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  1. On Philomatics and Psychomatics for Combining Philosophy and Psychology with Mathematics.Benyamin Ghojogh & Morteza Babaie - manuscript
    We propose the concepts of philomatics and psychomatics as hybrid combinations of philosophy and psychology with mathematics. We explain four motivations for this combination which are fulfilling the desire of analytical philosophy, proposing science of philosophy, justifying mathematical algorithms by philosophy, and abstraction in both philosophy and mathematics. We enumerate various examples for philomatics and psychomatics, some of which are explained in more depth. The first example is the analysis of relation between the context principle, semantic holism, and the usage (...)
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  2. Can AI Abstract the Architecture of Mathematics?Posina Rayudu - manuscript
    The irrational exuberance associated with contemporary artificial intelligence (AI) reminds me of Charles Dickens: "it was the age of foolishness, it was the epoch of belief" (cf. Nature Editorial, 2016; to get a feel for the vanity fair that is AI, see Mitchell and Krakauer, 2023; Stilgoe, 2023). It is particularly distressing—feels like yet another rerun of Seinfeld, which is all about nothing (pun intended); we have seen it in the 60s and again in the 90s. AI might have had (...)
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  3. A statistical learning approach to a problem of induction.Kino Zhao - manuscript
    At its strongest, Hume's problem of induction denies the existence of any well justified assumptionless inductive inference rule. At the weakest, it challenges our ability to articulate and apply good inductive inference rules. This paper examines an analysis that is closer to the latter camp. It reviews one answer to this problem drawn from the VC theorem in statistical learning theory and argues for its inadequacy. In particular, I show that it cannot be computed, in general, whether we are in (...)
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  4. A Falsificationist Account of Artificial Neural Networks.Oliver Buchholz & Eric Raidl - forthcoming - The British Journal for the Philosophy of Science.
    Machine learning operates at the intersection of statistics and computer science. This raises the question as to its underlying methodology. While much emphasis has been put on the close link between the process of learning from data and induction, the falsificationist component of machine learning has received minor attention. In this paper, we argue that the idea of falsification is central to the methodology of machine learning. It is commonly thought that machine learning algorithms infer general prediction rules from past (...)
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  5. Language Agents Reduce the Risk of Existential Catastrophe.Simon Goldstein & Cameron Domenico Kirk-Giannini - forthcoming - AI and Society:1-11.
    Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they function as though they have desires and beliefs, and then make (...)
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  6. Real Sparks of Artificial Intelligence and the Importance of Inner Interpretability.Alex Grzankowski - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft. Although there is a great deal of value in their work, I will argue that, for familiar philosophical reasons, their methodology, ‘Black-box Interpretability’ is wrongheaded. But there is a better way. There is an exciting and emerging discipline of ‘Inner Interpretability’ (also sometimes called ‘White-box Interpretability’) that aims to uncover the internal activations and weights of models in order (...)
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  7. Operationalising Representation in Natural Language Processing.Jacqueline Harding - forthcoming - British Journal for the Philosophy of Science.
    Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...)
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  8. Does Artificial Intelligence Use Private Language?Ryan Miller - forthcoming - In Proceedings of the International Ludwig Wittgenstein Symposium 2021. Vienna: Lit Verlag.
    Wittgenstein’s Private Language Argument holds that language requires rule-following, rule following requires the possibility of error, error is precluded in pure introspection, and inner mental life is known only by pure introspection, thus language cannot exist entirely within inner mental life. Fodor defends his Language of Thought program against the Private Language Argument with a dilemma: either privacy is so narrow that internal mental life can be known outside of introspection, or so broad that computer language serves as a counter-example. (...)
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  9. Making decisions with evidential probability and objective Bayesian calibration inductive logics.Mantas Radzvilas, William Peden & Francesco De Pretis - forthcoming - International Journal of Approximate Reasoning:1-37.
