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Journal articles on the topic 'Algorithmic decision systems'
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Herzog, Lisa. "Algorithmisches Entscheiden, Ambiguitätstoleranz und die Frage nach dem Sinn." Deutsche Zeitschrift für Philosophie 69, no. 2 (April 1, 2021): 197–213. http://dx.doi.org/10.1515/dzph-2021-0016.
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Abstract In more and more contexts, human decision-making is replaced by algorithmic decision-making. While promising to deliver efficient and objective decisions, algorithmic decision systems have specific weaknesses, some of which are particularly dangerous if data are collected and processed by profit-oriented companies. In this paper, I focus on two problems that are at the root of the logic of algorithmic decision-making: (1) (in)tolerance for ambiguity, and (2) instantiations of Campbell’s law, i. e. of indicators that are used for “social decision-making” being subject to “corruption pressures” and tending to “distort and corrupt” the underlying social processes. As a result, algorithmic decision-making can risk missing the point of the social practice in question. These problems are intertwined with problems of structural injustice; hence, if algorithms are to deliver on their promises of efficiency and objectivity, accountability and critical scrutiny are needed.
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Kleinberg, Jon, and Manish Raghavan. "Algorithmic monoculture and social welfare." Proceedings of the National Academy of Sciences 118, no. 22 (May 25, 2021): e2018340118. http://dx.doi.org/10.1073/pnas.2018340118.
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As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here, we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under “normal” operations and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.
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Zerilli, John, Alistair Knott, James Maclaurin, and Colin Gavaghan. "Algorithmic Decision-Making and the Control Problem." Minds and Machines 29, no. 4 (December 2019): 555–78. http://dx.doi.org/10.1007/s11023-019-09513-7.
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AbstractThe danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem”, understood as the tendency of the human within a human–machine control loop to become complacent, over-reliant or unduly diffident when faced with the outputs of a reliable autonomous system. While the control problem has been investigated for some time, up to this point its manifestation in machine learning contexts has not received serious attention. This paper aims to fill that gap. We argue that, except in certain special circumstances, algorithmic decision tools should not be used in high-stakes or safety-critical decisions unless the systems concerned are significantly “better than human” in the relevant domain or subdomain of decision-making. More concretely, we recommend three strategies to address the control problem, the most promising of which involves a complementary (and potentially dynamic) coupling between highly proficient algorithmic tools and human agents working alongside one another. We also identify six key principles which all such human–machine systems should reflect in their design. These can serve as a framework both for assessing the viability of any such human–machine system as well as guiding the design and implementation of such systems generally.
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Green, Ben, and Yiling Chen. "Algorithm-in-the-Loop Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13663–64. http://dx.doi.org/10.1609/aaai.v34i09.7115.
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We introduce a new framework for conceiving of and studying algorithms that are deployed to aid human decision making: “algorithm-in-the-loop” systems. The algorithm-in-the-loop framework centers human decision making, providing a more precise lens for studying the social impacts of algorithmic decision making aids. We report on two experiments that evaluate algorithm-in-the-loop decision making and find significant limits to these systems.
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Rubel, Alan, Clinton Castro, and Adam Pham. "Algorithms, Agency, and Respect for Persons." Social Theory and Practice 46, no. 3 (2020): 547–72. http://dx.doi.org/10.5840/soctheorpract202062497.
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Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency.
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Žliobaitė, Indrė. "Measuring discrimination in algorithmic decision making." Data Mining and Knowledge Discovery 31, no. 4 (March 31, 2017): 1060–89. http://dx.doi.org/10.1007/s10618-017-0506-1.
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BRKAN, Maja, and Grégory BONNET. "Legal and Technical Feasibility of the GDPR’s Quest for Explanation of Algorithmic Decisions: of Black Boxes, White Boxes and Fata Morganas." European Journal of Risk Regulation 11, no. 1 (March 2020): 18–50. http://dx.doi.org/10.1017/err.2020.10.
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Understanding of the causes and correlations for algorithmic decisions is currently one of the major challenges of computer science, addressed under an umbrella term “explainable AI (XAI)”. Being able to explain an AI-based system may help to make algorithmic decisions more satisfying and acceptable, to better control and update AI-based systems in case of failure, to build more accurate models, and to discover new knowledge directly or indirectly. On the legal side, the question whether the General Data Protection Regulation (GDPR) provides data subjects with the right to explanation in case of automated decision-making has equally been the subject of a heated doctrinal debate. While arguing that the right to explanation in the GDPR should be a result of interpretative analysis of several GDPR provisions jointly, the authors move this debate forward by discussing the technical and legal feasibility of the explanation of algorithmic decisions. Legal limits, in particular the secrecy of algorithms, as well as technical obstacles could potentially obstruct the practical implementation of this right. By adopting an interdisciplinary approach, the authors explore not only whether it is possible to translate the EU legal requirements for an explanation into the actual machine learning decision-making, but also whether those limitations can shape the way the legal right is used in practice.
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Shrestha, Yash Raj, and Yongjie Yang. "Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems." Algorithms 12, no. 9 (September 18, 2019): 199. http://dx.doi.org/10.3390/a12090199.
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Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and more decisions being delegated to algorithms, we have also encountered increasing evidence of ethical issues with respect to biases and lack of fairness pertaining to algorithmic decision-making outcomes. Such outcomes may lead to detrimental consequences to minority groups in terms of gender, ethnicity, and race. As a response, recent research has shifted from design of algorithms that merely pursue purely optimal outcomes with respect to a fixed objective function into ones that also ensure additional fairness properties. In this study, we aim to provide a broad and accessible overview of the recent research endeavor aimed at introducing fairness into algorithms used in automated decision-making in three principle domains, namely, multi-winner voting, machine learning, and recommender systems. Even though these domains have developed separately from each other, they share commonality with respect to decision-making as an application, which requires evaluation of a given set of alternatives that needs to be ranked with respect to a clearly defined objective function. More specifically, these relate to tasks such as (1) collectively selecting a fixed number of winner (or potentially high valued) alternatives from a given initial set of alternatives; (2) clustering a given set of alternatives into disjoint groups based on various similarity measures; or (3) finding a consensus ranking of entire or a subset of given alternatives. To this end, we illustrate a multitude of fairness properties studied in these three streams of literature, discuss their commonalities and interrelationships, synthesize what we know so far, and provide a useful perspective for future research.
