Results for ' machine learning algorithm'

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  1. Clinical applications of machine learning algorithms: beyond the black box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
    Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
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  2. AI Powered Anti-Cyber bullying system using Machine Learning Algorithm of Multinomial Naïve Bayes and Optimized Linear Support Vector Machine.Tosin Ige & Sikiru Adewale - 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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  3. 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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  4.  89
    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 (...)
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  5.  97
    Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics.Vlasta Sikimić & Sandro Radovanović - 2022 - European Journal for Philosophy of Science 12 (3):1-21.
    As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments (...)
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  6. Sarcasm Detection in Headline News using Machine and Deep Learning Algorithms.Alaa Barhoom, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):66-73.
    Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...)
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  7. Sarcasm Detection in Headline News using Machine and Deep Learning Algorithms.Alaa Barhoom, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):66-73.
    Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...)
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  8. Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...)
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  9. Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...)
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  10. Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning.Gabbrielle Johnson - forthcoming - Journal Moral Philosophy.
    As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments (...)
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  11. Machine learning, justification, and computational reliabilism.Juan Manuel Durán - manuscript
    This article asks the question, ``what is reliable machine learning?'' As I intend to answer it, this is a question about epistemic justification. Reliable machine learning gives justification for believing its output. Current approaches to reliability (e.g., transparency) involve showing the inner workings of an algorithm (functions, variables, etc.) and how they render outputs. We then have justification for believing the output because we know how it was computed. Thus, justification is contingent on what can (...)
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  12. Egalitarian Machine Learning.Clinton Castro, David O’Brien & Ben Schwan - 2023 - Res Publica 29 (2):237–264.
    Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of (...)
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  13. Fair machine learning under partial compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 55–65.
    Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the (...)
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  14.  65
    MACHINE LEARNING IMPROVED ADVANCED DIAGNOSIS OF SOFT TISSUES TUMORS.M. Bavadharani - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):112-123.
    Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have proposed (...)
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  15. Consequences of unexplainable machine learning for the notions of a trusted doctor and patient autonomy.Michal Klincewicz & Lily Frank - 2020 - Proceedings of the 2nd EXplainable AI in Law Workshop (XAILA 2019) Co-Located with 32nd International Conference on Legal Knowledge and Information Systems (JURIX 2019).
    This paper provides an analysis of the way in which two foundational principles of medical ethics–the trusted doctor and patient autonomy–can be undermined by the use of machine learning (ML) algorithms and addresses its legal significance. This paper can be a guide to both health care providers and other stakeholders about how to anticipate and in some cases mitigate ethical conflicts caused by the use of ML in healthcare. It can also be read as a road map as (...)
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  16. Exploring Machine Learning Techniques for Coronary Heart Disease Prediction.Hisham Khdair - 2021 - International Journal of Advanced Computer Science and Applications 12 (5):28-36.
    Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the (...)
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  17. Fraudulent Financial Transactions Detection Using Machine Learning.Mosa M. M. Megdad, Samy S. Abu-Naser & Bassem S. Abu-Nasser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (3):30-39.
    It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep (...). The dataset was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)
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  18. Widening Access to Applied Machine Learning With TinyML.Vijay Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Lara Suzuki, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart & Dustin Tingley - 2022 - Harvard Data Science Review 4 (1).
    Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML (...)
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  19. Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.Joshua Hatherley & Robert Sparrow - 2023 - Journal of the American Medical Informatics Association 30 (2):361-366.
    Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for (...)
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  20.  3
    Machine learning in bail decisions and judges’ trustworthiness.Alexis Morin-Martel - 2023 - AI and Society:1-12.
    The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong (...)
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  21. The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–⁠32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to (...)
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  22. The Use and Misuse of Counterfactuals in Ethical Machine Learning.Atoosa Kasirzadeh & Andrew Smart - 2021 - In ACM Conference on Fairness, Accountability, and Transparency (FAccT 21).
    The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness (...)
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  23. Semantic Information G Theory and Logical Bayesian Inference for Machine Learning.Chenguang Lu - 2019 - Information 10 (8):261.
