697 research outputs found
Achieving descriptive accuracy in explanations via argumentation: the case of probabilistic classifiers
The pursuit of trust in and fairness of AI systems in order to enable human-centric goals has been gathering pace of late, often supported by the use of explanations for the outputs of these systems. Several properties of explanations have been highlighted as critical for achieving trustworthy and fair AI systems, but one that has thus far been overlooked is that of descriptive accuracy (DA), i.e., that the explanation contents are in correspondence with the internal working of the explained system. Indeed, the violation of this core property would lead to the paradoxical situation of systems producing explanations which are not suitably related to how the system actually works: clearly this may hinder user trust. Further, if explanations violate DA then they can be deceitful, resulting in an unfair behavior toward the users. Crucial as the DA property appears to be, it has been somehow overlooked in the XAI literature to date. To address this problem, we consider the questions of formalizing DA and of analyzing its satisfaction by explanation methods. We provide formal definitions of naive, structural and dialectical DA, using the family of probabilistic classifiers as the context for our analysis. We evaluate the satisfaction of our given notions of DA by several explanation methods, amounting to two popular feature-attribution methods from the literature, variants thereof and a novel form of explanation that we propose. We conduct experiments with a varied selection of concrete probabilistic classifiers and highlight the importance, with a user study, of our most demanding notion of dialectical DA, which our novel method satisfies by design and others may violate. We thus demonstrate how DA could be a critical component in achieving trustworthy and fair systems, in line with the principles of human-centric AI
Argumentation for machine learning: a survey
Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future
Context-aware feature attribution through argumentation
Feature attribution is a fundamental task in both machine learning and data
analysis, which involves determining the contribution of individual features or
variables to a model's output. This process helps identify the most important
features for predicting an outcome. The history of feature attribution methods
can be traced back to General Additive Models (GAMs), which extend linear
regression models by incorporating non-linear relationships between dependent
and independent variables. In recent years, gradient-based methods and
surrogate models have been applied to unravel complex Artificial Intelligence
(AI) systems, but these methods have limitations. GAMs tend to achieve lower
accuracy, gradient-based methods can be difficult to interpret, and surrogate
models often suffer from stability and fidelity issues. Furthermore, most
existing methods do not consider users' contexts, which can significantly
influence their preferences. To address these limitations and advance the
current state-of-the-art, we define a novel feature attribution framework
called Context-Aware Feature Attribution Through Argumentation (CA-FATA). Our
framework harnesses the power of argumentation by treating each feature as an
argument that can either support, attack or neutralize a prediction.
Additionally, CA-FATA formulates feature attribution as an argumentation
procedure, and each computation has explicit semantics, which makes it
inherently interpretable. CA-FATA also easily integrates side information, such
as users' contexts, resulting in more accurate predictions
Explaining classifiers’ outputs with causal models and argumentation
We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for mod-els’ outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the ex-tracted bipolar AFs may be used as relation-based explanations for the outputs of causal models. We then evaluate our method empirically when the causal models represent (Bayesian and neural network) machine learning models for classification. The results show advantages over a popular approach from the literature, both in highlighting specific relationships between feature and classification variables and in generating counterfactual explanations with respect to a commonly used metric
SpArX: Sparse Argumentative Explanations for Neural Networks
Neural networks (NNs) have various applications in AI, but explaining their
decision process remains challenging. Existing approaches often focus on
explaining how changing individual inputs affects NNs' outputs. However, an
explanation that is consistent with the input-output behaviour of an NN is not
necessarily faithful to the actual mechanics thereof. In this paper, we exploit
relationships between multi-layer perceptrons (MLPs) and quantitative
argumentation frameworks (QAFs) to create argumentative explanations for the
mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining
as much of the original mechanics as possible. It then translates the sparse
MLP into an equivalent QAF to shed light on the underlying decision process of
the MLP, producing global and/or local explanations. We demonstrate
experimentally that SpArX can give more faithful explanations than existing
approaches, while simultaneously providing deeper insights into the actual
reasoning process of MLPs
Argumentation-based recommendations: fantastic explanations and how to find them
A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations
Interactive Explanations by Conflict Resolution via Argumentative Exchanges
As the field of explainable AI (XAI) is maturing, calls for interactive
explanations for (the outputs of) AI models are growing, but the
state-of-the-art predominantly focuses on static explanations. In this paper,
we focus instead on interactive explanations framed as conflict resolution
between agents (i.e. AI models and/or humans) by leveraging on computational
argumentation. Specifically, we define Argumentative eXchanges (AXs) for
dynamically sharing, in multi-agent systems, information harboured in
individual agents' quantitative bipolar argumentation frameworks towards
resolving conflicts amongst the agents. We then deploy AXs in the XAI setting
in which a machine and a human interact about the machine's predictions. We
identify and assess several theoretical properties characterising AXs that are
suitable for XAI. Finally, we instantiate AXs for XAI by defining various agent
behaviours, e.g. capturing counterfactual patterns of reasoning in machines and
highlighting the effects of cognitive biases in humans. We show experimentally
(in a simulated environment) the comparative advantages of these behaviours in
terms of conflict resolution, and show that the strongest argument may not
always be the most effective.Comment: 14 pages, 2 figure
ArguCast: a system for online multi-forecasting with gradual argumentation
Judgmental forecasting is a form of forecasting which employs (human) users to make predictions about specied future events. Judgmental forecasting has been shown to perform better than quantitative methods for forecasting, e.g. when historical data is unavailable or causal reasoning is needed. However, it has a number of limitations, arising from users’ irrationality and cognitive biases. To mitigate against these phenomena, we leverage on computational argumentation, a eld which excels in the representation and resolution of conicting knowledge and human-like reasoning, and propose novel ArguCast frameworks (ACFs) and the novel online system ArguCast, integrating ACFs. ACFs and ArguCast accommodate multi-forecasting, by allowing multiple users to debate on multiple forecasting predictions simultaneously, each potentially admitting multiple outcomes. Finally, we propose a novel notion of user rationality in ACFs based on votes on arguments in ACFs, allowing the ltering out of irrational opinions before obtaining group forecasting predictions by means commonly used in judgmental forecasting
An Argumentative Dialogue System for COVID-19 Vaccine Information
open3noDialogue systems are widely used in AI to support timely and interactive
communication with users. We propose a general-purpose dialogue system
architecture that leverages computational argumentation to perform reasoning
and provide consistent and explainable answers. We illustrate the system using
a COVID-19 vaccine information case study.openFazzinga, Bettina; Galassi, Andrea; Torroni, PaoloFazzinga, Bettina; Galassi, Andrea; Torroni, Paol
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