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Ahonen, Pertti, and Tero Erkkilä. "Transparency in algorithmic decision-making: Ideational tensions and conceptual shifts in Finland." Information Polity 25, no. 4 (December 4, 2020): 419–32. http://dx.doi.org/10.3233/ip-200259.
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This article uses a theoretical and methodological framework derived from the political theorist Quentin Skinner and the conceptual historian Reinhart Koselleck to examine ideational and conceptual tensions and shifts related to the transparency of algorithmic and other automatic governmental decision-making in Finland. Most of the research material comprises national and international official documents and semi-structured expert interviews. In Finland, the concepts of ‘algorithmic transparency’ and other ‘transparency of automatic decision-making’ are situated amongst a complex array of legal, ethical, political, policy-oriented, managerial, and technical semantic fields. From 2016 to 2019 Finland’s Deputy Ombudsman of Parliament and the Constitutional Committee of Parliament pinpointed issues in algorithmic and other automatic decision-making with the consequence that at the turn of 2019 and 2020, the Ministry of Justice started moving towards the preparation of new legislation to resolve these issues. In conclusion and as expected, Finland’s version of the Nordic tradition of the public sphere with established legal guarantees of public access to government documents indeed has both important enabling and constraining effects upon resolving the transparency issues.
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YÁÑEZ, J., J. MONTERO, and D. GÓMEZ. "AN ALGORITHMIC APPROACH TO PREFERENCE REPRESENTATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, supp02 (August 2008): 1–18. http://dx.doi.org/10.1142/s0218488508005455.
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In a previous paper, the authors proposed an alternative approach to classical dimension theory, based upon a general representation of strict preferences not being restricted to partial order sets. Without any relevant restriction, the proposed approach was conceived as a potential powerful tool for decision making problems where basic information has been modeled by means of valued binary preference relations. In fact, assuming that each decision maker is able to consistently manage intensity values for preferences is a strong assumption even when there are few alternatives being involved (if the number of alternatives is large, the same criticism applies to crisp preferences). Any representation tool, as the one proposed by the authors, will in principle play a key role in order to help decision makers to understand their preference structure. In this paper we introduce an alternative approach in order to avoid certain complexity issues of the initial proposal, allowing a close representation easier to be obtained in practice.
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Peeters, Rik. "The agency of algorithms: Understanding human-algorithm interaction in administrative decision-making." Information Polity 25, no. 4 (December 4, 2020): 507–22. http://dx.doi.org/10.3233/ip-200253.
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With the rise of computer algorithms in administrative decision-making, concerns are voiced about their lack of transparency and discretionary space for human decision-makers. However, calls to ‘keep humans in the loop’ may be moot points if we fail to understand how algorithms impact human decision-making and how algorithmic design impacts the practical possibilities for transparency and human discretion. Through a review of recent academic literature, three algorithmic design variables that determine the preconditions for human transparency and discretion and four main sources of variation in ‘human-algorithm interaction’ are identified. The article makes two contributions. First, the existing evidence is analysed and organized to demonstrate that, by working upon behavioural mechanisms of decision-making, the agency of algorithms extends beyond their computer code and can profoundly impact human behaviour and decision-making. Second, a research agenda for studying how computer algorithms affect administrative decision-making is proposed.
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Hossfeld, Steffen. "The Advantage of Digital Decision Making for Strategic Decisions – Proofed by a Supply Chain Case." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 3, no. 5 (2017): 7–20. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.35.1001.
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This paper will discuss the advantage of decision making supported by a digital system and will provide an overview of an empiric analysis researched on this topic. Decision making in organizations is a significant system implied task of managers and therefore a broad area in scientific research, not only in the disciplines management or business studies – even from technical to humanistic disciplines. Nowadays the trend of digitalization captures all areas of life especially in business, as well as the typical management task of decision making. Triggered by the digitalization trend business will move toward an autonomous decision making of machines or cyber systems. The important step toward autonomous decisions or decision support (cyber systems will prepare a decision, but finally executed by a human) will be the next development step for decision making. Designed algorithmic models for these decisions will use the content of classical decision models to reach maximum utility. Hence, a view on classical decision making will illuminate the basis for these models, from researchers like von Neumann/Morgenstern or Bernoulli. Furthermore, digitalization changes the process of decision making, especially focussing on the behavioral part of decisions, discussed from Simon, Selten or Tversky/Kahneman. Due to reason that today the human aspect of decision making is sustainable in organizations. The hypothesis that the digital set up for decision making in organizations will increase the efficiency of strategic decisions will be proofed by an empiric study. The research method for the empirical part of this paper is a questionnaire. It is online questionnaire which will be answered by professionals and scholars. As a typical example of a strategic decision, the author explains a business case in the supply chain function of organizations. A section of the well-known SCOR model will be tested on digitalization characteristics. The research findings of the questionnaire will illustrate that the digitalization of supply chain processes is in the scope of managers and consultants, to reach higher efficiency by increased turnover or by reducing costs. The result of this paper verifies an improved decision process by usage of digital features, but the capability of the entire digital possibilities is not fully achieved yet
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Griffiths, Catherine. "Visual Tactics Toward an Ethical Debugging." Digital Culture & Society 4, no. 1 (March 1, 2018): 217–26. http://dx.doi.org/10.14361/dcs-2018-0113.
