This commentary tests a methodology proposed by Munk et al. (2022) for using failed predictions in machine learning as a method to identify ambiguous and rich cases for qualitative analysis. Using a dataset describing actions performed by fictional characters interacting with machine vision technologies in 500 artworks, movies, novels and videogames, I trained a simple machine learning algorithm (using the kNN algorithm in R) to predict whether or not an action was active or passive using only information about the fictional (...) characters. Predictable actions were generally unemotional and unambiguous activities where machine vision technologies were treated as simple tools. Unpredictable actions, that is, actions that the algorithm could not correctly predict, were more ambivalent and emotionally loaded, with more complex power relationships between characters and technologies. The results thus support Munk et al.'s theory that failed predictions can be productively used to identify rich cases for qualitative analysis. This test goes beyond simply replicating Munk et al.'s results by demonstrating that the method can be applied to a broader humanities domain, and that it does not require complex neural networks but can also work with a simpler machine learning algorithm. Further research is needed to develop an understanding of what kinds of data the method is useful for and which kinds of machine learning are most generative. To support this, the R code required to produce the results is included so the test can be replicated. The code can also be reused or adapted to test the method on other datasets. (shrink)

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. (shrink)

The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one’s position in a future accident. We contend, (...) however, that both contractarian approaches and harm minimisation standards are flawed, due to a failure to account for the fundamental difference between those ‘involved’ and ‘uninvolved’ in an impending crash. Drawing from classical works on the trolley problem, we show how this notion can be substantiated by reference to either the distinction between negative and positive rights, or to differences in people’s claims. By supplementing harm minimisation with corresponding constraints, we can develop crash algorithms for autonomous cars which are both ethically adequate and promise to overcome certain significant practical barriers to implementation. (shrink)

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 be ineffective in the (...) context of AI. This paper examines the possibility of studying algorithmic discrimination from the perspective of negligence law. Negligent selection and validation of datasets and classifiers is a salient cause of improper training in machine learning. It is a breach of an employer’s duty to protect others, a failure of its exercise of due care in the manner of choosing employees. Adopting negligence to algorithmic discrimination requires a reformulation of what is foreseeable and reasonable within the context of black box algorithms, and a shift in the burden of proof to the employer, who must present evidence of its efforts to avoid bias. (shrink)

During the past decade, a small but rapidly growing number of Law&Tech scholars have been applying algorithmic methods in their legal research. This Article does it too, for the sake of saving disclosure regulation failure: a normative strategy that has long been considered dead by legal scholars, but conspicuously abused by rule-makers. Existing proposals to revive disclosure duties, however, either focus on the industry policies (e.g. seeking to reduce consumers’ costs of reading) or on rulemaking (e.g. by simplifying (...) linguistic intricacies). But failure may well depend on both. Therefore, this Article develops a `comprehensive approach', suggesting to use computational tools to cope with linguistic and behavioral failures at both the enactment and implementation phases of disclosure duties, thus filling a void in the Law & Tech scholarship. Specifically, it outlines how algorithmic tools can be used in a holistic manner to address the many failures of disclosures from the rulemaking in parliament to consumer screens. It suggests a multi-layered design where lawmakers deploy three tools in order to produce optimal disclosure rules: machine learning, natural language processing, and behavioral experimentation through regulatory sandboxes. To clarify how and why these tasks should be performed, disclosures in the contexts of online contract terms and privacy online are taken as examples. Because algorithmic rulemaking is frequently met with well-justified skepticism, problems of its compatibility with legitimacy, efficacy and proportionality are also discussed. (shrink)

In several experiments of survival analysis, the cause of death or failure of any subject may be characterized by more than one cause. Since the cause of failure may be dependent or independent, in this work, we discuss the competing risk lifetime model under progressive type-II censored where the removal follows a binomial distribution. We consider the Akshaya lifetime failure model under independent causes and the number of subjects removed at every failure time when the removal (...) follows the binomial distribution with known parameters. The classical and Bayesian approaches are used to account for the point and interval estimation procedures for parameters and parametric functions. The Bayes estimate is obtained by using the Markov Chain Monte Carlo method under symmetric and asymmetric loss functions. We apply the Metropolis–Hasting algorithm to generate MCMC samples from the posterior density function. A simulated data set is applied to diagnose the performance of the two techniques applied here. The data represented the survival times of mice kept in a conventional germ-free environment, all of which were exposed to a fixed dose of radiation at the age of 5 to 6 weeks, which was used as a practice for the model discussed. There are 3 causes of death. In group 1, we considered thymic lymphoma to be the first cause and other causes to be the second. On the base of mice data, the survival mean time of mice of the second cause is higher than the first cause. (shrink)

