2.5 Application of Ethical Frameworks to AI Scenarios

Ethical frameworks provide crucial guidance for AI decision-making. By applying principles like deontology, utilitarianism, and virtue ethics to real-world scenarios, we can navigate complex moral dilemmas in AI development and deployment.

Each framework offers unique insights, but also has limitations. By using multiple approaches and following a structured decision-making process, we can make more comprehensive and ethically sound choices for AI systems that impact society.

Ethical Frameworks for AI

Applying Ethical Frameworks to AI Scenarios

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• Deontological ethics evaluates the inherent rightness or wrongness of actions based on moral rules or duties, regardless of consequences
• Utilitarianism, a consequentialist theory, judges the most ethical choice as the one that produces the greatest good for the greatest number
• Virtue ethics focuses on moral character and virtues (compassion, fairness, integrity, accountability, wisdom) rather than duties, rules, or consequences
• Care ethics prioritizes the interdependence of people, interpersonal relationships, and responsibilities (attentiveness, competence, responsiveness)
• The capability approach emphasizes people's real freedoms and opportunities to make choices they value, considering what they can actually do and be
• Applying ethical frameworks to AI case studies requires:
  • Identifying key ethical issues, stakeholders, and interests
  • Analyzing potential consequences of different actions
  • Determining how each framework would guide decision-making in that context

Case Study Examples

• Self-driving car accident liability and responsibility (deontology vs. utilitarianism)
• Algorithmic bias in hiring or lending decisions (fairness, non-discrimination)
• Emotional AI in mental health chatbots (care ethics, virtue ethics)
• Facial recognition surveillance and privacy rights (capability approach)

Strengths and Limitations of Ethical Frameworks

Strengths of Ethical Frameworks for AI

• Deontology provides clear rules for determining the morality of an action
• Utilitarianism offers a decision procedure for right action based on outcomes
• Virtue ethics allows richer discussion of moral character and education
• Care ethics recognizes the moral importance of relationships and responsibilities
• The capability approach emphasizes substantive freedoms beyond just mental states
• Each framework focuses attention on important moral considerations (duties, consequences, virtues, relationships, freedoms)

Limitations and Challenges in Applying Ethical Frameworks to AI

• Deontology may struggle with complex scenarios involving conflicting duties and can lead to suboptimal consequences
• Utilitarianism faces challenges with fairness, minority rights, and measuring/comparing different goods or preferences across people
• Virtue ethics offers less clear action-guidance and can be seen as relativistic; identifying virtuous action in AI can be difficult
• Care ethics may prioritize partial relational considerations over impartial ones; specifying how to weigh AI responsibilities is hard
• The capability approach requires contentious determination of which capabilities matter and how to weight them
• Using a single framework may miss important moral considerations captured by others; multiple frameworks provide more comprehensive analysis

Ethical Principles in AI Decision-Making

Potential Conflicts Between Ethical Principles in AI

• Tension between respecting individual autonomy/liberty rights and achieving the best overall welfare consequences (paternalistic nudges, online speech restrictions)
• Trade-offs between ensuring fairness/non-discrimination and maximizing accuracy/efficiency of AI systems (algorithms disadvantaging some subgroups)
• Conflicts between protecting individual privacy rights and realizing social benefits of large-scale data collection and analysis (contact tracing apps)
• Tension between promoting AI transparency/explicability for public trust and protecting intellectual property/competitive advantage of companies
• Conflicts between short-term benefits and long-term risks of AI systems (misaligned or uncontrolled AI posing existential threats)
• Clashes between duties of loyalty/confidentiality to clients/employers and whistle-blower responsibilities to expose unethical AI practices

Weighing and Balancing Different Ethical Principles

• Systematically analyze cases through the lens of each relevant ethical framework
  • Utilitarian analysis compares consequences and overall welfare impacts
  • Deontological analysis considers inherent rightness/wrongness of actions based on duties
  • Virtue ethics examines what actions exemplify moral virtues and good character
  • Care ethics explores how actions fulfill responsibilities within relationships
• Directly examine conflicts or trade-offs between frameworks and consider which principles should take priority in the context
• Attempt to find actions that satisfy multiple ethical frameworks when possible
• Make all-things-considered judgments incorporating insights from each framework, acknowledging difficulty of the case

Structured Ethical Decision-Making in AI

Key Steps in Ethical Decision-Making Process for AI

• Identify factual details of the case (decision points, stakeholders, potential impacts) and state relevant assumptions
• Determine central ethical issues, questions, and value conflicts at stake
• Identify which ethical frameworks and principles are most relevant given the key issues
• Analyze the case through each relevant ethical framework, highlighting what it prioritizes and suggests
• Examine conflicts between frameworks and principles, considering which should take priority
• Make an all-things-considered judgment based on the analysis, describing key reasons and acknowledging difficulty
• Implement proposed solution, monitor outcomes, make revisions if needed, and incorporate lessons into future decisions

Benefits of a Structured Approach Incorporating Multiple Frameworks

• Provides a systematic, rigorous process for ethical analysis and discussion of AI issues
• Ensures consideration of different types of morally relevant factors (duties, consequences, virtues, relationships, capabilities)
• Highlights central tensions and value trade-offs at stake, forcing clarity about priorities
• Guards against overlooking important ethical considerations or stakeholder perspectives
• Allows for contextual flexibility in balancing principles based on details of the case
• Promotes more comprehensive deliberation for wiser and more defensible AI decisions