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Abstract To advance design research into a critical study of artificially intelligent algorithms, strategies from the fields of critical code studies and data visualisation are combined to propose a methodology for computational visualisation. By opening the algorithmic black box to think through the meaning created by structure and process, computational visualisation seeks to elucidate the complexity and obfuscation at the heart of artificial intelligence systems. There are rising ethical dilemmas that are a consequence of the use of machine learning algorithms in socially sensitive spaces, such as in determining criminal sentencing, job performance, or access to welfare. This is in part due to the lack of a theoretical framework to understand how and why decisions are made at the algorithmic level. The ethical implications are becoming more severe as such algorithmic decision-making is being given higher authority while there is a simultaneous blind spot in where and how biases arise. Computational visualisation, as a method, explores how contemporary visual design tactics including generative design and interaction design, can intersect with a critical exegesis of algorithms to challenge the black box and obfuscation of machine learning and work toward an ethical debugging of biases in such systems.
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Zweig, Katharina A., Georg Wenzelburger, and Tobias D. Krafft. "On Chances and Risks of Security Related Algorithmic Decision Making Systems." European Journal for Security Research 3, no. 2 (April 20, 2018): 181–203. http://dx.doi.org/10.1007/s41125-018-0031-2.
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Cundiff, W. E. "Database organisation: An algorithmic framework for decision support systems in APL." Decision Support Systems 5, no. 1 (March 1989): 57–64. http://dx.doi.org/10.1016/0167-9236(89)90028-6.
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Frank, Till D. "Decision-Making in Physical Intelligent Systems Regulated by Growth Rate Factors." Computer and Information Science 7, no. 4 (September 3, 2014): 55. http://dx.doi.org/10.5539/cis.v7n4p55.
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In the literature, self-organizing physical and chemical systems have been proposed as candidates for physical intelligent systems that may solve problems in the field of artificial intelligent in a non-algorithmic way that is not based on computation. In this theoretical study, decision-making in such physical intelligent systems is discussed in terms of non-equilibrium transitions between two self-organized states. The control parameter driving the non-equilibrium transitions is related to two growth rate factors. It is shown for a particular non-equilibrium system that the decision-making process satisfies the principle of selecting the state with the fastest growth rate factor. The system under consideration is a two component gas discharge system whose current flows can be described by means of an electronic blueprint.
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JAEGER, MANFRED. "PROBABILISTIC DECISION GRAPHS — COMBINING VERIFICATION AND AI TECHNIQUES FOR PROBABILISTIC INFERENCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, supp01 (January 2004): 19–42. http://dx.doi.org/10.1142/s0218488504002564.
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We adopt probabilistic decision graphs developed in the field of automated verification as a tool for probabilistic model representation and inference. We show that probabilistic inference has linear time complexity in the size of the probabilistic decision graph, that the smallest probabilistic decision graph for a given distribution is at most as large as the smallest junction tree for the same distribution, and that in some cases it can in fact be much smaller. Behind these very promising features of probabilistic decision graphs lies the fact that they integrate into a single coherent framework a number of representational and algorithmic optimizations developed for Bayesian networks (use of hidden variables, context-specific independence, structured representation of conditional probability tables).
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Lysaght, Tamra, Hannah Yeefen Lim, Vicki Xafis, and Kee Yuan Ngiam. "AI-Assisted Decision-making in Healthcare." Asian Bioethics Review 11, no. 3 (September 2019): 299–314. http://dx.doi.org/10.1007/s41649-019-00096-0.
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Abstract Artificial intelligence (AI) is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019). The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. (2019) in this special issue of the Asian Bioethics Review for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end of this paper.
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Marabelli, Marco, Sue Newell, and Valerie Handunge. "The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges." Journal of Strategic Information Systems 30, no. 3 (September 2021): 101683. http://dx.doi.org/10.1016/j.jsis.2021.101683.
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Uthamacumaran, A. "A Review of Complex Systems Approaches to Cancer Networks." Complex Systems 29, no. 4 (December 15, 2020): 779–835. http://dx.doi.org/10.25088/complexsystems.29.4.779.
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Cancers remain the leading cause of disease-related pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.
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Kuteynikov, D. L., O. A. Izhaev, S. S. Zenin, and V. A. Lebedev. "Algorithmic Transparency and Accountability: Legal Approaches to Solving the "Black Box" Problem." Lex Russica 73, no. 6 (June 26, 2020): 139–48. http://dx.doi.org/10.17803/1729-5920.2020.163.6.139-148.
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The paper examines the European and American legal approaches based on legislation regulating the use of computer algorithms, i.e. systems for automated decision-making of legally significant decisions. It is established that these jurisdictions apply essentially different concepts.The European approach provides for regulating the use of automated decision-making systems through legislation on personal data. The authors conclude that the general data protection regulation does not impose a legal obligation on the controllers to disclose technical information, i.e. to open a "black box", to the subject of personal data, in respect of which the algorithm makes a decision. This may happen in the future, when the legislative authorities specify the provisions of this Regulation, according to which the controller must provide the subject of personal data with meaningful information about the logic of decisions taken in relation to it.In the United States, issues of transparency and accountability of algorithms are regulated by various antidiscrimination acts that regulate certain areas of human activity. At the same time, they are fragmentary and their totality does not represent a complex, interconnected system of regulatory legal acts. In practice, legal regulation is carried out ad hoc with reference to certain legal provisions prohibiting the processing of sensitive types of personal data.The paper states that the legal regulation of algorithmic transparency and accountability is in its infancy in Russia. The existing legislation on personal data suggests that the domestic approach to solving the "black box" problem is close to the European one. When developing and adopting relevant regulatory legal acts, it is necessary to proceed from the fact that the subject of personal data should have the right to receive information explaining the logic of the decision made in relation to itin an accessible form.
