5,030 research outputs found
Francesco-Sovrano/DiscoLQA: Camera-ready Version
No full text<p>Replication package for the AI & Law paper titled "DiscoLQA: zero-shot discourse-based legal question answering on European Legislation" (https://doi.org/10.1007/s10506-023-09387-2).</p>
Francesco-Sovrano/DoXpy: Camera-ready Version
No full text<p>Replication package for the KNOSYS paper titled "An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability" (https://doi.org/10.1016/j.knosys.2023.110866).</p>
Francesco-Sovrano/Swiss-G2C-User-Guide-Analysis: First release
No full text<p>Code and data for the paper "Beyond the Lab: An In-Depth Analysis of Real-World Practices in Government-to-Citizen Software User Documentation"</p>
Francesco-Sovrano/Automating-Regulatory-Compliance-An-Empirical-Study-on-Ranking-Transparency-of-EU-Online-Platforms: Camera-ready Version
No full text<p>Version of the code used for the paper 'An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms' (https://doi.org/10.48550/arXiv.2312.14794), accepted for publication at ICSE-SEIS 2024 (https://conf.researchr.org/track/icse-2024/icse-2024-software-engineering-in-society#event-overview).</p>
Combining Experience Replay with Exploration by Random Network Distillation
Full text linkOur work is a simple extension of the paper "Exploration by Random Network Distillation"[1]. More in detail, we show how to efficiently combine Intrinsic Rewards with Experience Replay in order to achieve more efficient and robust exploration (with respect to PPO/RND) and consequently better results in terms of agent performances and sample efficiency. We are able to do it by using a new technique named Prioritized Oversampled Experience Replay (POER), that has been built upon the definition of what is the important experience useful to replay. Finally, we evaluate our technique on the famous Atari game Montezuma's Revenge and some other hard exploration Atari games
How to Quantify the Degree of Explainability: Experiments and Practical Implications
Full text linkExplainable AI was born as a pathway to allow humans to explore and understand the inner working of complex systems. Though, establishing what is an explanation and objectively evaluating explainability, are not trivial tasks. With this paper, we present a new model-agnostic metric to measure the Degree of Explainability of (correct) information in an objective way, exploiting a specific theoretical model from Ordinary Language Philosophy called the Achinstein’s Theory of Explanations, implemented with an algorithm relying on deep language models for knowledge graph extraction and information retrieval. In order to understand whether this metric is actually behaving as explainability is expected to, we have devised an experiment on two realistic Explainable AI-based systems for healthcare and finance, using famous AI technology including Artificial Neural Networks and TreeSHAP. The results we obtained suggest that our proposed metric for measuring the Degree of Explainability is robust on several scenario
From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein’s Theory of Explanation
Full text linkGenerating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces
Full text linkWe propose a new method for generating explanations with Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. With this work, we aim to prove that the philosophical theory of explanations presented by Achinstein can be actually adapted for being implemented into a concrete software application, as an interactive and illocutionary process of answering questions. Specifically, our contribution is an approach to frame illocution in a computer-friendly way, to achieve user-centrality with statistical question answering. Indeed, we frame the illocution of an explanatory process as that mechanism responsible for anticipating the needs of the explainee in the form of unposed, implicit, archetypal questions, hence improving the user-centrality of the underlying explanatory process. Therefore, we hypothesise that if an explanatory process is an illocutionary act of providing content-giving answers to questions, and illocution is as we defined it, the more explicit and implicit questions can be answered by an explanatory tool, the more usable (as per ISO 9241-210) its explanations. We tested our hypothesis with a user-study involving more than 60 participants, on two XAI-based systems, one for credit approval (finance) and one for heart disease prediction (healthcare). The results showed that increasing the illocutionary power of an explanatory tool can produce statistically significant improvements (hence with a P value lower than .05) on effectiveness. This, combined with a visible alignment between the increments in effectiveness and satisfaction, suggests that our understanding of illocution can be correct, giving evidence in favour of our theory
Foreseeing the Impact of the Proposed AI Act on the Sustainability and Safety of Critical Infrastructures
Full text linkThe AI Act has been recently proposed by the European Commission to regulate the use of AI in the EU, especially on high-risk applications, i.e. systems intended to be used as safety components in the management and operation of road traffic and the supply of water, gas, heating and electricity. On the other hand, IEC 61508, one of the most adopted international standards for safety-critical electronic components, seem to mostly forbid the use of AI in such systems. Given this conflict between IEC 61508 and the proposed AI Act, also stressed by the fact that IEC 61508 is not an harmonised European standard, with the present paper we study and analyse what is going to happen to industry after the entry into force of the AI Act. In particular, we focus on how the proposed AI Act might positively impact on the sustainability of critical infrastructures by allowing the use of AI on an industry where it was previously forbidden. To do so, we provide several examples of AI-based solutions falling under the umbrella of IEC 61508 that might have a positive impact on sustainability in alignment with the current long-term goals of the EU and the Sustainable Development Goals of the United Nations, i.e., affordable and clean energy, sustainable cities and communities
Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data
Full text linkIn this paper we introduce a new class of software tools engaged in delivering successful explanations of complex processes on top of basic Explainable AI (XAI) software systems. These tools, that we call cumulatively Explanatory AI (YAI) systems, enhance the quality of the basic output of a XAI by adopting a user-centred approach to explanation that can cater to the individual needs of the explainees with measurable improvements in usability. Our approach is based on Achinstein’s theory of explanations, where explaining is an illocutionary (i.e., broad yet pertinent and deliberate) act of pragmatically answering a question. Accordingly, user-centrality enters in the equation by considering that the overall amount of information generated by answering all questions can rapidly become overwhelming and that individual users may perceive the need to explore just a few of them. In this paper, we give the theoretical foundations of YAI, formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. To this end, we frame the explanatory space as an hypergraph of knowledge and we identify a set of heuristics and properties that can help approximating a decomposition of it into a tree-like representation for efficient and user-centred explanation retrieval. Finally, we provide some old and new empirical results to support our theory, showing that explanations are more than textual or visual presentations of the sole information provided by a XAI
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