In his famous ‘Integrity Objection’, Bernard Williams condemns utilitarianism for requiring us to regard our projects as dispensable, and thus precluding us from being properly committed to them. In this paper, I argue against commitment as Williams defines it, drawing upon insights from the socialist tradition as well as mainstream analytic moral philosophy. I show that given the mutual interdependence of individuals (a phenomenon emphasised by socialists) several appealing non-utilitarian moral principles also require us to regard our projects as dispensable. (...) This means that those who endorse those principles cannot appeal to Williams’s argument against utilitarianism. It also puts pressure on his thought that moral theories ought to permit commitment – in fact, it suggests that they ought not. Regarding one’s projects as dispensable may be alienating, and this may motivate us to hang onto commitment and reject these non-utilitarian principles along with utilitarianism. However, commitment also threatens a kind of alienation – from other people. Drawing upon the socialist tradition again, I argue that avoiding this form of alienation is necessary for proper engagement with our projects, and thereby with ourselves. (shrink)

Act-consequentialism (C) is self-effacing for an agent iff that agent’s not accepting C would produce the best outcome. The question of whether C is self-effacing is important for evaluating C. Some hold that if C is self-effacing that would be a mark against it (Williams 1973: 134); however, the claim that C is self-effacing is also used to defend C against certain objections (Parfit 1984: Ch. 1, Railton 1984). -/- In this paper I will show that one argument suggested by (...) Parfit and Railton fails to establish that C is self-effacing for individuals. However, a slightly different argument may show that C is self-effacing for groups. This raises the intriguing possibility that it might be right for an individual, but not for a group of which they are a member, to accept a moral theory. This possibility, odd though it seems, might be helpful to consequentialists. (shrink)

Capitalism is defined as the economic structure in which decisions over production are largely made by or on behalf of individuals in virtue of their private property ownership, subject to the incentives and constraints of market competition. In this paper, I will argue that considerations of long-term welfare, such as those developed by Greaves and MacAskill (2021), support anticapitalism in a weak sense (reducing the extent to which the economy is capitalistic) and perhaps support anticapitalism in a stronger sense (establishing (...) an alternative economic structure in which capitalism is not predominant). I hope to encourage longtermists to give anticapitalism serious consideration, and to encourage anticapitalists to pursue criticisms of capitalism’s efficiency as well as its injustices. (shrink)

Some of the world's most powerful corporations practise what Shoshana Zuboff (2015; 2019) calls ‘surveillance capitalism’. The core of their business is harvesting, analysing and selling data about the people who use their products. In Zuboff's view, the first corporation to engage in surveillance capitalism was Google, followed by Facebook; recently, firms such as Microsoft and Amazon have pivoted towards such a model. In this paper, I suggest that Karl Marx's analysis of the relations between industrial capitalists and workers is (...) closely analogous to the relations between surveillance capitalists and users. Furthermore, three problematic aspects of industrial capitalism that Marx describes – alienation, exploitation and accumulation – are also aspects, in new forms, of surveillance capitalism. I draw heavily on Zuboff's work to make these parallels. However, my Marx-inspired account of surveillance capitalism differs from hers over the nature of the exchange between users and surveillance capitalists. For Zuboff, this is akin either to robbery or the gathering of raw materials; on the Marx-inspired account it is a voluntary sale. This difference has important implications for the question of how to resist surveillance capitalism. -/- Joint winner of the 2020 Philosophy essay prize. (shrink)

This paper examines a recent argument in favour of strong precautionary action—possibly including working to hasten human extinction—on the basis of a decision-theoretic view that accommodates the risk-attitudes of all affected while giving more weight to the more risk-averse attitudes. First, we dispute the need to take into account other people’s attitudes towards risk at all. Second we argue that a version of the non-identity problem undermines the case for doing so in the context of future people. Lastly, we suggest (...) that we should not work to hasten human extinction, even if significant risk aversion is warranted. (shrink)

Utilitarianism is the view that as far as morality goes, one ought to choose the option which will result in the most overall well-being—that is, that maximises the sum of whatever makes life worth living, with each person’s life equally weighted. The promise of utilitarianism is to reduce morality to one simple principle, easily incorporated into policy analysis, economics and decision theory. However, utilitarianism is not popular amongst moral philosophers today. This is in large part due to the influence of (...) Williams’s ‘Integrity Objection’ (1973). Though the Integrity Objection has been influential in turning philosophers against utilitarianism, it is also difficult to make precise, evidenced by the myriad interpretations in the literature. In this paper I interpret the objection as holding that agents who accept utilitarianism cannot, as a matter of psychology, be committed to their projects. I explore other interpretations, finding some to be inconsistent with Williams’s approach, and others to be relatively easily answerable by the utilitarian. The psychological problem I identify is harder for utilitarians to avoid, though I have begun to offer a response in other work. (shrink)