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ATIG, MOHAMED FAOUZI, K. NARAYAN KUMAR, and PRAKASH SAIVASAN. "ADJACENT ORDERED MULTI-PUSHDOWN SYSTEMS." International Journal of Foundations of Computer Science 25, no. 08 (December 2014): 1083–96. http://dx.doi.org/10.1142/s0129054114400255.
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Multi-pushdown systems are formal models of multi-threaded programs. As they are Turing powerful in their full generality, several decidable subclasses, constituting under-approximations of the original system, have been studied in the recent years. Ordered Multi-Pushdown Systems (OMPDSs) impose an order on the stacks and limit pop actions to the lowest non-empty stack. The control state reachability for OMPDSs is 2-ETIME-COMPLETE. We propose a restriction on OMPDSs, called Adjacent OMPDSs (AOMPDS), where values may be pushed only on the lowest non-empty stack or one of its two neighbours. We describe EXPTIME decision procedures for reachability and LTL model-checking and establish matching lower bounds. We demonstrate the utility of this model as an algorithmic tool via optimal reductions from other models.
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Ertekin, Turgay. "The Efficacy and Superiority of the Expert Systems in Reservoir Engineering Decision Making Processes." Applied Sciences 11, no. 14 (July 9, 2021): 6347. http://dx.doi.org/10.3390/app11146347.
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In the process of making a critical decision in reservoir engineering, most of the time we find ourselves in a quandary. Like in any other scientific or technical field, when we find ourselves having to make a critical decision at a juncture, we cannot go ahead with our gut feelings, but rather must figure out what knowledge and information is lacking. In generating the missing knowledge and understanding, the depth and the rapid nature of the search will surface as two critical parameters. In other words, most of the time, a shallow search that can be conducted in a short period of time will not produce the missing information and the knowledge and more often, possibly, it will provide misguidance. When a large volume of sources of information is reviewed and the missing knowledge is generated using unbiased deductive methodologies, then, one can make an informed decision based on facts rather than intuition. In achieving such a desired result, it will be necessary to use fast algorithmic protocols to not sacrifice the wide nature of the search domain, to ensure that it is possible to generate the desired solution. In this paper, it is shown how in reservoir engineering desirable decisions can be reached in a timely manner choosing the most appealing course of action. It is true that in reservoir engineering applications, the decision-making process may involve a blend of intuition and scientific and rational thinking, critical factors such as blind spots, and the use of conventional methodologies that make decision-making hard to fully operationalize or to get a handle on. Luckily, there are mathematical and computational tools to ensure that scientists/engineers consistently make correct decisions, which include gathering as much information as possible and considering all possible alternatives (like combinatorial analysis protocols). The tool (model) proposed in this paper for making critical reservoir engineering decisions is a new computational platform/protocol that exploits the advantages of mathematically developed formulations and of the models that are based on the data/information collected. It is furthermore shown that the analyses conducted, and critical decisions reached, represent more thorough and far-reaching solutions that are structured using less computational overhead, thereby increasing the quality of the decision even further.
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Wright, Adam, Skye Aaron, Allison B. McCoy, Robert El-Kareh, Daniel Fort, Steven Z. Kassakian, Christopher A. Longhurst, et al. "Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements." Applied Clinical Informatics 12, no. 01 (January 2021): 182–89. http://dx.doi.org/10.1055/s-0041-1722918.
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Abstract Objective Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements. Methods Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors. Results Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback. Discussion An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations. Conclusion Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.
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Ajunwa, Ifeoma. "The “black box” at work." Big Data & Society 7, no. 2 (July 2020): 205395172096618. http://dx.doi.org/10.1177/2053951720938093.
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An oversized reliance on big data-driven algorithmic decision-making systems, coupled with a lack of critical inquiry regarding such systems, combine to create the paradoxical “black box” at work. The “black box” simultaneously demands a higher level of transparency from the worker in regard to data collection, while shrouding the decision-making in secrecy, making employer decisions even more opaque to the worker. To access employment, the worker is commanded to divulge highly personal information, and when hired, must submit further still to algorithmic processes of evaluations which will make authoritative claims as to the workers’ productivity. Furthermore, in and out of the workplace, the worker is governed by an invisible data-created leash deploying wearable technology to collect intimate worker data. At all stages, the worker is confronted with a lack of transparency, accountability, or explanation as to the inner workings or even the logic of the “black box” at work. This data revolution of the workplace is alarming for several reasons: (1) the “black box at work” not only serves to conceal disparities in hiring, but could also allow for a level of “data-laundering” that beggars any notion of equal opportunity in employment and (2) there exists, the danger of a “mission creep” attitude to data collection that allows for pervasive surveillance, contributing to the erosion of both the personhood and autonomy of workers. Thus, the “black box at work” not only enables worker domination in the workplace, it deprives the worker of Rawlsian justice.
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Prinsloo, Paul. "Of ‘black boxes’ and algorithmic decision-making in (higher) education – A commentary." Big Data & Society 7, no. 1 (January 2020): 205395172093399. http://dx.doi.org/10.1177/2053951720933994.
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Higher education institutions have access to higher volumes and a greater variety and granularity of student data, often in real-time, than ever before. As such, the collection, analysis and use of student data are increasingly crucial in operational and strategic planning, and in delivering appropriate and effective learning experiences to students. Student data – not only in what data is (not) collected, but also how the data is framed and used – has material and discursive effects, both permanent and fleeting. We have to critically engage claims that artificial intelligence and the ever expansive/expanding systems of algorithmic decision-making provide speedy, accessible, revealing, panoramic, prophetic and smart analyses of students' risks, potential and learning needs. We need to pry open the black boxes higher education institutions (and increasingly venture capital and learning management system providers) use to admit, steer, predict and prescribe students’ learning journeys.