    Calibration inductive logics are based on accepting estimates of relative frequencies, which are used to generate imprecise probabilities. In turn, these imprecise probabilities are intended to guide beliefs and decisions — a process called “calibration”. Two prominent examples are Henry E. Kyburg's system of Evidential Probability and Jon Williamson's version of Objective Bayesianism. There are many unexplored questions about these logics. How well do they perform in the short-run? Under what circumstances do they do better or worse? What is their (...)
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  10. Predicting and Preferring.Nathaniel Sharadin - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.
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  11. Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning.Adewale Sikiru, Tosin Ige & Bolanle Matti Hafiz - forthcoming - Proceedings of the IEEE:1-6.
    This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over (...)
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  12. Human Induction in Machine Learning: A Survey of the Nexus.Petr Spelda & Vit Stritecky - forthcoming - ACM Computing Surveys.
    As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet (...)
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  13. A note on the learning-theoretic characterizations of randomness and convergence.Tomasz Steifer - forthcoming - Review of Symbolic Logic:1-15.
    Recently, a connection has been established between two branches of computability theory, namely between algorithmic randomness and algorithmic learning theory. Learning-theoretical characterizations of several notions of randomness were discovered. We study such characterizations based on the asymptotic density of positive answers. In particular, this note provides a new learning-theoretic definition of weak 2-randomness, solving the problem posed by (Zaffora Blando, Rev. Symb. Log. 2019). The note also highlights the close connection between these characterizations and the problem of convergence on random (...)
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  14. On Explaining the Success of Induction.Tom F. Sterkenburg - forthcoming - British Journal for the Philosophy of Science.
    Douven (in press) observes that Schurz's meta-inductive justification of induction cannot explain the great empirical success of induction, and offers an explanation based on computer simulations of the social and evolutionary development of our inductive practices. In this paper, I argue that Douven's account does not address the explanatory question that Schurz's argument leaves open, and that the assumption of the environment's induction-friendliness that is inherent to Douven's simulations is not justified by Schurz's argument.
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  15. Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions.Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith & Simone Stumpf - 2024 - Information Fusion 106 (June 2024).
    As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse (...)
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  16. Universal Agent Mixtures and the Geometry of Intelligence.Samuel Allen Alexander, David Quarel, Len Du & Marcus Hutter - 2023 - Aistats.
    Inspired by recent progress in multi-agent Reinforcement Learning (RL), in this work we examine the collective intelligent behaviour of theoretical universal agents by introducing a weighted mixture operation. Given a weighted set of agents, their weighted mixture is a new agent whose expected total reward in any environment is the corresponding weighted average of the original agents' expected total rewards in that environment. Thus, if RL agent intelligence is quantified in terms of performance across environments, the weighted mixture's intelligence is (...)
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  17. Inter‐temporal rationality without temporal representation.Simon A. B. Brown - 2023 - Mind and Language 38 (2):495-514.
    Recent influential accounts of temporal representation—the use of mental representations with explicit temporal contents, such as before and after relations and durations—sharply distinguish representation from mere sensitivity. A common, important picture of inter-temporal rationality is that it consists in maximizing total expected discounted utility across time. By analyzing reinforcement learning algorithms, this article shows that, given such notions of temporal representation and inter-temporal rationality, it would be possible for an agent to achieve inter-temporal rationality without temporal representation. It then explores (...)
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  18. The deep neural network approach to the reference class problem.Oliver Buchholz - 2023 - Synthese 201 (3):1-24.
    Methods of machine learning (ML) are gradually complementing and sometimes even replacing methods of classical statistics in science. This raises the question whether ML faces the same methodological problems as classical statistics. This paper sheds light on this question by investigating a long-standing challenge to classical statistics: the reference class problem (RCP). It arises whenever statistical evidence is applied to an individual object, since the individual belongs to several reference classes and evidence might vary across them. Thus, the problem consists (...)