There have been many efforts to infer causation from association byusing statistical models. Algorithms for automating this processare a more recent innovation. In Humphreys and Freedman[(1996) British Journal for the Philosophy of Science 47, 113–123] we showed that one such approach, by Spirtes et al., was fatally flawed. Here we put our arguments in a broader context and reply to Korb and Wallace [(1997) British Journal for thePhilosophy of Science 48, 543–553] and to Spirtes et al.[(1997) British Journal for the (...) Philosophy of Science 48, 555–568]. Their arguments leave our position unchanged: claims to have developed a rigorous engine for inferring causation from association are premature at best, the theorems have no implications for samples of any realistic size, and the examples used to illustrate the algorithms are indicative of failure rather than success. The gap between association and causation has yet to be bridged. (shrink)

Heidegger argues that modern technology is quantifiably different from all earlier periods because of a shift in ethos from in situ craftwork to globalised production and storage at the behest of consumerism. He argues that this shift in technology has fundamentally shaped our epistemology, and it is almost impossible to comprehend anything outside the technological enframing of knowledge. The exception is when something breaks down, and the fault ‘shows up’ in fresh ways. Stiegler has several important addendums to Heidegger’s thesis. (...) Firstly, that Heidegger fails to fully appreciate the early Greek myth of Prometheus, and the technological depth that fire offers all human societies. Secondly, the fall, or failure, which is doubled in the myth of Prometheus, and shows up in epistemology accordingly. Thirdly the acceleration of technology since the onset of Information Technology, and the way this is disorientating our Being. I argue the fall in both Heidegger and early Stiegler has encaptured their imagination. In later work, Stiegler argues that acceleration is fused with algorithmic pretensions, that are distorting and undermining the creative political imaginary, and making it difficult to revalue the values that underpin the nihilist Anthropocene. The overcoming of the Anthropocene engages politics, economics, power, physics, and ecology. (shrink)

Automatic Generation Control in modern power systems is getting complex, due to intermittency in the output power of multiple sources along with considerable digressions in the loads and system parameters. To address this problem, this paper proposes an approach to calculate Power System Restoration Indices of a 2-area thermal-hydro restructured power system. This study also highlights the necessary ancillary service requirements for the system under a deregulated environment to cater to large-scale power failures and entire system outages. An abrupt change (...) in consumer load demands and disturbances in any control region of a multiarea system causes severe fluctuations in frequency and interarea power exchanges. However, simple Proportional and Integral controllers are most prevalent in the literature to effectively resolve AGC issues, while its integral gain is smaller due to the larger overshoot in transient performance. Therefore, an attempt has been made with a novel control strategy, known as the pseudoderivative feedforward with feedback controller, is developed to keep the interarea power exchanges and the frequency to the specified limits after load changes. A PDFF controller is designed and implemented using the flower pollination algorithm to obtain optimal dynamic performance for different types of potential power flows in a restructured power system under investigation. The proposed PDFF controller localizes the zero at an optimal place that reduces the rise time of the step-response to reduce the excessive overshoot and gives much better dynamic performances as compared to the PI control structure. The Integral Square Error is considered as a performance criterion to derive the optimized gain of the PDFF control structure using FPA. Different PSRI are computed based on the transient response of the 2-area deregulated multisource system and different restoration measures to be taken are also discussed. The simulation results clearly show that the proposed approach is very powerful in decreasing the frequency and tie-power digressions under different load perturbations. (shrink)

This paper concerns the role of the unconscious in technology. The central thesis is that there exists an experience of non-acceptance and failed incorporation of technology, which (a) does not depend on the technical engineering dimension of the artifact but (b) instead concerns the relationship between the human unconscious and the artifact. This thesis is supported and developed through the analysis of a case study – that is, the creation and development of the first version of Google Glass. The (...) class='Hi'>failure of the first version of Google Glass is explained in terms of the non-acceptance of this technology. This paper presents an analysis of users’ experiences and comments as a basis for interpreting the relationship between the unconscious and technology. Their non-acceptance of this technology is explained and further clarified from a postphenomenological point of view, utilizing the concept of “technological uncanny.” The analysis suggests that antimediation can be understood as a form of noise. In fact, as non-acceptance, antimediation directly concerns the relationship between contingency and control. (shrink)

This article aims to make a distinction among contemporary artworks that use algorithmic technologies. Departing from Science, Technology and Society (STS) studies, the focus is given to artworks that use algorithmic agencies, as assemblages of human and nonhuman entities. Making an ethno-methodological analysis of three artworks, The Pitiful Story of Deniz Yılmaz (2015–present) of Bager Akbay, Artificial Intelligence for Governance, the Kitty AI (2016) of Pınar Yoldaş and Plantoid (2015) of Primavera Di Filippi, the article examines paratactical use (...) of algorithmic agencies in contemporary artistic practice. Exploring, revealing and learning from the tactics used by platforms, corporations and institutions that control and govern, paratactical use of algorithmic agencies generates a self-reflexive perspective on such tactics of control. Paratactical works also explore absurdities and contingencies embedded in algorithmic technologies. They work within failures and errors, and perform subversive ways of thinking, acting and enacting. In conclusion, paratactical works deal with the reproduction, distribution and normalization of ignorance, extinction, degeneration, corruption and destruction. (shrink)