This thesis defends utilitarianism: the view that as far as morality goes, one ought to choose the option which will result in the most overall well-being. Utilitarianism is widely rejected by philosophers today, largely because of a number of influential objections. In this thesis I deal with three of them. Each is found in Bernard Williams’s ‘A Critique of Utilitarianism’ (1973). The first is the Integrity Objection, an intervention that has been influential whilst being subject to a wide variety of (...) interpretations. In Chapter Two I give my interpretation of Williams’s Integrity objection; in Chapter Three I discuss one common response to it, and in Chapters Four and Five I give my own defence of utilitarianism against it. In Chapter Six I discuss a second objection: the problem of pre-emption. This problem is also found in Williams, but has received greater attention through the work of other authors in recent years. It suggests that utilitarianism is unable to deal with some of the modern world’s most pressing moral problems, and raises an internal tension between the twin utilitarian aims of making a difference and achieving the best outcomes. In Chapter Seven I discuss a third objection: that utilitarianism is insufficiently egalitarian. I find this claim to be unwarranted, in light of recent social science and philosophy. My responses to Williams’s objections draw upon resources from the socialist tradition – in particular, that tradition’s emphasis on the importance of social connections between individuals. Socialists have often been hostile to utilitarianism, in part for socialist-inflected versions of Williams’s objections. Thus, in responding to these objections I aim to demonstrate that socialist thought contains the means to defuse not only mainstream philosophy’s rejection of utilitarianism but also its own, and thus to re-open the possibilities for a productive engagement between the two traditions. (shrink)

The Repugnant Conclusion served an important purpose in catalyzing and inspiring the pioneering stage of population ethics research. We believe, however, that the Repugnant Conclusion now receives too much focus. Avoiding the Repugnant Conclusion should no longer be the central goal driving population ethics research, despite its importance to the fundamental accomplishments of the existing literature.

Stress has become a significant concern in today’s fast-paced world, affecting individuals’ physical and mental well-being. This project, titled Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms, aims to address this issue by leveraging data-driven insights to identify stress levels. The proposed system analyzes sleeping patterns, including sleep duration, interruptions, and quality, to classify stress levels effectively. By utilizing advanced machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine, the model processes (...) data from wearable devices or sleep-monitoring apps to extract relevant features. Key parameters such as sleep latency, efficiency, and disturbances are analyzed alongside other influencing factors like age, lifestyle, and physical activity. The project employs a robust dataset for training and testing, ensuring high accuracy and reliability in predicting stress levels. The system not only identifies stress levels but also provides actionable insights and recommendations to improve sleep quality and overall well-being. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to measure the model's performance. The outcome of this project demonstrates the potential of machine learning in enhancing healthcare applications. It provides a scalable and efficient tool for stress detection, promoting early intervention and better management of stress-related disorders. (shrink)

In critical medical emergencies, timely and accurate drug recommendation is essential for saving lives and reducing complications. This project proposes a Drug Recommendation System utilizing Machine Learning (ML) techniques to assist healthcare professionals in making quick and accurate drug selections based on patient symptoms, medical history, and emergency condition. The system integrates data from diverse medical databases, including symptoms, diseases, patient demographics, and prior medical records, to recommend the most appropriate drugs or treatments in real-time. The ML model is trained (...) on historical medical data, utilizing algorithms such as Decision Trees, Random Forests, and Neural Networks to predict optimal drug choices for different emergency situations. The system not only considers the immediate medical needs but also factors in contraindications, potential drug interactions, and patient-specific conditions, ensuring safer drug administration. This approach aims to reduce human errors in emergency drug prescriptions, enhance the speed of medical response, and improve the overall quality of care during emergencies. By leveraging data-driven insights, the system supports healthcare professionals in making well-informed decisions, even in high-pressure situations. Ultimately, the Drug Recommendation System serves as a valuable tool in healthcare, enhancing emergency medical care and optimizing treatment outcomes. (shrink)

Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. Key parameters such (...) as time, location, type of crime, and socio-economic factors are considered to build a comprehensive predictive model. Machine learning algorithms like Decision Trees, Random Forest, and Support Vector Machines are used for efficient data preprocessing and pattern recognition. Deep learning models, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable advanced feature extraction and temporal analysis for enhanced prediction accuracy. The system's performance is evaluated using metrics such as precision, recall, F1-score, and accuracy, demonstrating its reliability and scalability for real-world applications. Furthermore, the use of visualization tools allows stakeholders to comprehend crime patterns effectively, supporting proactive policing strategies. This data-driven approach not only improves crime deterrence but also aids policymakers in resource allocation and urban planning. The proposed system has the potential to revolutionize crime management by transforming traditional reactive measures into predictive, preventive, and intelligent solutions. (shrink)

A foundational problem in population ethics is the “repugnant conclusion", introduced by Derek Parfit in Reasons and Persons. It holds that for any possible population of at least ten billion lives of very high positive welfare, there is some larger possible population of lives of very low positive welfare whose existence would be better, if other things are equal. I call this claim RC1. In this article, I argue that by carefully considering the nature and variety of possible lives of (...) very low positive welfare, and distinguishing RC1 from a related but stronger claim I call RC2, we can show that RC1 is more acceptable than it first appears. It has been claimed that perfectionism— that is, a special concern for the best things in life—is a reason to reject the repugnant conclusion. I argue that although perfectionism gives us a reason to reject RC2, it does not give us any reason to reject, and might in fact support, RC1. In his last two papers on the subject, Parfit develops a strategy for avoiding the repugnant conclusion that appeals in part to perfectionism. In the final part of this article, I argue that Parfit’s strategy can help us avoid RC2 but not RC1. If I am right that RC1 is more acceptable than RC2, this may not be an unwelcome result. (shrink)

Objective: A deep understanding of the relationship between a scarce drug's dose and clinical response is necessary to appropriately distribute a supply-constrained drug along these lines. Summary of key data: The vast majority of drug development and repurposing during the COVID-19 pandemic – an event that has made clear the ever-present scarcity in healthcare systems –has been ignorant of scarcity and dose optimisation's ability to help address it. Conclusions: Future pandemic clinical trials systems should obtain dose optimisation data, as these (...) appear necessary to enable appropriate scarce resource allocation according to societal values. (shrink)