Mateus de Souza Monteiro

Juliana Jansen Ferreira, Mateus Monteiro

Artificial Intelligence is becoming part of any technology we use nowadays. If the AI informs people's decisions, the explanation about AI's outcomes, results, and behavior becomes a necessary capability. However, the discussion of XAI features with various stakeholders is not a trivial task. Most of the available frameworks and methods for XAI focus on data scientists and ML developers as users. Our research is about XAI for end-users of AI systems. We argue that we need to discuss XAI early in the AI-system design process and with all stakeholders. In this work, we aimed at investigating how to operationalize the discussion about XAI scenarios and opportunities among designers and developers of AI and its end-users. We took the Signifying Message as our conceptual tool to structure and discuss XAI scenarios. We experiment with its use for the discussion of a healthcare AI-System.

Juliana Jansen Ferreira, Mateus Monteiro

The explanation dimension of Artificial Intelligence (AI) based system has been a hot topic for the past years. Different communities have raised concerns about the increasing presence of AI in people's everyday tasks and how it can affect people's lives. There is a lot of research addressing the interpretability and transparency concepts of explainable AI (XAI), which are usually related to algorithms and Machine Learning (ML) models. But in decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system. Decision-makers usually need to justify their decision to others in different domains. If that decision is somehow based on or influenced by an AI-system outcome, the explanation about how the AI reached that result is key to building trust between AI and humans in decision-making scenarios. In this position paper, we discuss the role of XAI in decision-making scenarios, our vision of Decision-Making with AI-system in the loop, and explore one case from the literature about how XAI can impact people justifying their decisions, considering the importance of building the human-AI relationship for those scenarios.

Juliana Jansen Ferreira, Mateus de Souza Monteiro

As Artificial Intelligence (AI) technology gets more intertwined with every system, people are using AI to make decisions on their everyday activities. In simple contexts, such as Netflix recommendations, or in more complex context like in judicial scenarios, AI is part of people's decisions. People make decisions and usually, they need to explain their decision to others or in some matter. It is particularly critical in contexts where human expertise is central to decision-making. In order to explain their decisions with AI support, people need to understand how AI is part of that decision. When considering the aspect of fairness, the role that AI has on a decision-making process becomes even more sensitive since it affects the fairness and the responsibility of those people making the ultimate decision. We have been exploring an evidence-based explanation design approach to 'tell the story of a decision'. In this position paper, we discuss our approach for AI systems using fairness sensitive cases in the literature.

Juliana Jansen Ferreira, Mateus de Souza Monteiro

The application of Artificial Intelligence (AI) tools in different domains are becoming mandatory for all companies wishing to excel in their industries. One major challenge for a successful application of AI is to combine the machine learning (ML) expertise with the domain knowledge to have the best results applying AI tools. Domain specialists have an understanding of the data and how it can impact their decisions. ML experts have the ability to use AI-based tools dealing with large amounts of data and generating insights for domain experts. But without a deep understanding of the data, ML experts are not able to tune their models to get optimal results for a specific domain. Therefore, domain experts are key users for ML tools and the explainability of those AI tools become an essential feature in that context. There are a lot of efforts to research AI explainability for different contexts, users and goals. In this position paper, we discuss interesting findings about how ML experts can express concerns about AI explainability while defining features of an ML tool to be developed for a specific domain. We analyze data from two brainstorm sessions done to discuss the functionalities of an ML tool to support geoscientists (domain experts) on analyzing seismic data (domain-specific data) with ML resources.