A recent approach to the problem of logical omniscience holds that belief is question-sensitive: what an agent believes depends on what question they try to answer (Pérez Carballo, 2016; Yalcin, 2018; Hoek, 2022). While the question-sensitive approach can avoid some logical omniscience problems, we argue that it suffers from nearby problems. First, these accounts all validate closure principles that are just as implausible as the ones it was designed to avoid. Second, question-sensitivity by itself isn’t suitable for explaining many kinds (...) of failures of logical omniscience. Recognizing the flaws of this approach, however, naturally leads to a more promising solution. Our account generalizes the question-sensitive approach by appealing to (1) the defeasible nature of dispositions toward action associated with belief and (2) the algorithms an agent uses to make decisions. On our view, then, believing that φ means being disposed to employ an algorithm which outputs acting on the information that φ in normal circumstances associated with φ. We argue that this account naturally generalizes the question-sensitive accounts while avoiding their faults. (shrink)

We examine the performance of some standard causal discovery algorithms, both constraint-based and score-based, from the perspective of how robust they are against failures of the Causal Faithfulness Assumption. For this purpose, we make only the so-called Triangle-Faithfulness assumption, which is a fairly weak consequence of the Faithfulness assumption, and otherwise allows unfaithful distributions. In particular, we allow violations of Adjacency-Faithfulness and Orientation-Faithfulness. We show that the PC algorithm, a representative constraint-based method, can be made more robust against unfaithfulness by (...) incorporating elements of the GES algorithm, a representative score-based method; similarly, the GES algorithm can be made less error-prone by incorporating elements of the conservative PC algorithm. As our simulations demonstrate, the increased robustness seems to matter even when faithfulness is not exactly violated, for with only finite sample, distributions that are not exactly unfaithful may be sufficiently close to being unfaithful to make trouble. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the (...) current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)

The paper presents different clustering approaches in legal judgments from the Special Civil Court located at the Federal University of Santa Catarina. The subject is Consumer Law, specifically cases in which consumers claim moral and material compensation from airlines for service failures. To identify patterns from the dataset, we apply four types of clustering algorithms: Hierarchical and Lingo, K-means and Affinity Propagation. We evaluate the results based on the following criteria: entropy and purity; algorithm's ability in providing labels; legal expert’s (...) evaluation; and experimental complexity. The results demonstrate that the most advantageous approach is Hierarchical Clustering, since it has the best entropy and purity numbers, as well as the least difficulty for the expert to analyze the clusters, and the least experimental complexity. The main contribution of the paper is to show the advantages and disadvantages of each approach, especially to identify labels in unstructured and non-indexed legal texts. (shrink)

Qualitative researchers, particularly those researching business and society topics, should embrace artificial intelligence (AI) to conduct efficient, explicatory, and equitable research but also exercise caution to avoid its pitfalls.

Artificial intelligence holds great promise for improved health‐care outcomes. But it also poses substantial new hazards, including algorithmic discrimination. For example, an algorithm used to identify candidates for beneficial “high risk care management” programs routinely failed to select racial minorities. Furthermore, some algorithms deliberately adjust for race in ways that divert resources away from minority patients. To illustrate, algorithms have underestimated African Americans’ risks of kidney stones and death from heart failure. Algorithmic discrimination can violate Title VI (...) of the Civil Rights Act and Section 1557 of the Affordable Care Act when it unjustifiably disadvantages underserved populations. This article urges that both legal and technical tools be deployed to promote AI fairness. Plaintiffs should be able to assert disparate impact claims in health‐care litigation, and Congress should enact an Algorithmic Accountability Act. In addition, fairness should be a key element in designing, implementing, validating, and employing AI. (shrink)

The input data to grammar learning algorithms often consist of overt forms that do not contain full structural descriptions. This lack of information may contribute to the failure of learning. Past work on Optimality Theory introduced Robust Interpretive Parsing (RIP) as a partial solution to this problem. We generalize RIP and suggest replacing the winner candidate with a weighted mean violation of the potential winner candidates. A Boltzmann distribution is introduced on the winner set, and the distribution’s parameter $T$ (...) is gradually decreased. Finally, we show that GRIP, the Generalized Robust Interpretive Parsing Algorithm significantly improves the learning success rate in a model with standard constraints for metrical stress assignment. (shrink)

Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model (...) simulations are fundamental for their philosophical assessment. I argue that simulation models can be distinguished from other forms of models by the many algorithmic structures, representation relations, and new semantic connections involved in their architecture. In this article, I reconstruct a general architecture for a simulation model, one that faithfully captures the complexities involved in most scientific research with computer simulations. Furthermore, I submit that a new methodology capable of conforming such architecture into a fully functional, computationally tractable computer simulation must be in place. I discuss this methodology—what I call recasting—and argue for its philosophical novelty. If these efforts are heading towards the right interpretation of simulation models, then one can show that computer simulations shed new light on the philosophy of science. To illustrate the potential of my interpretation of simulation models, I briefly discuss simulation-based explanations as a novel approach to questions about scientific explanation. (shrink)