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Niu, Yun Yun, and Zhi Gao Wang. "Semi-Uniform Solution for Common Algorithmic Problem by P System in the Minimally Parallel Mode." Applied Mechanics and Materials 568-570 (June 2014): 802–6. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.802.
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It is known that the Common Algorithmic Problem (CAP) has a nice property that several other NP-complete problems can be reduced to it in linear time. In the literature, the decision version of this problem can be efficiently solved with a family of recognizer P systems with active membranes with three electrical charges working in the maximally parallel way. We here work with a variant of P systems with active membranes that do not use polarizations and present a semi-uniform solution to CAP in the minimally parallel mode.
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Gillingham, Philip. "Decision Support Systems, Social Justice and Algorithmic Accountability in Social Work: A New Challenge." Practice 31, no. 4 (February 17, 2019): 277–90. http://dx.doi.org/10.1080/09503153.2019.1575954.
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Perez Vallejos, Elvira, Liz Dowthwaite, Helen Creswich, Virginia Portillo, Ansgar Koene, Marina Jirotka, Amy McCarthy, and Derek McAuley. "The impact of algorithmic decision-making processes on young people’s well-being." Health Informatics Journal 27, no. 1 (January 2021): 146045822097275. http://dx.doi.org/10.1177/1460458220972750.
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This study aims to capture the online experiences of young people when interacting with algorithm mediated systems and their impact on their well-being. We draw on qualitative (focus groups) and quantitative (survey) data from a total of 260 young people to bring their opinions to the forefront while eliciting discussions. The results of the study revealed the young people’s positive as well as negative experiences of using online platforms. Benefits such as convenience, entertainment and personalised search results were identified. However, the data also reveals participants’ concerns for their privacy, safety and trust when online, which can have a significant impact on their well-being. We conclude by recommending that online platforms acknowledge and enact on their responsibility to protect the privacy of their young users, recognising the significant developmental milestones that this group experience during these early years, and the impact that algorithm mediated systems may have on them. We argue that governments need to incorporate policies that require technologists and others to embed the safeguarding of users’ well-being within the core of the design of Internet products and services to improve the user experiences and psychological well-being of all, but especially those of children and young people.
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SCHRÖDER, LUTZ, and DIRK PATTINSON. "Modular algorithms for heterogeneous modal logics via multi-sorted coalgebra." Mathematical Structures in Computer Science 21, no. 2 (March 25, 2011): 235–66. http://dx.doi.org/10.1017/s0960129510000563.
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State-based systems and modal logics for reasoning about them often heterogeneously combine a number of features such as non-determinism and probabilities. In this paper, we show that the combination of features can be reflected algorithmically, and we develop modular decision procedures for heterogeneous modal logics. The modularity is achieved by formalising the underlying state-based systems as multi-sorted coalgebras and associating both a logical and algorithmic description with a number of basic building blocks. Our main result is that logics arising as combinations of these building blocks can be decided in polynomial space provided this is also the case for the components. By instantiating the general framework to concrete cases, we obtain PSpace decision procedures for a wide variety of structurally different logics, describing, for example, Segala systems and games with uncertain information.
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Mannor, Shie, and John N. Tsitsiklis. "Algorithmic aspects of mean–variance optimization in Markov decision processes." European Journal of Operational Research 231, no. 3 (December 2013): 645–53. http://dx.doi.org/10.1016/j.ejor.2013.06.019.
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Ewing, Gregory, and Ibrahim Demir. "An ethical decision-making framework with serious gaming: a smart water case study on flooding." Journal of Hydroinformatics 23, no. 3 (March 18, 2021): 466–82. http://dx.doi.org/10.2166/hydro.2021.097.
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Abstract Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, we propose the Water Ethics Web Engine (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision support. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community.
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Li, Li, and Amir Ghasemi. "IoT-Enabled Machine Learning for an Algorithmic Spectrum Decision Process." IEEE Internet of Things Journal 6, no. 2 (April 2019): 1911–19. http://dx.doi.org/10.1109/jiot.2018.2883490.
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Balayn, Agathe, Christoph Lofi, and Geert-Jan Houben. "Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems." VLDB Journal 30, no. 5 (May 5, 2021): 739–68. http://dx.doi.org/10.1007/s00778-021-00671-8.
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AbstractThe increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.
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Naghshvarianjahromi, Mahdi, Shiva Kumar, and M. Jamal Deen. "Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory." Applied Sciences 10, no. 3 (February 8, 2020): 1150. http://dx.doi.org/10.3390/app10031150.
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The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of ~7 dB and an enhancement in data rate efficiency ~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications.
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Ballamudi, Koteswara Rao. "Hybrid Automata: An Algorithmic Approach Behavioral Hybrid Systems." Asia Pacific Journal of Energy and Environment 6, no. 2 (December 31, 2019): 83–90. http://dx.doi.org/10.18034/apjee.v6i2.541.