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  19. From deep learning to rational machines: what the history of philosophy can teach us about the future of artifical intelligence.Cameron J. Buckner - 2023 - New York, NY: Oxford University Press.
    This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning’s current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active (...)
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  20. Epistemic virtues of harnessing rigorous machine learning systems in ethically sensitive domains.Thomas F. Burns - 2023 - Journal of Medical Ethics 49 (8):547-548.
    Some physicians, in their care of patients at risk of misusing opioids, use machine learning (ML)-based prediction drug monitoring programmes (PDMPs) to guide their decision making in the prescription of opioids. This can cause a conflict: a PDMP Score can indicate a patient is at a high risk of opioid abuse while a patient expressly reports oppositely. The prescriber is then left to balance the credibility and trust of the patient with the PDMP Score. Pozzi1 argues that a prescriber who (...)
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  21. La scorciatoia.Nello Cristianini - 2023 - Bologna: Il Mulino.
    La scorciatoia - Come le macchine sono diventate intelligenti senza pensare in modo umano -/- Le nostre creature sono diverse da noi e talvolta più forti. Per poterci convivere dobbiamo imparare a conoscerle Vagliano curricula, concedono mutui, scelgono le notizie che leggiamo: le macchine intelligenti sono entrate nelle nostre vite, ma non sono come ce le aspettavamo. Fanno molte delle cose che volevamo, e anche qualcuna in più, ma non possiamo capirle o ragionare con loro, perché il loro comportamento è (...)
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  22. Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection.Tosin Ige & Christopher Kiekintveld - 2023 - Proceedings of the IEEE 1:5.
    Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world applications. The aim of this study is to implement and compare the performances of each variant of the Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant’s assumption and their performance. Our investigation showed that each variant of the Bayesian algorithm blindly follows its (...)
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  23. Machine learning and the quest for objectivity in climate model parameterization.Julie Jebeile, Vincent Lam, Mason Majszak & Tim Räz - 2023 - Climatic Change 176 (101).
    Parameterization and parameter tuning are central aspects of climate modeling, and there is widespread consensus that these procedures involve certain subjective elements. Even if the use of these subjective elements is not necessarily epistemically problematic, there is an intuitive appeal for replacing them with more objective (automated) methods, such as machine learning. Relying on several case studies, we argue that, while machine learning techniques may help to improve climate model parameterization in several ways, they still require expert judgment that involves (...)
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  24. Holding Large Language Models to Account.Ryan Miller - 2023 - In Berndt Müller (ed.), Proceedings of the AISB Convention. Swansea: Society for the Study of Artificial Intelligence and the Simulation of Behaviour. pp. 7-14.
    If Large Language Models can make real scientific contributions, then they can genuinely use language, be systematically wrong, and be held responsible for their errors. AI models which can make scientific contributions thereby meet the criteria for scientific authorship.
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  25. Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?Frank Ursin, Felix Lindner, Timo Ropinski, Sabine Salloch & Cristian Timmermann - 2023 - Ethik in der Medizin 35 (2):173-199.
    Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which (...)
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  26. Machine Learning, Misinformation, and Citizen Science.Adrian K. Yee - 2023 - European Journal for Philosophy of Science 13 (56):1-24.
    Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.
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  27. Extending Environments To Measure Self-Reflection In Reinforcement Learning.Samuel Allen Alexander, Michael Castaneda, Kevin Compher & Oscar Martinez - 2022 - Journal of Artificial General Intelligence 13 (1).
    We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent's hypothetical behavior. Since good performance usually requires paying attention to whatever things the environment's outputs are based on, we argue that for an agent to achieve on-average good performance across many such extended environments, it is necessary for the agent to self-reflect. Thus weighted-average performance over the space of all suitably well-behaved extended environments could be considered a (...)
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  28. Pseudo-visibility: A Game Mechanic Involving Willful Ignorance.Samuel Allen Alexander & Arthur Paul Pedersen - 2022 - FLAIRS-35.