The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft. Although there is a great deal of value in their work, I will argue that, for familiar philosophical reasons, their methodology, ‘Black-box Interpretability’ is wrongheaded. But there is a better way. There is an exciting and emerging discipline of ‘Inner Interpretability’ (also sometimes called ‘White-box Interpretability’) that aims to uncover the internal activations and weights of models in order (...) to understand what they represent and the algorithms they implement. In my view, a crucial mistake in Black-box Interpretability is the failure to appreciate that how processes are carried out matters when it comes to intelligence and understanding. I can’t pretend to have a full story that provides both necessary and sufficient conditions for being intelligent, but I do think that Inner Interpretability dovetails nicely with plausible philosophical views of what intelligence requires. So the conclusion is modest, but the important point in my view is seeing how to get the research on the right track. Towards the end of the paper, I will show how some of the philosophical concepts can be used to further refine how Inner Interpretability is approached, so the paper helps draw out a profitable, future two-way exchange between Philosophers and Computer Scientists. (shrink)

Journalism and media studies lack robust theoretical concepts for studying journalistic knowledge ‎generation. More specifically, conceptual challenges attend the emergence of big data and ‎algorithmic sources of journalistic knowledge. A family of frameworks apt to this challenge is ‎provided by “social epistemology”: a young philosophical field which regards society’s participation ‎in knowledge generation as inevitable. Social epistemology offers the best of both worlds for ‎journalists and media scholars: a thorough familiarity with biases and failures of obtaining ‎knowledge, and a (...) strong orientation toward best practices in the realm of knowledge-acquisition ‎and truth-seeking. This paper articulates the lessons of social epistemology for two central nodes of ‎knowledge-acquisition in contemporary journalism: human-mediated knowledge and technology-‎mediated knowledge. ‎. (shrink)

As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A subdiscipline of computer science known as interpretable machine learning (IML) has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three conceptual challenges (...) that are largely overlooked by authors in this area. I argue that the vast majority of IML algorithms are plagued by (1) ambiguity with respect to their true target; (2) a disregard for error rates and severe testing; and (3) an emphasis on product over process. Each point is developed at length, drawing on relevant debates in epistemology and philosophy of science. Examples and counterexamples from IML are considered, demonstrating how failure to acknowledge these problems can result in counterintuitive and potentially misleading explanations. Without greater care for the conceptual foundations of IML, future work in this area is doomed to repeat the same mistakes. (shrink)

According to Clifford Geertz, the purpose of anthropology is not to explain culture but to explicate it. That should cause us to rethink our relationship with machine learning. It is, we contend, perfectly possible that machine learning algorithms, which are unable to explain, and could even be unexplainable themselves, can still be of critical use in a process of explication. Thus, we report on an experiment with anthropological AI. From a dataset of 175K Facebook comments, we trained a neural network (...) to predict the emoji reaction associated with a comment and asked a group of human players to compete against the machine. We show that a) the machine can reach the same accuracy as the players, b) it fails in roughly the same ways as the players, and c) easily predictable emoji reactions tend to reflect unambiguous situations where interpretation is easy. We therefore repurpose the failures of the neural network to point us to deeper and more ambiguous situations where interpretation is hard and explication becomes both necessary and interesting. We use this experiment as a point of departure for discussing how experiences from anthropology, and in particular the tension between formalist ethnoscience and interpretive thick description, might contribute to debates about explainable AI. (shrink)

As a promising method with excellent characteristics in terms of resilience and dependability, distributed methods are gradually used in the field of energy management of microgrid. However, these methods have more stringent requirements on the working conditions, which will make the system more sensitive to communication failures and cyberattacks. As a result, it is both theoretical merits and practical values to investigate the malicious effect of cyber attacks on microgrid. This paper studies the distributed economic dispatch problem under denial-of-service attacks (...) for the microgrid, in which each generator can communicate with its neighbors and has the computational capability to implement local operation. Firstly, a DoS attack model is proposed, in which the DoS attacker intentionally jams the communication channel to deteriorate the performance of the microgrid. Then, the evolution mechanism of the dispatch system of the microgrid under different attack scenarios is adequately discussed. On this basis, an optimal attack strategy based on enumerating-search algorithm is presented to allocate the limited attack resources reasonably, so as to maximize the effect of DoS attacks. Finally, the validity of the theoretical studies about the attack effect under different scenarios and the effectiveness of the proposed enumerating-search-based optimal attack strategy are illustrated through the simulation examples on the IEEE 57-bus system and IEEE 39-bus system, respectively. (shrink)

