6,230 research outputs found

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    E-learning in the Cloud Computing Environment: Features, Architecture, Challenges and Solutions

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    The need to constantly and consistently improve the quality and quantity of the educational system is essential. E-learning has emerged from the rapid cycle of change and the expansion of new technologies. Advances in information technology have increased network bandwidth, data access speed, and reduced data storage costs. In recent years, the implementation of cloud computing in educational settings has garnered the interest of major companies, leading to substantial investments in this area. Cloud computing improves engineering education by providing an environment that can be accessed from anywhere and allowing access to educational resources on demand. Cloud computing is a term used to describe the provision of hosting services on the Internet. It is predicted to be the next generation of information technology architecture and offers great potential to enhance productivity and reduce costs. Cloud service providers offer their processing and memory resources to users. By paying for the use of these resources, users can access them for their calculations and processing anytime and anywhere. Cloud computing provides the ability to increase productivity, save information technology resources, and enhance computing power, converting processing power into a tool with constant access capabilities. The use of cloud computing in a system that supports remote education has its own set of characteristics and requires a unique strategy. Students can access a wide variety of instructional engineering materials at any time and from any location, thanks to cloud computing. Additionally, they can share their materials with other community members. The use of cloud computing in e-learning offers several advantages, such as unlimited computing resources, high scalability, and reduced costs associated with e-learning. An improvement in the quality of teaching and learning is achieved through the use of flexible cloud computing, which offers a variety of resources for educators and students. In light of this, the current research presents cloud computing technology as a suitable and superior option for e-learning systems

    Understanding feeling-of-knowing in information search : an EEG study

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    The realisation and the variability of information needs (IN) with respect to a searcher’s gap in knowledge is driven by the perceived Anomalous State of Knowledge (ASK). The concept of Feeling-of-Knowing (FOK), as the introspective feeling of knowledge awareness, shares the characteristics of an ASK state. From an IR perspective, FOK as a premise to trigger IN is unexplored. Motivated by the neuroimaging studies in IR, we investigate the neurophysiological drivers associated with FOK, to provide evidence validating FOK as a distinctive state in IN realisation. We employ Electroencephalography to capture the brain activity of 24 healthy participants performing a textual Question Answering IR scenario. We analyse the evoked neural patterns corresponding to three states of knowledge: i.e., (1)“I know”, (2)“FOK”, (3)“I do not know”. Our findings show the distinct neurophysiological signatures (N1, P2, N400, P6) in response to information segments processed in the context of our three levels. They further reveal that the brain manifestation associated with “FOK” does not significantly differ from the ones associated with “I do not know”, indicating their association with recognition of a gap in knowledge and as such could further inform the IN formation on different levels of knowing

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Learning recommender systems from biased user interactions

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    Recommender systems have been widely deployed to help users quickly find what they need from a collection of items. Predominant recommendation methods rely on supervised learning models to predict user ratings on items or the probabilities of users interacting with items. In addition, reinforcement learning models are crucial in improving long-term user engagement within recommender systems. In practice, both of these recommendation methods are commonly trained on logged user interactions and, therefore, subject to bias present in logged user interactions. This thesis concerns complex forms of bias in real-world user behaviors and aims to mitigate the effect of bias on reinforcement learning-based recommendation methods. The first part of the thesis consists of two research chapters, each dedicated to tackling a specific form of bias: dynamic selection bias and multifactorial bias. To mitigate the effect of dynamic selection bias and multifactorial bias, we propose a bias propensity estimation method for each. By incorporating the results from the bias propensity estimation methods, the widely used inverse propensity scoring-based debiasing method can be extended to correct for the corresponding bias. The second part of the thesis consists of two chapters that concern the effect of bias on reinforcement learning-based recommendation methods. Its first chapter focuses on mitigating the effect of bias on simulators, which enables the learning and evaluation of reinforcement learning-based recommendation methods. Its second chapter further explores different state encoders for reinforcement learning-based recommendation methods when learning and evaluating with the proposed debiased simulator

    An In-Depth Review of ChatGPT’s Pros and Cons for Learning and Teaching in Education

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    As technology progresses, there has been an increasing interest in using Chatbot GPT (Generative Pre-trained Transformer) in education. Chatbot GPT, or ChatGPT, gained one million users within the first week of launching in November 2022 and had amassed over 100 million active users by February 2023. This type of artificial intelligence uses natural language processing to convert it into a user. This paper presents a comprehensive analysis and review of 34 articles published on ChatGPT and its potential impact on education by utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. This review analyzed various studies and articles to examine the strengths and limitations of GPT language models in education from 2018 to the present. The advantages of ChatGPT include its capacity to provide personalized and adaptive learning, instant feedback, and improved accessibility. However, there are potential drawbacks, such as the lack of emotional intelligence, the risk of overreliance on technology, and privacy concerns. This review suggests that ChatGPT has significant promise for education yet reinforces the necessity for further research and careful consideration of possible risks and limitations. Specifically, it pointed out potential invisible manipulations by instructing ChatGPT to answer educationrelated topics. The paper concludes by discussing the implications of ChatGPT for the future of education and emphasizing the need for further research in this field

    Fairness-aware Machine Learning in Educational Data Mining

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    Fairness is an essential requirement of every educational system, which is reflected in a variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM. In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions: We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a direct or indirect relationship with the protected attributes w.r.t. prediction outcomes. We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects. We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering. We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students' preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding the protected attribute. We propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal 0-1 knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. In short, the findings described in this thesis demonstrate the importance of fairness-aware ML in educational settings. We show that bias-aware data analysis, fairness measures, and fairness-aware ML models are essential aspects to ensure fairness in EDM and the educational environment.Ministry of Science and Culture of Lower Saxony/LernMINT/51410078/E
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