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Machine learning (ML) has become popular in the industrial sector as it helps to improve operations, increase efficiency, and reduce costs. However, deploying and managing ML models in production environments can be complex. This is where Machine Learning Operations (MLOps) comes in. MLOps aims to facilitate this deployment and management process. One of the MLOps challenges is understanding how ML models reason, which is key to trust and acceptance. Here, explainable AI (XAI) can help. Better error identification and improved model accuracy are only two resulting advantages. An often neglected fact is that deployed models are bypassed when model performance or explanations do not meet user expectations. In this paper, we provide a novel reference architecture to address the challenge of integrating explanations and feedback capabilities into MLOps. Our architecture is implemented in a series of industrial use cases in the project EXPLAIN. The proposed MLOps software architecture has several advantages. It provides an efficient way to manage ML models in production environments. Further, it allows for integrating explanations into the development and deployment processes.

Understanding when and why to apply any given eXplainable Artificial Intelligence (XAI) technique is not a straightforward task. There is no single approach that is best suited for a given context. This paper aims to address the challenge of selecting the most appropriate explainer given the context in which an explanation is required. For AI explainability to be effective, explanations and how they are presented needs to be oriented towards the stakeholder receiving the explanation. If—in general—no single explanation technique surpasses the rest, then reasoning over the available methods is required in order to select one that is context-appropriate. Due to the transparency they afford, we propose employing argumentation techniques to reach an agreement over the most suitable explainers from a given set of possible explainers.

In this paper, we propose a modular reasoning system consisting of a given mental model of the relevant stakeholder, a reasoner component that solves the argumentation problem generated by a multi-explainer component, and an AI model that is to be explained suitably to the stakeholder of interest. By formalizing supporting premises—and inferences—we can map stakeholder characteristics to those of explanation techniques. This allows us to reason over the techniques and prioritise the best one for the given context, while also offering transparency into the selection decision.

This article surveys the Variable Autonomy (VA) robotics literature that considers two contributory elements to Trustworthy AI: transparency and explainability. These elements should play a crucial role when designing and adopting robotic systems, especially in VA where poor or untimely adjustments of the system's level of autonomy can lead to errors, control conflicts, user frustration, and ultimate disuse of the system. Despite this need, transparency and explainability is, to the best of our knowledge, mostly overlooked in VA robotics literature or is not considered explicitly. In this article, we aim to present and examine the most recent contributions to the VA literature concerning transparency and explainability. In addition, we propose a way of thinking about VA by breaking these two concepts down based on: the mission of the human-robot team; who the stakeholder is; what needs to be made transparent or explained; why they need it; and how it can be achieved. Last, we provide insights and propose ways to move VA research forward. Our goal with this article is to raise awareness and inter-community discussions among the Trustworthy AI and the VA robotics communities.

Data and autonomous systems are taking over our lives, from healthcare to smart homes very few aspects of our day to day are not permeated by them. The technological advances enabled by these technologies are limitless. However, with advantages so too come challenges. As these technologies encompass more and more aspects of our lives, we are forgetting the ethical, legal, safety and moral concerns that arise as an outcome of integrating our lives with technology. In this work, we study the lifecycle of artificial intelligence from data gathering to deployment, providing a structured analytical assessment of the potential ethical, safety and legal concerns. The paper then presents the foundations for the first ethical artificial intelligence sustainability statement to guide future development of AI in a safe and sustainable manner.

Several high profile incidents that involve Artificial Intelligence (AI) have captured public attention and increased demand for regulation. Low public trust and attitudes towards AI reinforce the need for concrete policy around its development and use. However, current guidelines and standards rolled out by institutions globally are considered by many as high-level and open to interpretation, making them difficult to put into practice. This paper presents ongoing research in the field of Responsible AI and explores numerous methods of operationalising AI ethics. If AI is to be effectively regulated, it must not be considered as a technology alone—AI is embedded in the fabric of our societies and should thus be treated as a socio-technical system, requiring multi-stakeholder involvement and employment of continuous value-based methods of assessment. When putting guidelines and standards into practice, context is of critical importance. The methods and frameworks presented in this paper emphasise this need and pave the way towards operational AI ethics.

We summarize the AAAI-2023 Workshop on Artificial Intelligence Safety (SafeAI-2023), held at the 37th AAAI Conference on Artificial Intelligence on February 13-14, 2023 in Washington DC, USA.

We summarize the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023)1, held at the 32nd International Joint Conference on Artificial Intelligence (IJCAI-23) on August 21-22, 2023 in Macau, China.

As robots are introduced to various domains and applications, Human-Robot Teaming (HRT) capabilities are essential. Such capabilities involve teaming with humans in/on/out-the-loop at different levels of abstraction, leveraging the complementing capabilities of humans and robots. This requires robotic systems with the ability to dynamically vary their level or degree of autonomy to collaborate with the human(s) efficiently and overcome various challenging circumstances. Variable Autonomy (VA) is an umbrella term encompassing such research, including but not limited to shared control and shared autonomy, mixed-initiative, adjustable autonomy, and sliding autonomy. This workshop is driven by the timely need to bring together VA-related research and practices that are often disconnected across different communities as the field is relatively young. The workshop's goal is to consolidate research in VA. To this end, and given the complexity and span of Human-Robot systems, this workshop will adopt a holistic trans-disciplinary approach aiming to a) identify and classify related common challenges and opportunities; b) identify the disciplines that need to come together to tackle the challenges; c) identify and define common terminology, approaches, methodologies, benchmarks, and metrics; d) define short- and longterm research goals for the community. To achieve these objectives, this workshop aims to bring together industry stakeholders, researchers from fields under the banner of VA, and specialists from other highly related fields such as human factors and psychology. The workshop will consist of a mix of invited talks, contributed papers, and an interactive discussion panel, toward a shared vision for VA.