This paper provides an in-depth analysis and discussion of the key issues of quality management, evaluation, and detection contained in big data for automotive application scenarios. A generalized big data quality management model and programming framework are proposed, and a series of data quality detection and repair interfaces are built to express the processing semantics of various data quality issues. Through this data quality management model and detection and repair interfaces, users can quickly build custom data quality detection and repair (...) tasks for different data quality requirements. To improve the operational efficiency of complex data quality management algorithms in large-scale data scenarios, corresponding parallelization algorithms are studied and implemented for detection and repair algorithms with long computation time, including priority-based multiconditional function-dependent detection and repair algorithms, entity detection, and extraction algorithms based on semantic information and chunking techniques, and plain Bayesian-based missing value filling algorithms, and this paper proposes a data validity evaluation algorithm and enhances the validity of the original data in practical applications by adding temporal weights, and finally it passed the experimental validation. Through the comprehensive detection process of data importance, network busyness, duration of transmission process, and failure situation, the efficiency has been increased by 20%, and an adaptive data integrity detection method based on random algorithm and encryption algorithm is designed. After experimental verification, this method can effectively detect the integrity of the data transmission process and improve the application of data value, and the final effect is increased by 30.5%. (shrink)

Many of our concepts are introduced to us via, and seem only to be constrained by, roughand-ready explanations and some sample paradigm positive and negative applications. This happens even in informal logic and mathematics. Yet in some cases, the concepts in question – although only informally and vaguely characterized – in fact have, or appear to have, entirely determinate extensions. Here’s one familiar example. When we start learning computability theory, we are introduced to the idea of an algorithmically computable function (...) (from numbers to numbers) – i.e. one whose value for any given input can be determined by a step-by-step calculating procedure, where each step is fully determined by some antecedently given finite set of calculating rules. We are told that we are to abstract from practical considerations of how many steps will be needed and how much ink will be spilt in the process, so long as everything remains finite. We are also told that each step is to be ‘small’ and the rules governing it must be ‘simple’, available to a cognitively limited calculating agent: for we want an algorithmic procedure, step-by-minimal-step, to be idiot-proof. For a classic elucidation of this kind, see e.g. Rogers (1967, pp. 1–5). Church’s Thesis, in one form, then claims this informally explicated concept in fact has a perfectly precise extension, the set of recursive functions. Church’s Thesis can be supported in a quasi-empirical way, by the failure of our searches for counterexamples. It can be supported too in a more principled way, by the observation that different appealing ways of sharpening up the informal chararacterization of algorithmic computability end up specifying the same set of recursive functions. But such considerations fall short of a demonstration of the Thesis. So is there a different argumentative strategy we could use, one that could lead to a proof? Sometimes it is claimed that there just can’t be, because you can never really prove results involving an informal concept like algorithmic computability.. (shrink)

Objectives Medical futility at the end of life is a growing challenge to medicine. The goals of the authors were to elucidate how clinicians define futility, when they perceive life-sustaining treatment (LST) to be futile, how they communicate this situation and why LST is sometimes continued despite being recognised as futile. Methods The authors reviewed ethics case consultation protocols and conducted semi-structured interviews with 18 physicians and 11 nurses from adult intensive and palliative care units at a tertiary hospital in (...) Germany. The transcripts were subjected to qualitative content analysis. Results Futility was identified in the majority of case consultations. Interviewees associated futility with the failure to achieve goals of care that offer a benefit to the patient's quality of life and are proportionate to the risks, harms and costs. Prototypic examples mentioned are situations of irreversible dependence on LST, advanced metastatic malignancies and extensive brain injury. Participants agreed that futility should be assessed by physicians after consultation with the care team. Intensivists favoured an indirect and stepwise disclosure of the prognosis. Palliative care clinicians focused on a candid and empathetic information strategy. The reasons for continuing futile LST are primarily emotional, such as guilt, grief, fear of legal consequences and concerns about the family's reaction. Other obstacles are organisational routines, insufficient legal and palliative knowledge and treatment requests by patients or families. Conclusion Managing futility could be improved by communication training, knowledge transfer, organisational improvements and emotional and ethical support systems. The authors propose an algorithm for end-of-life decision making focusing on goals of treatment. (shrink)

The paper identifies defining characteristics of the principal models of problem-solving behavior which are useful in developing a general theory of problem-solving. An attempt is made both to make explicit those disagreements between theorists of different persuasions which have served as obstacles to an integrated approach, and to show that these disagreements have arisen from a number of conceptual confusions: The conflict between information processors and behavioral analysts has resulted from a common failure to understand theoretical sufficiency, and hence (...) these theorists have been at a loss to understand one another. Two directions of research in problem-solving, mechanical algorithmic problem-solving and the psychology of invention, have been thought to be divergent, but in fact complement one another once it is clear that problem-solving involves neither discovery nor invention, but rather is a matter of perceptual encoding and processing. Successful problem-solving behavior can be described as adaptive, learning behavior in which organization skills are effectively associated with situationally determined perceptual encoding processes of the individual mechanical or biological problem-solver. (shrink)

