Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that fail to (...) connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security. (shrink)

This essay introduces this special issue on virtue ethics in relation to military AI. It describes the current situation of military AI ethics as following that of AI ethics in general, caught between consequentialism and deontology. Virtue ethics serves as an alternative that can address some of the weaknesses of these dominant forms of ethics. The essay describes how the articles in the issue exemplify the value of virtue-related approaches for these questions, before ending with thoughts for further research.

One of the most common contemporary approaches for developing an ethics of artificial intelligence (AI) involves elaborating guiding principles. This essay explores the limitations of this approach, using the history of bioethics as a comparative case. The examples of bioethics and recent AI ethics suggest that principles are difficult to implement in everyday practice, fail to direct individual action, and can frequently result in a pure proceduralism. The essay encourages an additional attention to virtue, which forms the dispositions of actors, (...) directs them to valued ends, and encourages right action in particular situation to complement principles. Practices and professions like medicine or warfighting that already have rich traditions of reflection on virtue ethics will be especially fruitful fields for developing an AI ethics of virtue. (shrink)

We explore aversion to the use of algorithms in moral decision-making. So far, this aversion has been explained mainly by the fear of opaque decisions that are potentially biased. Using incentivized experiments, we study which role the desire for human discretion in moral decision-making plays. This seems justified in light of evidence suggesting that people might not doubt the quality of algorithmic decisions, but still reject them. In our first study, we found that people prefer humans with decision-making discretion to (...) algorithms that rigidly apply exogenously given human-created fairness principles to specific cases. In the second study, we found that people do not prefer humans to algorithms because they appreciate flesh-and-blood decision-makers per se, but because they appreciate humans’ freedom to transcend fairness principles at will. Our results contribute to a deeper understanding of algorithm aversion. They indicate that emphasizing the transparency of algorithms that clearly follow fairness principles might not be the only element for fostering societal algorithm acceptance and suggest reconsidering certain features of the decision-making process. (shrink)

The growing uses of algorithm-based decision-making in human resources management have drawn considerable attention from different stakeholders. While prior literature mainly focused on stakeholders directly related to HR decisions (e.g., employees), this paper pertained to a third-party observer perspective and investigated how consumers would respond to companies’ adoption of algorithm-based HR decision-making. Through five experimental studies, we showed that the adoption of algorithm-based (vs. human-based) HR decision-making could induce consumers’ unfavorable ethicality inferences of the company (study 1); (...) because implementing a calculative and data-driven approach (i.e. algorithm-based) to make employee-related decisions violates the deontological principles of respectful employee treatment (study 2). However, this effect was attenuated when consumers had high (vs. low) power distance beliefs (study 3); the algorithm served as assistance (vs. replacement) for human decisions (study 4); or the adoption was framed as employee-oriented (vs. company-oriented) motivated (study 5). Our findings suggested that consumers are aversive to algorithm-based HR decision-making because it is deontologically problematic regardless of its decision quality (i.e. accuracy). This paper contributes to the extant understanding of stakeholders’ responses to algorithm-based HR decision-making and consumers’ attitudes toward algorithm users. (shrink)

Foucault’s disciplinary society and his notion of panopticism are often invoked in discussions regarding electronic surveillance. Against this use of Foucault, I argue that contemporary trends in surveillance technology abstract human bodies from their territorial settings, separating them into a series of discrete flows through what Deleuze will term, the surveillant assemblage. The surveillant assemblage and its product, the socially sorted body, aim less at molding, punishing and controlling the body and more at triggering events of in- and ex-clusion from (...) life opportunities. The meaning of the body as monitored by latest generation vision technologies formed from machine only surveillance has been transformed. Such a body is no longer disciplinary in the Foucauldian sense. It is a virtual/flesh interface broken into discrete data flows whose comparison and breakage generate bodies as both legible and eligible (or illegible). (shrink)

While principle-based ethics is well known and widely accepted in psychiatry, much less is known about how decisions are made in clinical practice, which case scenarios exist, and which challenges exist for decision-making. Protocols of the central ethics committee responsible for four psychiatric hospitals over 7 years (N=17) were analysed. While four cases concerned suicide risk in the case of intended hospital discharge, the vast majority (N=13) concerned questions of whether the responsible physician should or should not initiate the use (...) of coercion in patients lacking mental capacity. The committee’s recommendations were non-uniform. Forced feeding and electroconvulsive therapy were endorsed in each one case. In two cases of intermittent loss of capacity due to heavy drinking or intermittent severe suicidal ideation, a self-binding contract was recommended and the use of coercion was considered as justified for a very limited period. In all other cases, most of which involved involuntary treatment, the use of coercion was not endorsed. Without exception, the recommendations were accepted with relief by the physicians and their treatment teams, who feared liability in the event of harm to the patient. Eventually, a model of a decision algorithm was derived from the ethical arguments in the protocols. (shrink)