    An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. MultilabelMultilabel A semantic channel in (...)
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  24. Prognostic System for Heart Disease using Machine Learning: A Review.R. Senthilkumar - 2021 - Journal of Science Technology and Research (JSTAR) 2 (1):33-38.
    In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, Cholesterol, Diabetes are (...)
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  25. Algorithmic Fairness from a Non-ideal Perspective.Sina Fazelpour & Zachary C. Lipton - 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.
    Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and (...)
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  26. Negligent Algorithmic Discrimination.Andrés Páez - 2021 - Law and Contemporary Problems 84 (3):19-33.
    The use of machine learning algorithms has become ubiquitous in hiring decisions. Recent studies have shown that many of these algorithms generate unlawful discriminatory effects in every step of the process. The training phase of the machine learning models used in these decisions has been identified as the main source of bias. For a long time, discrimination cases have been analyzed under the banner of disparate treatment and disparate impact, but these concepts have been shown to (...)
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  27. Machine-Believers Learning Faiths & Knowledges: The Gospel According to GPT.Virgil W. Brower - 2021 - Internationales Jahrbuch Für Medienphilosophie 7 (1):97-121.
    One is occasionally reminded of Foucault's proclamation in a 1970 interview that "perhaps, one day this century will be known as Deleuzian." Less often is one compelled to update and restart with a supplementary counter-proclamation of the mathematician, David Lindley: "the twenty-first century would be a Bayesian era..." The verb tenses of both are conspicuous. // To critically attend to what is today often feared and demonized, but also revered, deployed, and commonly referred to as algorithm(s), one cannot avoid (...)
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  28.  64
    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, (...)
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  29. On algorithmic fairness in medical practice.Thomas Grote & Geoff Keeling - 2022 - Cambridge Quarterly of Healthcare Ethics 31 (1):83-94.
    The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness (...)
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  30. Democratizing Algorithmic Fairness.Pak-Hang Wong - 2020 - Philosophy and Technology 33 (2):225-244.
    Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as (...)
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  31. Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.Lukas J. Meier, Alice Hein, Klaus Diepold & Alena Buyx - 2022 - American Journal of Bioethics 22 (7):4-20.
    Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. (...)
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  32. The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.Michael Stuart - 2019 - In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer Verlag.
    When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them (...)
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  33. Formalising trade-offs beyond algorithmic fairness: lessons from ethical philosophy and welfare economics.Michelle Seng Ah Lee, Luciano Floridi & Jatinder Singh - 2021 - AI and Ethics 3.
    There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and (...)
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  34. The ethics of algorithms: mapping the debate.Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter & Luciano Floridi - 2016 - Big Data and Society 3 (2).
    In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...)
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  35. The ethics of algorithms: key problems and solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2021 - AI and Society.
    Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic (...)
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  36. A DEEP LEARNING APPROACH FOR LSTM BASED COVID-19 FORECASTING SYSTEM.K. Jothimani - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):28-38.
    : COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts (...)
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  37. The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems.Kathleen Creel & Deborah Hellman - 2022 - Canadian Journal of Philosophy 52 (1):26-43.
    This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to (...)
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  38. Genealogy of Algorithms: Datafication as Transvaluation.Virgil W. Brower - 2020 - le Foucaldien 6 (1):1-43.
    This article investigates religious ideals persistent in the datafication of information society. Its nodal point is Thomas Bayes, after whom Laplace names the primal probability algorithm. It reconsiders their mathematical innovations with Laplace's providential deism and Bayes' singular theological treatise. Conceptions of divine justice one finds among probability theorists play no small part in the algorithmic data-mining and microtargeting of Cambridge Analytica. Theological traces within mathematical computation are emphasized as the vantage over large numbers shifts to weights beyond enumeration (...)
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  39. The algorithm audit: Scoring the algorithms that score us.Jovana Davidovic, Shea Brown & Ali Hasan - 2021 - Big Data and Society 8 (1).
    In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...)
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  40. The Relations Between Pedagogical and Scientific Explanations of Algorithms: Case Studies from the French Administration.Maël Pégny - manuscript
    The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology (...)