Although the employment situation of disabled people has widely been identified as in need of improvement, progress in this area remains slow. While some progress has been made in including the physically or sensory disabled in the workplace, other types of disability have been largely neglected. This applies particularly to disabled workers in atypical employment, such as those whose workplace is the Digital Economy. In this article, we discuss the case of disabled app developers as a significant example of how (...) the current regulatory framework fails to be inclusive in its attempts to protect the rights of disabled Digital Economy workers. We identify two problems that are at the heart of this: first, a continuing failure to collect relevant and comprehensive data on disability, and second, a lack of accountability towards disabled workers in atypical employment. Consequently, we call for better data collection and argue for urgent policy changes to close the existing accountability gap. (shrink)

Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated filtering algorithm (...) work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction. (shrink)

Causal models of agents have been used to analyse the safety aspects of machine learning systems. But identifying agents is non-trivial -- often the causal model is just assumed by the modeler without much justification -- and modelling failures can lead to mistakes in the safety analysis. This paper proposes the first formal causal definition of agents -- roughly that agents are systems that would adapt their policy if their actions influenced the world in a different way. From this we (...) derive the first causal discovery algorithm for discovering agents from empirical data, and give algorithms for translating between causal models and game-theoretic influence diagrams. We demonstrate our approach by resolving some previous confusions caused by incorrect causal modelling of agents. (shrink)

By drawing on the philosophy of Bernard Stiegler, the phenomena of mechanical (a.k.a. artificial, digital, or electronic) intelligence is explored in terms of its real significance as an ever-repeating threat of the reemergence of stupidity (as cowardice), which can be transformed into knowledge (pharmacological analysis of poisons and remedies) by practices of care, through the outlook of what researchers describe equivocally as “artificial stupidity”, which has been identified as a new direction in the future of computer science and machine problem (...) solving as well as a new difficulty to be overcome. I weave together of web of “artificial stupidity”, which denotes the mechanic (1), the human (2), or the global (3). With regards to machine intelligence, artificial stupidity refers to: 1a) Weak A.I. or a rhetorical inversion of designating contemporary practices of narrow task-based procedures by algorithms in opposition to “True A.I.”; 1b) the restriction or employment of constraints that weaken the effectiveness of A.I., which is to say a “dumbing-down” of A.I. by intentionally introducing mistakes by programmers for safety concerns and human interaction purposes; 1c) the failure of machines to perform designated tasks; 1d) a lack of a noetic capacity, which is a lack of moral and ethical discretion; 1e) a lack of causal reasoning (true intelligence) as opposed to statistical associative “curve fitting”; or 2) the phenomenon of increasing human “stupidity” or drive-based behaviors, which is considered as the degradation of human intelligence and/or “intelligent human behavior” through technics; and finally, 3) the global phenomenon of increasing entropy due to a black-box economy of closed systems and/or industry consolidation. (shrink)

As new unanticipated items are added to a memory, it must be able to reorganize itself, integrating the new items into its structure. The reorganization process must maintain the memory's structure and also build up the knowledge retrieval strategies need to search that structure. This study will present an algorithm for knowledge‐based memory reorganization. Included in that algorithm are processes for directed generalization and generalization refinement. A fact retrieval system called CYRUS which uses the algorithm is also presented. Conclusions are (...) drawn about maintaining accessibility in a conceptual memory, organizing generalized knowledge with respect to specific knowledge, and expected retrieval failures due to change over time in the memory's organization. (shrink)

Teleological theories of content are thought to suffer from two related difficulties. According to the problem of indeterminacy, biological function is indeterminate in the sense that, in the case of two competing interpretations of the function of an evolved mechanism, there is often no fact of the matter capable of determining which function is the correct one. Therefore, any attempts to construct content out of biological function entail the indeterminacy of content. According to the problem of transparency, statements of biological (...) function are transparent in that a statement of the form 'the function of evolved mechanism M is to represent Fs' can be substituted salva veritate by a statement of the form 'the function of evolved mechanism M is to represent Gs' provided that the statement 'F iff G' is counterfactual supporting. Therefore, any attempt to construct content out of biological function must fail to capture the intensionality of psychological ascriptions. This paper argues that the teleological account is undermined by neither of these problems. Failure to appreciate this point stems from a conflation of two types of proper function - organismic and algorithmic - possessed by an evolved mechanism. These functions underwrite attributions of content to distinct objects. The algorithmic proper function of a mechanism underwrites attributions of content to the mechanism itself, while the organismic proper function of a mechanism underwrites attribution of content to the organism that possesses the mechanism. However the problems of indeterminacy and transparency arise only if the attributions of content attach to the same object. (shrink)

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant increase (...) in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