    We present a game mechanic called pseudo-visibility for games inhabited by non-player characters (NPCs) driven by reinforcement learning (RL). NPCs are incentivized to pretend they cannot see pseudo-visible players: the training environment simulates an NPC to determine how the NPC would act if the pseudo-visible player were invisible, and penalizes the NPC for acting differently. NPCs are thereby trained to selectively ignore pseudo-visible players, except when they judge that the reaction penalty is an acceptable tradeoff (e.g., a guard might accept (...)
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  29. Varieties of Artificial Moral Agency and the New Control Problem.Marcus Arvan - 2022 - Humana.Mente - Journal of Philosophical Studies 15 (42):225-256.
    This paper presents a new trilemma with respect to resolving the control and alignment problems in machine ethics. Section 1 outlines three possible types of artificial moral agents (AMAs): (1) 'Inhuman AMAs' programmed to learn or execute moral rules or principles without understanding them in anything like the way that we do; (2) 'Better-Human AMAs' programmed to learn, execute, and understand moral rules or principles somewhat like we do, but correcting for various sources of human moral error; and (3) 'Human-Like (...)
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  30. Machine Learning, Functions and Goals.Patrick Butlin - 2022 - Croatian Journal of Philosophy 22 (66):351-370.
    Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs (...)
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  31. Interprétabilité et explicabilité de phénomènes prédits par de l’apprentissage machine.Christophe Denis & Franck Varenne - 2022 - Revue Ouverte d'Intelligence Artificielle 3 (3-4):287-310.
    Le déficit d’explicabilité des techniques d’apprentissage machine (AM) pose des problèmes opérationnels, juridiques et éthiques. Un des principaux objectifs de notre projet est de fournir des explications éthiques des sorties générées par une application fondée sur de l’AM, considérée comme une boîte noire. La première étape de ce projet, présentée dans cet article, consiste à montrer que la validation de ces boîtes noires diffère épistémologiquement de celle mise en place dans le cadre d’une modélisation mathéma- tique et causale d’un phénomène (...)
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  32. Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.
    Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. (...)
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  33. THE ROBOTS ARE COMING: What’s Happening in Philosophy (WHiP)-The Philosophers, August 2022.Jeff Hawley - 2022 - Philosophynews.Com.
    Should we fear a future in which the already tricky world of academic publishing is increasingly crowded out by super-intelligent artificial general intelligence (AGI) systems writing papers on phenomenology and ethics? What are the chances that AGI advances to a stage where a human philosophy instructor is similarly removed from the equation? If Jobst Landgrebe and Barry Smith are correct, we have nothing to fear.
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  34. A Fuzzy-Cognitive-Maps Approach to Decision-Making in Medical Ethics.Alice Hein, Lukas J. Meier, Alena Buyx & Klaus Diepold - 2022 - 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
    Although machine intelligence is increasingly employed in healthcare, the realm of decision-making in medical ethics remains largely unexplored from a technical perspective. We propose an approach based on fuzzy cognitive maps (FCMs), which builds on Beauchamp and Childress’ prima-facie principles. The FCM’s weights are optimized using a genetic algorithm to provide recommendations regarding the initiation, continuation, or withdrawal of medical treatment. The resulting model approximates the answers provided by our team of medical ethicists fairly well and offers a high degree (...)
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  35. The Deskilling of Teaching and the Case for Intelligent Tutoring Systems.James Hughes - 2022 - Journal of Ethics and Emerging Technologies 31 (2):1-16.
    This essay describes trends in the organization of work that have laid the groundwork for the adoption of interactive AI-driven instruction tools, and the technological innovations that will make intelligent tutoring systems truly competitive with human teachers. Since the origin of occupational specialization, the collection and transmission of knowledge have been tied to individual careers and job roles, specifically doctors, teachers, clergy, and lawyers, the paradigmatic knowledge professionals. But these roles have also been tied to texts and organizations that can (...)