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Hybrid automata strategies have advanced as a vital tool to design, check and direct the execution of hybrid systems. Any way they can – and we assume should – be utilized to communicate quantitative models about hybrid systems in different areas, for example, experimental sciences. Since the conventional design of hybrid automata compares well to consecutively integrate behavioral chains in living creatures, we look for a use of hybrid modeling procedures in the social sciences and, particularly, brain research. We attempt to address the question related to how human drivers move onto an expressway and simultaneously utilize this study as our test-bed for utilizing hybrid automata inside behavioral sciences. Hybrid automata give a language to displaying and exploring advanced and simple calculations in real-time systems. Hybrid automata are studied here from a dynamical systems point of view. Essential and adequate conditions for the presence and uniqueness of arrangements are inferred and a class of hybrid automata whose arrangements rely consistently upon the underlying state is described. The outcomes on presence, uniqueness, and progression fill in as a beginning stage for solid study. In this paper, we present the structure of hybrid automata as a model and detailed language for hybrid systems. Hybrid automata can be seen as a theory of timed automata, in which the behavior of factors is represented in each state by a bunch of differential conditions. We show that a large number of the models considered in the workshop can be characterized by hybrid automata. While the reachability issue is undecidable in any event, for extremely confined classes of hybrid automata, we present two semi-decision techniques for checking security properties of piecewise-straight hybrid automata, in which all factors change at steady rates. The two techniques are based, individually, on limiting and figuring fix points on for the most part endless state spaces. We show that if the end of the method, at that point they offer the right responses. We then show that for a significant number of the run of the mill workshop models, the strategies do end and hence give an algorithmic approach to confirming their properties.
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Park, Jong Hyuk. "Advanced IT-Based Future Sustainable Computing (2017–2018)." Sustainability 11, no. 8 (April 15, 2019): 2264. http://dx.doi.org/10.3390/su11082264.
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Future Sustainability Computing (FSC) is an emerging concept that holds various types of paradigms, rules, procedures, and policies to support breadth and length of the deployment of Information Technology (IT) for abundant life. However, advanced IT-based FCS is facing several sustainability problems in different information processing and computing environments. Solutions to these problems can call upon various computational and algorithmic frameworks that employ optimization, integration, generation, and utilization technique within cloud, mobile, and cluster computing, such as meta-heuristics, decision support systems, prediction and control, dynamical systems, machine learning, and so on. Therefore, this special issue deals with various software and hardware design, novel architectures and frameworks, specific mathematical models, and efficient modeling-simulation for advance IT-based FCS. We accepted eighteen articles in the six different IT dimensions: machine learning, blockchain, optimized resource provision, communication network, IT governance, and information security. All accepted articles contribute to the applications and research in the FCS, such as software and information processing, cloud storage organization, smart devices, efficient algorithmic information processing and distribution.
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Gravett, Willem. "Sentenced by an algorithm — Bias and lack of accuracy in risk-assessment software in the United States criminal justice system." South African Journal of Criminal Justice 34, no. 1 (2021): 31–54. http://dx.doi.org/10.47348/sacj/v34/i1a2.
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Developments in artificial intelligence and machine learning have caused governments to start outsourcing authority in performing public functions to machines. Indeed, algorithmic decision-making is becoming ubiquitous, from assigning credit scores to people, to identifying the best candidates for an employment position, to ranking applicants for admission to university. Apart from the broader social, ethical and legal considerations, controversies have arisen regarding the inaccuracy of AI systems and their bias against vulnerable populations. The growing use of automated risk-assessment software in criminal sentencing is a cause for both optimism and scepticism. While these tools could potentially increase sentencing accuracy and reduce the risk of human error and bias by providing evidence-based reasons in place of ‘ad-hoc’ decisions by human beings beset with cognitive and implicit biases, they also have the potential to reinforce and exacerbate existing biases, and to undermine certain of the basic constitutional guarantees embedded in the justice system. A 2016 decision in the United States, S v Loomis, exemplifies the threat that the unchecked and unrestrained outsourcing of public power to AI systems might undermine human rights and the rule of law.
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Levis, Tamari, Dagan Schwartz, and Yuval Bitan. "Triage Nurses Decision-Support Application Design." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 7, no. 1 (June 2018): 52–55. http://dx.doi.org/10.1177/2327857918071011.
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In this paper, we will present the planning and design process of a triage decision-support application, aimed to be used for both research data gathering and real-time triage decision-making. Triage is an initial classification of emergency department (ED) patients, according to the severity level of their medical condition. The need of fast and accurate triage decision-making, lead to the development of widely used triage algorithms, such as ESI (Emergency Severity Index). Observations and interviews with triage personnel exposed difficulties of triage process and helped us create an ESI-based decision making model. Next, we built a multiple-choice questioner to characterize the application and required features. 40 triage nurses completed the questioner. Results indicated that the most highly requested feature was an automated severity grade calculator, which became the core of the proposed design. While current design focuses on the analytical decision model, statistical analysis of the questioner results indicated that it is often insufficient when facing medical reality complexities, dictating nurse’s frequent use of intuition. Using triage systems data analysis and modern machine-learning methodologies, we inspire to develop a second version of the application that will integrate intuitive insights into triage scale algorithmic decision process.
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Pitchai, Arish, Reddy A. V., and Nickolas Savarimuthu. "Fuzzy based Quantum Genetic Algorithm for Project Team Formation." International Journal of Intelligent Information Technologies 12, no. 1 (January 2016): 31–46. http://dx.doi.org/10.4018/ijiit.2016010102.
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Formation of an effective project team plays an important role in successful completion of the projects in organizations. As the computation involved in this task grows exponentially with the growth in the size of personnel, manual implementation is of no use. Decision support systems (DSS) developed by specialized consultants help large organizations in personnel selection process. Since, the given problem can be modelled as a combinatorial optimization problem, Genetic Algorithmic approach is preferred in building the decision making software. Fuzzy descriptors are being used to facilitate the flexible requirement specifications that indicates required team member skills. The Quantum Walk based Genetic Algorithm (QWGA) is proposed in this paper to identify near optimal teams that optimizes the fuzzy criteria obtained from the initial team requirements. Efficiency of the proposed design is tested on a variety of artificially constructed instances. The results prove that the proposed optimization algorithm is practical and effective.
