be transferred to different teammates/tasks and can even teach teammates with heterogeneous action spaces.