Online targeting isolates individual consumers, causing what we call epistemic fragmentation. This phenomenon amplifies the harms of advertising and inflicts structural damage to the public forum. The two natural strategies to tackle the problem of regulating online targeted advertising, increasing consumer awareness and extending proactive monitoring, fail because even sophisticated individual consumers are vulnerable in isolation, and the contextual knowledge needed for effective proactive monitoring remains largely inaccessible to platforms and external regulators. The limitations of both consumer awareness and of (...) proactive monitoring strategies can be attributed to their failure to address epistemic fragmentation. We call attention to a third possibility that we call a civic model of governance for online targeted advertising, which overcomes this problem, and describe four possible pathways to implement this model. (shrink)

This article examines types of abductive inference in Hegelian philosophy and machine learning from a formal comparative perspective and argues that Robert Brandom’s recent reconstruction of the logic of recollection in Hegel’s Phenomenology of Spirit may be fruitful for anticipating modes of collaborative abductive inference in human/A.I. interactions. Firstly, the argument consists of showing how Brandom’s reading of Hegelian recollection may be understood as a specific type of abductive inference, one in which the past interpretive failures and errors of a (...) community are explained hypothetically by way of the construction of a narrative that rehabilitates those very errors as means for the ongoing successful development of the community, as in Brandom’s privileged jurisprudential example of Anglo-American case law. Next, this Hegelian abductive dynamic is contrasted with the error-reducing backpropagation algorithms characterizing many current versions of machine learning, which can be understood to perform abductions in a certain sense for various problems but not in the full self-constituting communitarian mode of creative recollection canvassed by Brandom. Finally, it is shown how the two modes of “error correction” may possibly coordinate successfully on certain types of abductive inference problems that are neither fully recollective in the Hegelian sense nor algorithmically optimizable. (shrink)

This new study provides a refreshing look at the issue of exceptions and shows that much of the problem stems from a failure to recognize at least two kinds of exception-ridden law: ceteris paribus laws and ideal laws. Billy Wheeler offers the first book-length discussion of ideal laws. The key difference between these two kinds of laws concerns the nature of the conditions that need to be satisfied and their epistemological role in the law’s formulation and discovery. He presents (...) a Humean-inspired approach that draws heavily on concepts from the information and computing sciences. Specifically, Wheeler argues that laws are best seen as algorithms for compressing empirical data and that ideal laws are needed as 'lossy compressors' for complex data. Major figures in the metaphysics of science receive special attention such as Ronald Giere, Bas van Fraassen, Nancy Cartwright, David Lewis and Marc Lange. This book is essential reading for philosophers of science and will interest metaphysicians, epistemologists and others interested in applying concepts from computing to traditional philosophical problems. (shrink)

The three essays presented here concern natural connections between grammatical derivations and structures provided by certain standard grammar formalisms, on the one hand, and deductions in logical systems, on the other hand. In the first essay we analyse the adequacy of Polish notation for higher-order languages. The Ajdukiewicz algorithm (Ajdukiewicz 1935) is discussed in terms of generalized MP-deductions. We exhibit a failure in Ajdukiewicz’s original version of the algorithm and give a correct one; we prove that generalized MP-deductions have (...) the frontier property, which is essential for the plausibility of Polish notation. The second essay deals with logical systems corresponding to different grammar formalisms, as e.g. Finite State Acceptors, Context-Free Grammars, Categorial Grammars, and others. We show how can logical methods be used to establish certain linguistically significant properties of formal grammars. The third essay discusses the interplay between Natural Deduction proofs in grammar oriented logics and semantic structures expressible by typed lambda terms and combinators. (shrink)

An asymmetry between the demands at the computational and algorithmic levels of description furnishes the illusion that the abstract profile at the computational level can be multiply realized, and that something is actually being shared at the algorithmic one. A disembodied rendering of the situation lays the stress upon the different ways in which an algorithm can be implemented. However, from an embodied approach, things look rather different. The relevant pairing, I shall argue, is not between implementation and (...) algorithm, but rather between algorithm and computation. The autonomy of psychology is a result of the failure to appreciate this point. (shrink)

Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the (...) human brain, which can handle complex and changeable things that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification. (shrink)

This study presents a process model of very long‐term episodic memory. The process presented is a reconstructive process. The process involves application of three kinds of reconstructive strategies—component‐to‐context instantiation strategies, component‐instantiation strategies, and context‐to‐context instantiation strategies. The first is used to direct search to appropriate conceptual categories in memory. The other two are used to direct search within the chosen conceptual category. A fourth type of strategy, called executive search strategies, guide search for concepts related to the one targeted for (...) retrieval.A conceptual memory organization implied by human reconstructive memory is presented along with examples which motivate it. A basic retrieval algorithm is presented for traversing that stucture. Retrieval strategies arise from failures in that algorithm. The memory organization and retrieval processes are implemented in a computer program called CYRUS which stores events in the lives of former Secretaries of State Cyrus Vance and Edmund Muskie and answers questions posed in English concerning that information. Examples which motivate the process model are drawn from protocols of human memory search. Examples of CYRUS's behavior demonstrate the implemented process model. Conclusions are drawn concerning retrieval failures and the relationship of episodic and semantic memory. (shrink)

