As artificial intelligence (AI) rapidly advances, it becomes imperative to explore and comprehend its societal impact. Policymakers, opinion leaders, researchers, and the general public have some pertinent questions. How do biases influence automated decision-making? What are the implications of AI for jobs and the global economy? Should self-driving cars make moral judgments, and if so, how? How should we approach the ethical, legal, and social aspects of robots?
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There is growing concern about the consequences of widespread data access by governments, corporations, and other organizations, leading to invasive predictions about citizen behavior.
Central to all these questions is the issue of responsibility for AI systems' decisions and actions. Can we hold a machine accountable for its actions? What roles do humans play in researching, designing, building, selling, purchasing, and using these systems? Answering these inquiries necessitates a fresh perspective on socio-technical interactions, ethical considerations in intelligent systems, and new approaches to controlling and managing the autonomy of AI systems.
Understanding AI Ethics
The field of ethics revolves around questions concerning how people should act and what constitutes a fulfilling or "good" life. It comprises three primary areas: meta-ethics, applied ethics, and normative ethics.
Meta-ethics delves into the origins and meaning of ethical principles, seeking to understand the source and role of ethics, the influence of reason in ethical judgments, and universal human values.
Applied ethics involves the practical application of moral considerations to address specific controversial issues like euthanasia, animal rights, environmental concerns, and nuclear war. It increasingly focuses on the behavior of intelligent artificial systems and robotics, highlighting the importance of practical ethics in AI and technology.
Normative ethics defines the ideal situation by examining our value systems and distinguishing between right and wrong actions. It aims to create a framework of guidelines that govern human behavior. In the context of artificial systems, normative ethics plays a crucial role in comprehending and implementing ethical principles in their design.
AI ethics have emerged as a response to the growing concerns surrounding the impact of AI. Instances of harm resulting from both the misuse of technology and design flaws have increased, including psychometric voter manipulation, facial recognition surveillance, and mass data collection without consent. AI ethics is considered a new and evolving field within the broader scope of digital ethics, which addresses issues arising from new digital technologies like AI, big data analytics, and blockchain.
Pillars of AI Ethics
As AI progresses rapidly, the literature on AI ethics has introduced several terms and principles. A review in 2019 identified 11 ethical principles: transparency, justice, non-maleficence, privacy, responsibility, beneficence, dignity, freedom, trust, sustainability, and solidarity. However, there is some overlap among these principles, necessitating clarification. Therefore, engineering expertise intersects with ethical principles and examines six themes to apply ethics in engineering and AI systems: human agency and oversight, safety, privacy, transparency, fairness, and accountability.
- Human agency and oversight focus on individual and societal impact, addressing mental autonomy, meaningful and informed consent, and the societal implications for identity, belonging, and communities. It also considers the economic and environmental impact of AI.
- Safety emphasizes preventing harm and robustness against adversarial attacks. It addresses malicious use, reliability, and reproducibility, and plans to handle known and unknown risks.
- Privacy revolves around respecting personal information and giving informed consent. Data stewardship, minimization, and addressing the public-political and private-personal spheres are key aspects.
- Transparency is crucial for establishing trust and accountability. It involves the explainability of AI decisions, communication of capabilities and purposes, and open governance.
- Fairness, based on human equality, involves addressing bias, ensuring accessibility to AI benefits for all, and encouraging participation and diversity in AI development.
- Accountability is essential for ethical AI. Human oversight mechanisms, accountability for harms, and algorithmic impact assessments through impact assessments and technology auditing are crucial aspects.
Thus, the ethical imperative of human-centric AI calls for systems that advance well-being, human dignity, and flourishing. Implementing ethics in engineering and AI systems requires considering human agency, safety, privacy, transparency, fairness, and accountability. Through these efforts, the considerable benefits of AI can be harnessed while mitigating potential risks and harms, contributing to a more ethical and responsible AI ecosystem.
Ethical Dilemmas in AI
Emerging technologies such as AI, cloud computing, autonomous vehicles, big data, and cybersecurity hold immense potential in the current technological era. However, these advancements raise ethical concerns regarding data security and privacy that must be addressed before industry deployment. Ethical considerations involve principles such as autonomy, justice, beneficence, non-maleficence, and fidelity.