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  36. Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm.Tosin Ige - 2022 - International Journal of Advanced Computer Science and Applications 13 (5):1 - 4.
    Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful (...)
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  37. AI Powered Anti-Cyber bullying system using Machine Learning Algorithm of Multinomial Naïve Bayes and Optimized Linear Support Vector Machine.Tosin Ige - 2022 - International Journal of Advanced Computer Science and Applications 13 (5):1 - 5.
    Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue.” ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation (...)
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  38. Stimuli-Based Control of Negative Emotions in a Digital Learning Environment.Rossitza Kaltenborn, Mincho Hadjiski & Stefan Koynov - 2022 - In V. Sgurev, V. Jotsov & J. Kacprzyk (eds.), Advances in Intelligent Systems Research and Innovation. Cambridge, Vereinigtes Königreich:
    The proposed system for coping negative emotions arising during the learning process is considered as an embedded part of the complex intelligent learning system realized in a digital environment. By applying data-driven procedures on the current and retrospective data the main didactic-based stimuli provoking emotion generation are identified. They are examined as dominant negative emotions in the context of learning. Due to the presence of strong internal and output interconnections between teaching and emotional states, an intelligent decoupling multidimensional control scheme (...)
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  39. Big Data and Artificial Intelligence Based on Personalized Learning – Conformity with Whitehead’s Organismic Theory.Rossitza Kaltenborn & Mintcho Hadjiski - 2022 - In F. Riffert & V. Petrov (eds.), Education and Learning in a World of Accelerated Knowledge Growth: Current Trends in Process Thought. Cambridge, Vereinigtes Königreich:
    The study shows the existence of a broad conformity between Whitehead’s organismic cosmology and the contemporary theory of complex systems at a relevant level of abstraction. One of the most promising directions of educational transformation in the age of big data and artificial intelligence – personalized learning – is conceived as a system of systems and reveals its close congruence with a number of basic Whiteheadian concepts. A new functional structure of personalized learning systems is proposed, including all the core (...)
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  40. Are “Intersectionally Fair” AI Algorithms Really Fair to Women of Color? A Philosophical Analysis.Youjin Kong - 2022 - Facct: Proceedings of the Acm Conference on Fairness, Accountability, and Transparency:485-494.
    A growing number of studies on fairness in artificial intelligence (AI) use the notion of intersectionality to measure AI fairness. Most of these studies take intersectional fairness to be a matter of statistical parity among intersectional subgroups: an AI algorithm is “intersectionally fair” if the probability of the outcome is roughly the same across all subgroups defined by different combinations of the protected attributes. This paper identifies and examines three fundamental problems with this dominant interpretation of intersectional fairness in AI. (...)
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  41. (Un)Fairness in AI: An Intersectional Feminist Analysis.Youjin Kong - 2022 - Blog of the American Philosophical Association, Women in Philosophy Series.
    Racial, Gender, and Intersectional Biases in AI / -/- Dominant View of Intersectional Fairness in the AI Literature / -/- Three Fundamental Problems with the Dominant View / 1. Overemphasis on Intersections of Attributes / 2. Dilemma between Infinite Regress and Fairness Gerrymandering / 3. Narrow Understanding of Fairness as Parity / -/- Rethinking AI Fairness: from Weak to Strong Fairness.
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  42. Philosophical foundations of intelligence collection and analysis: a defense of ontological realism.William Mandrick & Barry Smith - 2022 - Intelligence and National Security 38.
    There is a common misconception across the lntelligence Community (IC) to the effect that information trapped within multiple heterogeneous data silos can be semantically integrated by the sorts of meaning-blind statistical methods employed in much of artificial intelligence (Al) and natural language processlng (NLP). This leads to the misconception that incoming data can be analysed coherently by relying exclusively on the use of statistical algorithms and thus without any shared framework for classifying what the data are about. Unfortunately, such approaches (...)
