Research Papers

The field of Explainable Artificial Intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional Neural Networks (CNNs) have been successful in making predictions, especially in image classification. However, these famous deep learning models use tens of millions of parameters based on a large number of pre-trained filters which have been repurposed from previous data sets. We propose a novel Interaction-based Convolutional Neural Network (ICNN) that does not make assumptions about the relevance of local information. Instead, we use a model-free Influence Score (I-score) to directly extract the influential information from images to form important variable modules. We demonstrate that the proposed method produces state-of-the-art prediction performance of 99.8% on a real-world data set classifying COVID-19 Chest X-ray images without sacrificing the explanatory power of the model. This proposed design can efficiently screen COVID-19 patients before human diagnosis, and will be the benchmark for addressing future XAI problems in large-scale data sets.

The Coloniality of Data Work in Latin America

Julian Posada

This presentation for the AIES 21 doctoral consortium examines the Latin American crowdsourcing market through a decolonial lens. This research is based on the analysis of the web traffic of ninety-three platforms, interviews with Venezuelan data workers of four platforms, and the analysis of the documentation issued by these organizations. The findings show that (1) centuries-old global divisions of labor persist, in this case, with requesters located in advanced economies and workers in the Global South. (2) That the platforms' configuration of the labor process constrains the agency of these workers when producing annotations. And, (3) that ideologies originating from the Global North serve to legitimize and reinforce this global labor market configuration.

Fair and Responsible AI: A Focus on the Ability to Contest

Henrietta Lyons, Eduardo Velloso, Tim Miller

As the use of artificial intelligence (AI) in high-stakes decision-making increases, the ability to contest such decisions is being recognised in AI ethics guidelines as an important safeguard for individuals. Yet, there is little guidance on how AI systems can be designed to support contestation. In this paper we explain that the design of a contestation process is important due to its impact on perceptions of fairness and satisfaction. We also consider design challenges, including a lack of transparency as well as the numerous design options that decision-making entities will be faced with. We argue for a human-centred approach to designing for contestability to ensure that the needs of decision subjects, and the community, are met.

While we have witnessed a rapid growth of ethics documents meant to guide AI development, the promotion of AI ethics has nonetheless proceeded with little input from AI practitioners themselves. Given the proliferation of AI for Social Good initiatives, this is an emerging gap that needs to be addressed in order to develop more meaningful ethical approaches to AI use and development. This paper offers a methodology, a shared fairness approach, aimed at identifying the needs of AI practitioners when it comes to confronting and resolving ethical challenges and to find a third space where their operational language can be married with that of the more abstract principles that presently remain at the periphery of their work experiences. We offer a grassroots approach to operational ethics based on dialog and mutualised responsibility. This methodology is centred around conversations intended to elicit practitioners perceived ethical attribution and distribution over key value laden operational decisions, to identify when these decisions arise and what ethical challenges they confront, and to engage in a language of ethics and responsibility which enables practitioners to internalise ethical responsibility. The methodology bridges responsibility imbalances that rest in structural decision making power and elite technical knowledge, by commencing with personal, facilitated conversations, returning the ethical discourse to those meant to give it meaning at the sharp end of the ecosystem. Our primary contribution is to add to the recent literature seeking to bring AI practitioners' experiences to the fore by offering a methodology for understanding how ethics manifests as a relational and interdependent sociotechnical practice in their work.

With the advent of Industry 4.0, Data Science and Explainable Artificial Intelligence (XAI) has received considerable intrest in recent literature. However, the entry threshold into XAI, in terms of computer coding and the requisite mathematical apparatus, is really high. For fault diagnosis of steel plates, this work reports on a methodology of incorporating XAI based insights into the Data Science process of development of high precision classifier. Using Synthetic Minority Oversampling Technique (SMOTE) and notion of medoids, insights from XAI tools viz. Ceteris Peribus profiles, Partial Dependence and Breakdown profiles have been harvested. Additionally, insights in the form of IF-THEN rules have also been extracted from an optimized Random Forest and Association Rule Mining. Incorporating all the insights into a single ensemble classifier, a 10 fold cross validated performance of 94% has been achieved. In sum total, this work makes three main contributions viz.: methodology based upon utilization of medoids and SMOTE, of gleaning insights and incorporating into model development process. Secondly the insights themselves are contribution, as they benefit the human experts of steel manufacturing industry, and thirdly a high precision fault diagnosis classifier has been developed.

As technology continues to advance, there is increasing concern about individuals being left behind. Many businesses are striving to adopt responsible design practices and avoid any unintended consequences of their products and services, ranging from privacy vulnerabilities to algorithmic bias. We propose a novel approach to fairness and inclusiveness based on experimentation. We use experimentation because we want to assess not only the intrinsic properties of products and algorithms but also their impact on people. We do this by introducing an inequality approach to A/B testing, leveraging the Atkinson index from the economics literature. We show how to perform causal inference over this inequality measure. We also introduce the concept of site-wide inequality impact, which captures the inclusiveness impact of targeting specific subpopulations for experiments, and show how to conduct statistical inference on this impact. We provide real examples from LinkedIn, as well as an open-source, highly scalable implementation of the computation of the Atkinson index and its variance in Spark/Scala. We also provide over a year's worth of learnings -- gathered by deploying our method at scale and analyzing thousands of experiments -- on which areas and which kinds of product innovations seem to inherently foster fairness through inclusiveness.