Ethical dilemmas faced by emerging technologies include:
- Data privacy: Protecting sensitive data from unauthorized access and misuse.
- AI risks: Assessing and mitigating potential harm caused by AI systems.
- Sustainability: Ensuring AI technologies promote environmental sustainability.
- Health implications: Evaluating the impact of AI on physical and mental well-being.
- Data weaponization: Preventing the unethical use of AI-powered technologies to manipulate information and public opinion.
Despite regulatory efforts, limited progress has been made in the ethical domain compared to technological advancements. Recent advances in AI governance focus on developing generalizable ethical decision frameworks that combine rule-based and example-based approaches to resolve ethical dilemmas.
Collecting data about various ethical dilemmas from people with different cultural backgrounds is essential to learn appropriate rules for ethical decision-making. Additionally, AI engineers must collaborate more with ethics and decision-making communities to leverage their expertise for ethical AI technologies.
Integrating ethics into AI curricula, including consequentialist, deontological, and virtue ethics, can guide AI researchers in prioritizing ethical considerations and shaping ethical human interactions. A global and unified AI regulatory framework is crucial to address ethical issues arising from AI technologies' impact on societies.
Different Approaches to AI Ethics
AI ethics can be approached from three main perspectives: principles, processes, and ethical consciousness.
The principles approach involves formulating guidelines to direct the use and development of AI systems. However, the principles are often vague, incongruent, and lack consensus, challenging their practical implementation.
The legislation approach aims to ensure the lawful development and deployment of AI technologies, but it faces nuanced concerns such as the need for new laws or updating existing ones, issues of jurisdiction, and challenges in translating common law traditions into automated systems. While biomedical ethics can be used as an illustrative example, there are differences (disanalogies) between biomedical and AI ethics due to the different contexts, relationships, and accountability mechanisms.
Processes for AI ethics focus on ethical-by-design, which includes interdisciplinary involvement, clear principles, and trade-offs during design to balance ethical considerations effectively. Governance in AI ethics encompasses technical and non-technical aspects, with technical governance focusing on accountability, transparency, and access, while non-technical governance addresses decision-makers, continuous education, and human-centric AI.
Ethical consciousness draws from business ethics and involves individuals, institutions, and cultural norms prioritizing moral awareness over economic or legal concerns. It encompasses codes of conduct, compliance, corporate social responsibility, and shifts in societal awareness of the ethical dimensions of AI.
Addressing AI Ethics Holistically
A comprehensive and holistic approach that combines principles, processes, and ethical consciousness is required to address AI ethics effectively. This will involve interdisciplinary collaboration, clear ethical guidelines, improved digital literacy, and continuous efforts to promote ethical decision-making in AI development and deployment.
Urgent action is necessary to tackle high-impact cases such as facial recognition, AI in health decisions, and biased algorithms. The debate on AI practices and governance should encompass considerations of business and labor practices, societal impacts, and ethical guidelines.
External accountability mechanisms are essential to ensuring the ethical use of AI and should be independent of companies. Ethics principles and governance methods complement each other, and a multi-level approach is crucial due to the complexity of data ecosystems.
Reference and Further Readings
- Paula Boddington. (2023). Artificial Intelligence: Foundations, Theory, and Algorithms. Springer. DOI: https://doi.org/10.1007/978-981-19-9382-4
- Kazim, E., and Koshiyama, A. S. (2021). A high-level overview of AI ethics. Patterns, 2(9), 100314. DOI: https://doi.org/10.1016/j.patter.2021.100314
- Tai MC. (2022). The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal, 32(4): 339-343. DOI: https://doi.org/10.4103/tcmj.tcmj_71_20
- Dhirani LL, Mukhtiar N, Chowdhry BS, and Newe T. (2023). Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review. Sensors. 23(3):1151. DOI: https://doi.org/10.3390/s23031151
- Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V., and Yang, Q. (2018). Building Ethics into Artificial Intelligence. arXiv. DOI: https://arxiv.org/pdf/1812.02953.pdf