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Tikhanychev, Oleg V. "On the problem of information support of decision-making automation." Journal Of Applied Informatics 15, no. 89 (October 30, 2020): 62–72. http://dx.doi.org/10.37791/2687-0649-2020-15-5-62-72.
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For computer programs need a formalized information. As is known, automated control systems work only with formal information. The basis for the formalization of a system of information classification and coding. The use of formalized data allows users to work with software from the automated control systems. By increasing the size of the control systems and the number of used software, there is a problem of organization of information exchange. Normative documents recommend organizing information interaction through communication protocols. However, with the growing size of the control systems there is the problem of maintaining these protocols to date. Within the framework of the currently used "hard" coding system to solve the problem of updating the information exchange is not possible. The aim of the study is to find ways to solve the problem of interoperability based on other principles other than organizational ones. To solve this problem, the article formulates the formulation of the problem of ensuring interoperability in distributed multi-agent environments based on the methods of "fuzzy" classification and coding. To solve this problem, it is proposed to use in the automated decision support systems fundamentally new information and linguistic support on the basis of the methodology of "fuzzy classification". Practice shows the possibility of using algorithmic formalization methods in a cluster of systems that are not too critical to the input information. In the future, with the development of technology, the scope of the proposed approach can be expanded. In the future, this will make it possible to move from the organization of a potentially effective, but difficult to implement in practice, “Single Information Space” to a “unified information field”.
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Meacham, Darian, and Francesco Tava. "The Algorithmic Disruption of Workplace Solidarity." Philosophy Today 65, no. 3 (2021): 571–98. http://dx.doi.org/10.5840/philtoday2021519408.
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This paper examines the development and technological mediation of the concept of solidarity. We focus on the workplace as a focal point of solidarity relations, and utilise a phenomenological approach to describe and analyse those relations. Workplace solidarity, which has been historically concretised through social objects such as labor unions, is of particular political relevance since it has played an outsize role in the broader struggle for social, economic, and political rights, recognition, and equality. We argue that the use of automated decision support systems (ADS) in labor process management may negatively affect the formation of these relations. As solidarity motivates collective political action and risk-taking, the mediation and potential obstruction of solidarity relations by ADS is politically significant. We contribute to the growing literature on the “future of work” problem in elucidating the technological mediation of workplace solidarity.
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Labini, Paolo Sylos, Marco Cianfriglia, Damiano Perri, Osvaldo Gervasi, Grigori Fursin, Anton Lokhmotov, Cedric Nugteren, Bruno Carpentieri, Fabiana Zollo, and Flavio Vella. "On the Anatomy of Predictive Models for Accelerating GPU Convolution Kernels and Beyond." ACM Transactions on Architecture and Code Optimization 18, no. 1 (January 21, 2021): 1–24. http://dx.doi.org/10.1145/3434402.
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Efficient HPC libraries often expose multiple tunable parameters, algorithmic implementations, or a combination of them, to provide optimized routines. The optimal parameters and algorithmic choices may depend on input properties such as the shapes of the matrices involved in the operation. Traditionally, these parameters are manually tuned or set by auto-tuners. In emerging applications such as deep learning, this approach is not effective across the wide range of inputs and architectures used in practice. In this work, we analyze different machine learning techniques and predictive models to accelerate the convolution operator and GEMM. Moreover, we address the problem of dataset generation, and we study the performance, accuracy, and generalization ability of the models. Our insights allow us to improve the performance of computationally expensive deep learning primitives on high-end GPUs as well as low-power embedded GPU architectures on three different libraries. Experimental results show significant improvement in the target applications from 50% up to 300% compared to auto-tuned and high-optimized vendor-based heuristics by using simple decision tree- and MLP-based models.
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Bărbat, Boldur E. "DOMINO: Trivalent Logic Semantics in Bivalent Syntax Clothes." International Journal of Computers Communications & Control 2, no. 4 (April 1, 2007): 303. http://dx.doi.org/10.15837/ijccc.2007.3.2362.
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The paper describes a rather general software mechanism developed primarily for decision making in dynamic and uncertain environments (typical application: managing overbooking). DOMINO (Decision-Oriented Mechanism for "IF" as Non-deterministic Operator) is meant to deal with undecidability due to any kind of future contingents. Its description here is self-contained but, since a validation is underway within a much broader undertaking involving agent-oriented software, to impair redundancy, several aspects explained in very recent papers are here abridged. In essence, DOMINO acts as an "IF" with enhanced semantics: it can answer "YES", "NO" or "UNDECIDABLE in the time span given" (it renders control to an exception handler). Despite its trivalent logic semantics, it respects the rigours of structural programming and the syntax of bivalent logic (it is programmed in plain C++ to be applicable to legacy systems too). As for most novel approaches, expectations are high, involving a less algorithmic, less probabilistic, less difficult to understand method to treat undecidability in dynamic and uncertain environments, where postponing decisions means keeping open alternatives (to react better to rapid environment changes).
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Matveev, Stanislav A., Evgeny B. Korotkov, Yuri A. Zhukov, Nikita S. Slobodzian, Mikhail I. Nadezhin, Andrei V. Gorbunov, and Leonid T. Tanklevskiy. "Diagnostic and Monitoring System for Technical Condition of Electromechanical Section of Thermal Control Systems in Spacecraft." International Journal of Mathematical, Engineering and Management Sciences 5, no. 1 (November 1, 2019): 181–92. http://dx.doi.org/10.33889/ijmems.2020.5.1.015.
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Modern diagnostics methods ensuring the safety of production and operation, as well as the improvement of functional characteristics of electromechanical systems’ are discussed, method of diagnostics according to the spectrum and hodograph of the engine’s equivalent current is presented. Functional concept is presented for the system of control, diagnostic and monitoring of technical condition of thermal control systems’ electromechanical part in the spacecraft. The decision-making and forecasting algorithm for the operational resource is based on ground-based studies and diagnostic results. This approach to device diagnostics and monitoring is also used in other servo drives, mechatronic and robotic systems of space vehicles and other objects that are inaccessible and left unattended. Hardware-algorithmic implementation of the system is described, recommendations on the components base selection are given.