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  43. Calculating the mind-change complexity of learning algebraic structures.Luca San Mauro, Nikolay Bazhenov & Vittorio Cipriani - 2022 - In Ulrich Berger, Johanna N. Y. Franklin, Florin Manea & Arno Pauly (eds.), Revolutions and Revelations in Computability. Cham, Svizzera: pp. 1-12.
    This paper studies algorithmic learning theory applied to algebraic structures. In previous papers, we have defined our framework, where a learner, given a family of structures, receives larger and larger pieces of an arbitrary copy of a structure in the family and, at each stage, is required to output a conjecture about the isomorphism type of such a structure. The learning is successful if there is a learner that eventually stabilizes to a correct conjecture. Here, we analyze the number of (...)
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  44. Algorithmic Microaggressions.Emma McClure & Benjamin Wald - 2022 - Feminist Philosophy Quarterly 8 (3).
    We argue that machine learning algorithms can inflict microaggressions on members of marginalized groups and that recognizing these harms as instances of microaggressions is key to effectively addressing the problem. The concept of microaggression is also illuminated by being studied in algorithmic contexts. We contribute to the microaggression literature by expanding the category of environmental microaggressions and highlighting the unique issues of moral responsibility that arise when we focus on this category. We theorize two kinds of algorithmic microaggression, stereotyping and (...)
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  45. Clinical Ethics – To Compute, or Not to Compute?Lukas J. Meier, Alice Hein, Klaus Diepold & Alena Buyx - 2022 - American Journal of Bioethics 22 (12):W1-W4.
    Can machine intelligence do clinical ethics? And if so, would applying it to actual medical cases be desirable? In a recent target article (Meier et al. 2022), we described the piloting of our advisory algorithm METHAD. Here, we reply to commentaries published in response to our project. The commentaries fall into two broad categories: concrete criticism that concerns the development of METHAD; and the more general question as to whether one should employ decision-support systems of this kind—the debate we set (...)
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  46. ANNs and Unifying Explanations: Reply to Erasmus, Brunet, and Fisher.Yunus Prasetya - 2022 - Philosophy and Technology 35 (2):1-9.
    In a recent article, Erasmus, Brunet, and Fisher (2021) argue that Artificial Neural Networks (ANNs) are explainable. They survey four influential accounts of explanation: the Deductive-Nomological model, the Inductive-Statistical model, the Causal-Mechanical model, and the New-Mechanist model. They argue that, on each of these accounts, the features that make something an explanation is invariant with regard to the complexity of the explanans and the explanandum. Therefore, they conclude, the complexity of ANNs (and other Machine Learning models) does not make them (...)
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  47. Model-induced escape.Barry Smith - 2022 - Facing the Future, Facing the Screen: 10Th Budapest Visual Learning Conference.
    We can illustrate the phenomenon of model-induced escape by examining the phenomenon of spam filters. Spam filter A is, we can assume, very effective at blocking spam. Indeed it is so effective that it motivates the authors of spam to invent new types of spam that will beat the filters of spam filter A. -/- An example of this phenomenon in the realm of philosophy is illustrated in the work of Nyíri on Wittgenstein's political beliefs. Nyíri writes a paper demonstrating (...)
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  48. On characterizations of learnability with computable learners.Tom F. Sterkenburg - 2022 - Proceedings of Machine Learning Research 178:3365-3379.
    We study computable PAC (CPAC) learning as introduced by Agarwal et al. (2020). First, we consider the main open question of finding characterizations of proper and improper CPAC learning. We give a characterization of a closely related notion of strong CPAC learning, and provide a negative answer to the COLT open problem posed by Agarwal et al. (2021) whether all decidably representable VC classes are improperly CPAC learnable. Second, we consider undecidability of (computable) PAC learnability. We give a simple general (...)
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  49. Inductive Risk, Understanding, and Opaque Machine Learning Models.Emily Sullivan - 2022 - Philosophy of Science 89 (5):1065-1074.
    Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity (...)
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  50. Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In (...)
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