Mayer, Caruso, and Salovey provide useful updates to the EI ability model and related concepts. However, they do not acknowledge conceptual limitations with the MSCEIT proportion scoring algorithm. In our view, failure to recognize these limitations has impeded refinements to the EI ability model and delayed support for positioning EI within the Cattell-Horn-Carroll three-stratum theory of intelligence. Fully appreciating algorithm-related issues justifies the reanalysis of MSCEIT data and may expand the range of metrics that are available to refine EI (...) theory. (shrink)

In this article, I develop three conceptual innovations within the area of formal metatheory, and present a computer program, called Reconstructor, that implements those developments. The first development consists in a methodology for testing formal reconstructions of scientific theories, which involves checking both whether translations of paradigmatically successful applications into models satisfy the formalisation of the laws, and also whether unsuccessful applications do not. I show how Reconstructor can help carry this out, since it allows the end-user to specify a (...) formal language, input axioms and models formulated in that language, and then ask if the models satisfy the axioms. The second innovation is the introduction of incomplete models (for which the denotation of some terms is missing) into scientific metatheory, in order to represent cases of missing information. I specify the paracomplete semantics built into Reconstructor to deal with sentences where denotation failures occur. The third development consists in a new way of explicating the structuralist notion of a determination method, by equating them with algorithms. This allows determination methods to be loaded into Reconstructor and then executed within a model to find out the value of a previously non-denoting term (i.e. it allows the formal reconstruction to make predictions). This, in turn, can help test the reconstruction in a different way. Finally, I conclude with some suggestions about additional uses the program may have. (shrink)

Predicting our future as individuals is a central to the role of much emerging technology, from hiring algorithms that predict our professional success (or failure) to biomarkers that predict how long (or short) our healthy (or unhealthy) life will be. Yet, much in western culture, from scripture to mythology to philosophy, suggests that knowing one's future may not be in the subject's best interests and might even lead to disaster. If predicting our future as individuals can be harmful as (...) well as beneficial, why are we so willing to engage in so much prediction, from cradle to grave? This book offers a philosophical answer, reflecting on seminal texts in western culture to argue that predicting our future renders much of our existence the automated effect of various causes, which, in turn, helps to alleviate the existential burden of autonomously making sense of our lives in a more competitive, demanding, accelerated society. An exploration of our tendency in a technological era to engineer and so rid ourselves of that which has hitherto been our primary reason for being - making life plans for a successful future, while faced with epistemological and ethical uncertainties - Predicted Humans will appeal to scholars of philosophy and social theory with interests in questions of moral responsibility and meaning in an increasingly technological world. (shrink)

Verification and validation of computer codes and models used in simulation are two aspects of the scientific practice of high importance and have recently been discussed by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to model’s relation to the real world and its intended use. It has been argued that because complex simulations are generally not transparent to a practitioner, (...) the Duhem problem can arise for verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or model’s general inadequacy to its target should be blamed in the case of the model failure. I argue that in order to disentangle verification and validation, a clear distinction between computer modeling and simulation needs to be made. Holding on to that distinction, I propose to relate verification to modeling and validation, which shares the common epistemology with experimentation, to simulation. To explain reasons of their intermittent entanglement I propose a weberian ideal-typical model of modeling and simulation as roles in practice. I suggest an approach to alleviate the Duhem problem for verification and validation generally applicable in practice and based on differences in epistemic strategies and scopes. (shrink)

Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated filtering algorithm (...) work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction. (shrink)

Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with (...) continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss--Markov (``\OU'') processes and show how such \textit{OU networks} can express complex dynamic phenomena such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world. (shrink)

In the complex environment, the suddenly changing structural parameters and abrupt actuator failures are often encountered, and the negligence or unproper handling method may induce undesired or unacceptable results. In this paper, taking the suddenly changing structural parameters and abrupt actuator failures into consideration, we focus on the robust adaptive control design for a class of heterogeneous Takagi–Sugeno fuzzy nonlinear systems subjected to discontinuous multiple uncertainties. The key point is that the switch modes not only vary with the system time (...) but also vary with the system states, and the intrinsic heterogeneous characteristics make it difficult to design stable controllers. Firstly, the concepts of differential inclusion are introduced to describe the heterogeneous fuzzy systems. Meanwhile, a fundamental lemma is provided to demonstrate the criteria of the boundness for a Filippov solution. Then, by using the set-valued Lie derivative of the Lyapunov function and introducing a vector of specific continuous functions, the closed-loop T-S fuzzy differential inclusion systems are proved to be ultimately bounded. The sufficient conditions for system stability are derived in term of linear matrix inequalities, which can be solved directly. Finally, a numerical example is provided to illustrate the effectiveness of the proposed control algorithm. (shrink)