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Leitsch, Alexander. "Deciding Clause Classes by Semantic Clash Resolution." Fundamenta Informaticae 18, no. 2-4 (April 1, 1993): 163–82. http://dx.doi.org/10.3233/fi-1993-182-406.
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It is investigated, how semantic clash resolution can be used to decide some classes of clause sets. Because semantic clash resolution is complete, the termination of the resolution procedure on a class Γ gives a decision procedure for Γ. Besides generalizing earlier results we investigate the relation between termination and clause complexity. For this purpose we define the general concept of atom complexity measure and show some general results about termination in terms of such measures. Moreover, rather than using fixed resolution refinements we define an algorithmic generator for decision procedures, which constructs appropriate semantic refinements out of the syntactical structure of the clause sets. This method is applied to the Bernays – Schönfinkel class, where it gives an efficient (resolution) decision procedure.
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Webb, Helena, Menisha Patel, Michael Rovatsos, Alan Davoust, Sofia Ceppi, Ansgar Koene, Liz Dowthwaite, Virginia Portillo, Marina Jirotka, and Monica Cano. "“It would be pretty immoral to choose a random algorithm”." Journal of Information, Communication and Ethics in Society 17, no. 2 (May 13, 2019): 210–28. http://dx.doi.org/10.1108/jices-11-2018-0092.
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Purpose The purpose of this paper is to report on empirical work conducted to open up algorithmic interpretability and transparency. In recent years, significant concerns have arisen regarding the increasing pervasiveness of algorithms and the impact of automated decision-making in our lives. Particularly problematic is the lack of transparency surrounding the development of these algorithmic systems and their use. It is often suggested that to make algorithms more fair, they should be made more transparent, but exactly how this can be achieved remains unclear. Design/methodology/approach An empirical study was conducted to begin unpacking issues around algorithmic interpretability and transparency. The study involved discussion-based experiments centred around a limited resource allocation scenario which required participants to select their most and least preferred algorithms in a particular context. In addition to collecting quantitative data about preferences, qualitative data captured participants’ expressed reasoning behind their selections. Findings Even when provided with the same information about the scenario, participants made different algorithm preference selections and rationalised their selections differently. The study results revealed diversity in participant responses but consistency in the emphasis they placed on normative concerns and the importance of context when accounting for their selections. The issues raised by participants as important to their selections resonate closely with values that have come to the fore in current debates over algorithm prevalence. Originality/value This work developed a novel empirical approach that demonstrates the value in pursuing algorithmic interpretability and transparency while also highlighting the complexities surrounding their accomplishment.
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Li, Fan, and Yuan Lu. "ENGAGING END USERS IN AN AI-ENABLED SMART SERVICE DESIGN - THE APPLICATION OF THE SMART SERVICE BLUEPRINT SCAPE (SSBS) FRAMEWORK." Proceedings of the Design Society 1 (July 27, 2021): 1363–72. http://dx.doi.org/10.1017/pds.2021.136.
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AbstractArtificial Intelligence (AI) has expanded in a diverse context, it infiltrates our social lives and is a critical part of algorithmic decision-making. Adopting AI technology, especially AI-enabled design, by end users who are non-AI experts is still limited. The incomprehensible, untransparent decision-making and difficulty of using AI become obstacles which prevent these end users to adopt AI technology. How to design the user experience (UX) based on AI technologies is an interesting topic to explore.This paper investigates how non-AI-expert end users can be engaged in the design process of an AI-enabled application by using a framework called Smart Service Blueprint Scape (SSBS), which aims to establish a bridge between UX and AI systems by mapping and translating AI decisions based on UX. A Dutch mobility service called ‘stUmobiel ’ was taken as a design case study. The goal is to design a reservation platform with stUmobiel end users. Co-creating with case users and assuring them to understand the decision-making and service provisional process of the AI-enabled design is crucial to promote users’ adoption. Furthermore, the concern of AI ethics also arises in the design process and should be discussed in a broader sense.
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Waack, Stephan. "On the Descriptive and Algorithmic Power of Parity Ordered Binary Decision Diagrams." Information and Computation 166, no. 1 (April 2001): 61–70. http://dx.doi.org/10.1006/inco.2000.3022.
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Carvalho, Diogo V., Eduardo M. Pereira, and Jaime S. Cardoso. "Machine Learning Interpretability: A Survey on Methods and Metrics." Electronics 8, no. 8 (July 26, 2019): 832. http://dx.doi.org/10.3390/electronics8080832.
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Machine learning systems are becoming increasingly ubiquitous. These systems’s adoption has been expanding, accelerating the shift towards a more algorithmic society, meaning that algorithmically informed decisions have greater potential for significant social impact. However, most of these accurate decision support systems remain complex black boxes, meaning their internal logic and inner workings are hidden to the user and even experts cannot fully understand the rationale behind their predictions. Moreover, new regulations and highly regulated domains have made the audit and verifiability of decisions mandatory, increasing the demand for the ability to question, understand, and trust machine learning systems, for which interpretability is indispensable. The research community has recognized this interpretability problem and focused on developing both interpretable models and explanation methods over the past few years. However, the emergence of these methods shows there is no consensus on how to assess the explanation quality. Which are the most suitable metrics to assess the quality of an explanation? The aim of this article is to provide a review of the current state of the research field on machine learning interpretability while focusing on the societal impact and on the developed methods and metrics. Furthermore, a complete literature review is presented in order to identify future directions of work on this field.