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  41. The Fair Chances in Algorithmic Fairness: A Response to Holm.Clinton Castro & Michele Loi - 2023 - Res Publica 29 (2):231–237.
    Holm (2022) argues that a class of algorithmic fairness measures, that he refers to as the ‘performance parity criteria’, can be understood as applications of John Broome’s Fairness Principle. We argue that the performance parity criteria cannot be read this way. This is because in the relevant context, the Fairness Principle requires the equalization of actual individuals’ individual-level chances of obtaining some good (such as an accurate prediction from a predictive system), but the performance parity criteria do not guarantee any (...)
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  42. Diagnosis of Pneumonia Using Deep Learning.Alaa M. A. Barhoom & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (2):48-68.
    Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of (...)
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  43. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. (...)
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  44. Machine intelligence: a chimera.Mihai Nadin - 2019 - AI and Society 34 (2):215-242.
    The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...)
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  45. rethinking machine ethics in the era of ubiquitous technology.Jeffrey White (ed.) - 2015 - Hershey, PA, USA: IGI.
    Table of Contents Foreword .................................................................................................... ......................................... xiv Preface .................................................................................................... .............................................. xv Acknowledgment .................................................................................................... .......................... xxiii Section 1 On the Cusp: Critical Appraisals of a Growing Dependency on Intelligent Machines Chapter 1 Algorithms versus Hive Minds and the Fate of Democracy ................................................................... 1 Rick Searle, IEET, USA Chapter 2 We Can Make Anything: Should We? .................................................................................................. 15 Chris Bateman, University of Bolton, UK Chapter 3 Grounding Machine Ethics within the Natural System ........................................................................ 30 Jared Gassen, JMG Advising, USA Nak Young Seong, Independent Scholar, (...)
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  46.  21
    Socially Good AI Contributions for the Implementation of Sustainable Development in Mountain Communities Through an Inclusive Student-Engaged Learning Model.Tyler Lance Jaynes, Baktybek Abdrisaev & Linda MacDonald Glenn - 2023 - In Francesca Mazzi & Luciano Floridi (eds.), The Ethics of Artificial Intelligence for the Sustainable Development Goals. Cham: Springer Verlag. pp. 269-289.
    AI is increasingly becoming based upon Internet-dependent systems to handle the massive amounts of data it requires to function effectively regardless of the availability of stable Internet connectivity in every affected community. As such, sustainable development (SD) for rural and mountain communities will require more than just equitable access to broadband Internet connection. It must also include a thorough means whereby to ensure that affected communities gain the education and tools necessary to engage inclusively with new technological advances, whether they (...)
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  47. Information, learning and falsification.David Balduzzi - 2011
    There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The third, statistical (...)
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  48. From human resources to human rights: Impact assessments for hiring algorithms.Josephine Yam & Joshua August Skorburg - 2021 - Ethics and Information Technology 23 (4):611-623.
    Over the years, companies have adopted hiring algorithms because they promise wider job candidate pools, lower recruitment costs and less human bias. Despite these promises, they also bring perils. Using them can inflict unintentional harms on individual human rights. These include the five human rights to work, equality and nondiscrimination, privacy, free expression and free association. Despite the human rights harms of hiring algorithms, the AI ethics literature has predominantly focused on abstract ethical principles. This is problematic for two reasons. (...)
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  49. Classification of Real and Fake Human Faces Using Deep Learning.Fatima Maher Salman & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):1-14.
    Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning (...)
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  50. Predicting Whether Student will continue to Attend College or not using Deep Learning.Samy S. Abu-Naser, Qasem M. M. Zarandah, Moshera M. Elgohary, Zakaria K. D. AlKayyali, Bassem S. Abu-Nasser & Ashraf M. Taha - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (6):33-45.
    According to the literature review, there is much room for improvement of college student retention. The aim of this research is to evaluate the possibility of using deep and machine learning algorithms to predict whether students continue to attend college or will stop attending college. In this research a feature assessment is done on the dataset available from Kaggle depository. The performance of 20 learning supervised machine learning algorithms and one deep learning algorithm (...)
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