2.1 Utilitarianism and Consequentialism in AI Ethics

Utilitarianism and consequentialism in AI ethics focus on maximizing positive outcomes for the greatest number of people. These approaches guide AI systems to make decisions based on their overall impact, weighing costs and benefits across different stakeholders and timeframes.

While utilitarian AI could optimize outcomes and address complex global challenges, it faces significant hurdles. Quantifying well-being, comparing consequences across domains, and balancing individual rights with collective welfare pose ethical dilemmas that require careful consideration in AI development.

Utilitarianism in AI Ethics

Defining Utilitarianism and Consequentialism

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• Utilitarianism is an ethical theory that states the most ethical action maximizes overall happiness or well-being for the greatest number of people
• Consequentialism is a class of ethical theories that judge the morality of an action based on its consequences rather than the action itself
• In the context of AI ethics, utilitarianism and consequentialism focus on designing AI systems that make decisions based on maximizing positive outcomes and minimizing negative consequences for all stakeholders
• Utilitarian AI would aim to choose actions that result in the greatest good for the greatest number, taking into account the well-being of humans, animals, and potentially other sentient beings (robots with advanced artificial general intelligence)
• Consequentialist AI would be designed to weigh and compare the outcomes of different actions to determine the most ethical course of action in a given situation (autonomous vehicles deciding whether to prioritize passenger safety or pedestrian safety in an unavoidable collision scenario)

Benefits vs Drawbacks of Utilitarian AI

Potential Benefits of Utilitarian AI

• Utilitarian AI could optimize outcomes for the majority, reducing human bias and subjectivity in decision-making and considering the broader societal impact of actions
• It could help address complex global challenges, such as resource allocation (distributing limited medical supplies during a pandemic), public health policies (implementing lockdowns or vaccine mandates), and environmental sustainability (transitioning to renewable energy sources), by focusing on maximizing overall welfare
• Utilitarian AI has the potential to make more impartial and consistent decisions compared to humans, who may be influenced by personal biases, emotions, or limited information processing capabilities
• By considering the consequences of actions on a larger scale, utilitarian AI could identify solutions that benefit society as a whole, even if they may not be immediately apparent or popular among individuals

Drawbacks and Risks of Utilitarian AI

• Quantifying and comparing different types of well-being is difficult, which could lead to utilitarian AI neglecting the rights and needs of minorities or justifying unethical means for the sake of achieving desirable ends
• Strict adherence to utilitarian principles may lead to AI making decisions that violate individual rights, privacy, or autonomy in the name of greater societal benefit (using facial recognition technology to track and control population movements)
• The aggregation of preferences and well-being across diverse populations poses significant challenges and may result in oversimplified or biased assessments of consequences (failing to account for cultural differences or the needs of marginalized communities)
• Utilitarian AI may prioritize short-term gains over long-term sustainability, potentially leading to decisions that have negative consequences for future generations (exploiting natural resources for immediate economic benefits)
• There is a risk that the designers of utilitarian AI systems may intentionally or unintentionally encode their own values and biases into the decision-making process, leading to outcomes that reflect narrow interests rather than the greater good

Challenges of Quantifying AI Consequences

Defining and Measuring Well-Being

• Quantifying the consequences of AI actions requires defining and measuring relevant metrics of well-being, such as happiness, health, safety, fairness, and economic welfare, which can be complex and context-dependent
• Different individuals and cultures may have varying conceptions of well-being, making it challenging to establish universally accepted metrics (valuing individual autonomy vs. communal harmony)
• Some aspects of well-being, such as emotional states or subjective experiences, are difficult to measure objectively or compare across individuals
• The relative importance of different well-being metrics may vary depending on the situation, requiring AI systems to adapt their decision-making processes accordingly (prioritizing safety over convenience in high-risk scenarios)

Comparing Consequences Across Domains and Timescales

• Comparing consequences across different domains (health, education, environment), timescales (short-term vs. long-term), and populations (local vs. global) necessitates making value judgments and trade-offs that may not have clear or universally accepted solutions
• The long-term and indirect consequences of AI actions may be difficult to predict or attribute, making it challenging to assess the full scope of their impact (the societal effects of widespread automation on employment and income inequality)
• Uncertainties and biases in data, models, and assumptions used to quantify consequences can lead to flawed or misleading evaluations of AI actions (relying on historical data that reflects past discriminatory practices)
• The subjective nature of well-being and the diversity of individual preferences complicate the aggregation and comparison of consequences across different stakeholders (balancing the needs of current and future generations in environmental decision-making)

Aggregate Welfare in AI Decision-Making

Maximizing Aggregate Welfare

• From a utilitarian perspective, the primary goal of AI decision-making should be to maximize aggregate welfare, which is the sum total of well-being across all affected individuals
• Aggregate welfare considers the interests and preferences of all stakeholders, including those who may be indirectly affected by AI actions, such as future generations or non-human sentient beings (animals impacted by habitat destruction)
• Maximizing aggregate welfare requires AI to weigh and balance the positive and negative consequences of its actions across different domains and timescales, taking into account both short-term and long-term impacts (investing in renewable energy infrastructure for long-term sustainability)
• Utilitarian AI would need to grapple with the challenges of defining, measuring, and comparing different aspects of well-being to ensure that its pursuit of aggregate welfare aligns with human values and priorities (considering mental health alongside physical health in medical decision-making)

Ethical Concerns and Considerations

• The focus on aggregate welfare may justify AI actions that prioritize the greater good over individual rights or interests, raising ethical concerns about fairness, autonomy, and the protection of vulnerable groups (forcibly quarantining individuals to prevent the spread of a disease)
• There is a risk that the pursuit of aggregate welfare could lead to the tyranny of the majority, where the preferences of the larger group dominate those of minorities or individuals with unique needs (neglecting the accessibility requirements of people with disabilities in urban planning)
• Utilitarian AI may face ethical dilemmas when the consequences of its actions are uncertain or when there are competing moral principles at stake (choosing between saving a larger number of lives or protecting the privacy of individuals)
• The designers of utilitarian AI systems must be transparent about their value assumptions and engage in ongoing dialogue with diverse stakeholders to ensure that the pursuit of aggregate welfare is aligned with societal values and priorities (involving affected communities in the development and deployment of AI systems)