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. (shrink)

The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence (...) algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image. (shrink)

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor. This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve (...) the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model. (shrink)

We extend previous work by modeling evolution of communication using a spatialized genetic algorithm which recombines strategies purely locally. Here cellular automata are used as a spatialized environment in which individuals gain points by capturing drifting food items and are 'harmed' if they fail to hide from migrating predators. Our individuals are capable of making one of two arbitrary sounds, heard only locally by their immediate neighbors. They can respond to sounds from their neighbors by opening their mouths or (...) by hiding. By opening their mouths in the presence of food they maximize gains; by hiding when a predator is present they minimize losses. We consider the result a 'natural' template for benefits from communication; unlike a range of other studies, it is here only the recipient of communicated information that immediately benefits. (shrink)

Model predictive control technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. However, the use of MPC technology needs to dynamically select an optimal solution from the Pareto solution set to implement, which needs the participant of the decision-makers frequently. In order to reduce the burden on decision-makers, we designed a knee point-based evolutionary multiobjective optimization algorithm, termed KBEMO. Knee point is the solution on Pareto front with the maximum marginal utility, (...) which is considered as the preferred solution if there is no other preference. This algorithm focuses on obtaining the knee region and automatically outputs knee points after the optimization. By combining this algorithm with MPC technology, it can effectively reduce the amount of computational consumption and obtain better convergence. Experimental results show that this method is more competitive than the traditional single-objective MPC method. (shrink)

This research poses the question of whether it is possible to optimize modern legislative drafting by integrating LLM-based systems into the lawmaking process to address the pervasive challenge of misinformation and disinformation in the age of AI. While misinformation is not a novel phenomenon, with the proliferation of social media and AI, disseminating false or misleading information has become a pressing societal concern, undermining democratic processes, public trust, and social cohesion. AI can be used to proliferate disinformation and misinformation through (...) fake news and deepfakes; can AI also be used for beneficial purposes to develop the antidote legislation combatting these challenges? Leveraging the capabilities of LLMS, such as ChatGPT and others, can present a promising direction for optimizing legislative drafting. By proposing the methodological approach of an AI bun, this article explores an important approach in which LLMS can support lawmakers and policy experts in crafting legislation. The article contributes to the discourse through a nuanced understanding of the opportunities and challenges in harnessing LLM-powered tools for legislative innovation. Ultimately, it underscores the transformative potential of LLMs as a potential resource for lawmakers seeking to navigate decision-making while developing legislation on an example of navigating the intricate landscape of misinformation and disinformation regulation in the age of AI. (shrink)

Connecting corporate software work in the United States and Germany, this essay tracks the racialization of mostly male Indian software engineers through the casualization of their labor. In doing so, I show the connections between overt, anti-immigrant violence today and the ongoing use of race to sediment divisions of labor in the industry as a whole. To explain racialization in the tech industry, I develop the concept of race-as-algorithm as a device to unpack how race is made productive within (...) digital economies and to show the flexibility of race as it works to create orders of classification that are sensitive to context. Using evidence collected through observation in tech offices and through interviews with programmers over five years, I track race as an essential but continually disavowed variable within the construction of global tech economies. Historical racializations of casual labor in plantation economies illuminates how casualness marks laborers whose rights can be muted and allows corporations to deny their culpability in promoting discrimination within and outside of the tech industry. These denials occur across a political field that divides “good” from “bad” migrants. Using the ethnographic symptoms that Indian tech workers identify in their environments, this essay reads these signs as an antidote to these continued denials. (shrink)

The computer distance teaching system teaches through the network, and there is no entrance threshold. Any student who is willing to study can log in to the network computer distance teaching system for study at any free time. Neural network has a strong self-learning ability and is an important part of artificial intelligence research. Based on this study, a neural network-embedded architecture based on shared memory and bus structure is proposed. By looking for an alternative method of exp function to (...) improve the speed of radial basis function algorithm, and then by analyzing the judgment conditions in the main loop during the algorithm process, these judgment conditions are modified conditionally to reduce the calculation scale, which can double the speed of the algorithm. Finally, this article verifies the function, performance, and interface of the computer distance education system. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic (...) class='Hi'>algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

What happens when a patient-interfacing conversational artificial intelligence system (CAI)—AI that combines natural language understanding, processing, and machine-learning models to autonomously...