Key concepts for AI Ethics

Key concepts for AI Ethics

See also: ❖ Index of Concepts

Here are some concepts with relevance for ethics of AI. This is a very incomplete list, designed to get you started.

• accountability: The obligation to explain and justify one's decisions or actions, particularly those made by AI systems.
  • See also: ConceptNote - Algorithmic Accountability in Healthcare - Wolterbeek
• anthropocentrism: A viewpoint placing humans at the center of considerations for the impact and ethical implications of AI.
• artificial general intelligence: AI systems with general cognitive abilities similar to human intelligence, making them capable of understanding, learning, and applying knowledge across a wide range of tasks.
• artificial moral agents: AI systems endowed with the capacity to make moral decisions or act ethically.
• artificial morality: The field of study concerned with the moral behavior of artificial entities.
• autonomy (respect for): The ethical principle that AI systems should respect individuals' ability to make their own decisions.
• automation: The use of AI systems or machines to perform tasks without human intervention.
• bias or lack of fairness: This involves the examination of AI systems to prevent them from reinforcing societal biases and discrimination, including issues like gender or racial bias in AI algorithms.
• consciousness: The debate surrounding whether AI can possess a subjective awareness and understanding similar to human consciousness.
• consent: The concept of gaining informed consent from individuals when their data is used for AI systems.
• contextual approach to privacy: The concept that privacy norms and expectations may vary depending on the specific context or situation.
• data protection & privacy: How personal information is gathered, used, and protected by AI systems.
• dataveillance: Surveillance through the collection and analysis of data, often by AI systems.
• digital divide: The disparity between those who have access to technology (including AI) and those who do not.
• digital literacy: The ability to understand, use, and interact effectively with digital technology, including AI.
• discrimination: The unjust or prejudicial treatment in AI systems, often as a result of bias in data or algorithms.
• distributive justice: The ethical principle concerned with the fair allocation of resources and benefits, and how AI might impact this.
• dual-use: The potential for AI technologies to be used for both beneficial and harmful purposes.
• duties of care towards artificial agents: The responsibilities humans have towards AI systems, especially as they become more complex and autonomous. (Related to robot rights; stronger than duties of nonmalificence)
• empathy in AI: The capability of AI to understand and respond to human emotions; particularly important for artificial virtuous agents or artificial moral agents.
• equality data: Data about individuals’ protected characteristics that are used to support equality, diversity, and inclusion.
• existential risks: The risk that AI could cause the extinction of humanity, either directly or indirectly.
• explainability: The ability to understand and articulate how an AI system works and makes decisions.
• fitness for purpose: Whether an AI system effectively accomplishes the task for which it was designed.
• harm to artificial agents: The concept of causing damage or detriment to AI systems and the ethical implications surrounding it.
• harm to memory (in natural agents vs artificial agents): The potential for AI to impact memory, either by changing human memory or through the manipulation of AI memory.
• human override: The ability for humans to intervene and override AI decisions.
• human rights Assessing the implications of AI on human rights.
• human-robot interaction: The study of interactions between humans and robots/AI systems, and the ethical implications of these interactions.
• inclusion and inclusivity: Ensuring diverse representation in the development and impact of AI systems, as well as including diverse perspectives and individuals in the development, deployment, and regulation of AI systems.
• integrity: The quality of being honest and having strong moral principles in the development and application of AI.
• intellectual humility: Recognizing and acknowledging the limits of our knowledge, particularly in the development and implementation of AI.
• legitimacy: The perception that an AI system is fair, just, and acceptable to its users and the public.
• machine ethics: The field of study focused on incorporating ethical principles into AI behavior.
• moral machine (AI – or other entities – as a moral machine): The concept that machines can potentially possess or mimic moral reasoning capabilities.
• neuroethics in AI: Ethical considerations regarding AI's impact on human cognition and neurological health.
• nonmalificence: The duty to refrain from intentionally causing harm.
• opacity & inscrutability & black box algorithms: The property of lacking transparency or explainability in AI decision-making processes, where it's not clear how a system arrived at its conclusion.
• openness = transparency: Making AI algorithms, data, and decision-making processes accessible and understandable to stakeholders and the public.
• personalization: Tailoring the responses of AI systems to individual users, thereby raising questions about privacy and autonomy.
• privacy: A central value in data protection, especially with regard to personal data.
• public participation: The involvement of the public in decision-making processes related to AI.
• reproducibility: The ability to duplicate the results of an AI system, aiding transparency and reliability.
• responsibility for harm caused by artificial agents: The question of who should bear the responsibility when an AI system causes harm.
• responsibility gaps: Situations where it's unclear who is responsible for the actions of an AI system.
• right to be forgotten = right to erasure: The right of individuals to have personal data erased, particularly relevant in the context of machine learning.
• robot rights = rights of artificial agents: The debate over what (if any) moral and legal rights should be granted to robots (or, more generally, AI systems), particularly as they become more complex and autonomous.
• sentience in AI: The debate around whether AI can achieve conscious awareness or feelings.
• social impact: The study of how AI influences society on a larger scale, such as job displacement due to automation.
• superintelligence: AI systems that greatly surpass human intelligence in most economically valuable work, raising significant ethical considerations.
• surveillance capitalism: The monetization of data collected through surveillance, often by AI technologies.
• technological unemployment: The potential loss of jobs due to automation and AI.
• techno-moral virtues: Virtues or moral qualities that are particularly important in the context of technology and AI, such as empathy and flexibility.
• trust: The reliability and integrity of AI systems that allow humans to have confidence in them.
• unintended findings: Unexpected or unintended outcomes or consequences from the use of AI.
• value alignment: Ensuring that AI systems' objectives and actions align with human values.
• virtue (in natural agents vs artificial agents): The comparison of moral virtues in humans and AI, and the potential for AI to exhibit or mimic virtuous behavior. Discussed in virtue ethics.
• vulnerability of AI: The susceptibility of AI systems to attacks or misuse, especially as related to safety (rather than a concern with harm to artificial agents.
• whistleblowing in AI: The act of reporting unethical practices in AI development and use.