Iterative and Adaptive Sampling with Spatial Attention for Black-Box Model Explanations

Bhavan Vasu, Chengjiang Long

Deep neural networks have achieved great success in many real-world applications, yet it remains unclear and difficult to explain their decision-making process to an end-user. In this paper, we address the explainable AI problem for deep neural networks with our proposed framework, named IASSA, which generates an importance map indicating how salient each pixel is for the model's prediction with an iterative and adaptive sampling module. We employ an affinity matrix calculated on multi-level deep learning features to explore long-range pixel-to-pixel correlation, which can shift the saliency values guided by our long-range and parameter-free spatial attention. Extensive experiments on the MS-COCO dataset show that our proposed approach matches or exceeds the performance of state-of-the-art black-box explanation methods.

Towards a Unified Evaluation of Explanation Methods without Ground Truth

Hao Zhang, Jiayi Chen, Haotian Xue +1

This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain ground-truth explanations of the neural network. To this end, we design four metrics to evaluate explanation results without ground-truth explanations. Our metrics can be broadly applied to nine benchmark methods of interpreting neural networks, which provides new insights of explanation methods.

A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is becoming important to ensure the secure and safe use of AI techniques. Thus, we, the Special Interest Group on Semantic Web and Ontology of the Japanese Society for AI, organized a challenge calling for techniques that reason and/or estimate which characters are criminals while providing a reasonable explanation based on an open knowledge graph of a well-known Sherlock Holmes mystery story. This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, the techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the second prize went to an approach that used SPARQL and rules; the best resource prize went to a submission that constructed word embedding of characters from all sentences of Sherlock Holmes novels; and the best idea prize went to a discussion multi-agents model. We conclude this paper with the plans and issues for the next challenge in 2019.

Distributional AGI Safety

Nenad Tomašev, Matija Franklin, Julian Jacobs +2

AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithic Artificial General Intelligence (AGI). The alternative AGI emergence hypothesis, where general capability levels are first manifested through coordination in groups of sub-AGI individual agents with complementary skills and affordances, has received far less attention. Here we argue that this patchwork AGI hypothesis needs to be given serious consideration, and should inform the development of corresponding safeguards and mitigations. The rapid deployment of advanced AI agents with tool-use capabilities and the ability to communicate and coordinate makes this an urgent safety consideration. We therefore propose a framework for distributional AGI safety that moves beyond evaluating and aligning individual agents. This framework centres on the design and implementation of virtual agentic sandbox economies (impermeable or semi-permeable), where agent-to-agent transactions are governed by robust market mechanisms, coupled with appropriate auditability, reputation management, and oversight to mitigate collective risks.

Agentic AI Framework for Individuals with Disabilities and Neurodivergence: A Multi-Agent System for Healthy Eating, Daily Routines, and Inclusive Well-Being

Salman Jan, Toqeer Ali Syed, Gohar Ali +3

The paper presents a detailed Agentic Artificial Intelligence (AI) model that would enable people with disabilities and neurodivergence to lead healthier lives and have more regular days. The system will use a multi-layer structure; it will include an Application and Interface Layer, an Agents Layer, and a Data Source Layer to provide adaptive, transparent, and inclusive support. Fundamentally, a hybrid reasoning engine will synchronize four special-purpose agents, which include: a personalized-nutrition-based, called a Meal Planner Agent; an adaptive-scheduling-based, called a Reminder Agent; interactive assistance during grocery shopping and cooking, called a Food Guidance Agent; and a continuous-intake-and-physiological-tracking, called a Monitoring Agent. All the agents interact through a central communicative system called the Blackboard/Event Bus, which allows autonomous interaction and real-time feedback loops with multimedia user interfaces. Privacy-sensitive data sources, including electronic health records (EHRs), nutritional databases, wearable sensors, and smart kitchen Internet of Things, are also included in the framework and placed into a policy-controlled layer, which ensures data safety and compliance with consent. Collaborative care and clinician dashboards allow common supervision, and discussable artificial intelligence (XAI) modules give brief explanations of why a decision was made, making users responsible and reliant. The proposed agentic AI framework is an extension beyond traditional assistive systems since it incorporates inclusiveness, personalization, and accessibility at all levels. It displays the intersection of multi-agent reasoning, multi-modal interfaces, and human-centered design that will enable the development of autonomy, health, and digital equity among people with disabilities and neurodivergence.

This systematic review examines how machine learning (ML) and deep learning (DL) have transformed forecasting, decision-making, and financial modelling, promoting innovation and efficiency in financial systems. Following PRISMA 2020 guidelines, we analyze 22 peer-reviewed and open-access articles (2024 to 2026) indexed in Scopus, applying ML and DL models across credit risk prediction, cryptocurrency, asset pricing, and macroeconomic policy modeling. The most used models include Random Forest, XG-Boost, Support Vector Machine, Long Short-Term Memory (LSTM), Bidirectional LSTM, Convolutional Neural Network (CNN), and hybrid or ensemble approaches combining statistical and AI methods. ML and DL techniques outperform traditional models by capturing nonlinear dependencies and enhancing predictive accuracy, while explainable AI methods (e.g., SHAP and feature importance analysis) improve transparency and interpretability. Emerging trends include cross-domain applications and the integration of responsible AI in finance. Despite notable progress, challenges remain in interpretability, generalizability, and data quality. Overall, this review provides a comprehensive overview of AI-driven computational finance and outlines future research directions.