Building Ethics into Artificial Intelligence (1812.02953v1)

Abstract: As AI systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.

Building Ethics into Artificial Intelligence

The paper "Building Ethics into Artificial Intelligence" addresses the increasingly vital topic of integrating ethical decision-making frameworks within AI systems. As AI becomes an integral part of various sectors, the demand for ethical governance in AI has mounted. This paper differentiates itself by focusing primarily on technical solutions for AI governance, complementing existing discussions that are predominantly psychological, social, and legal.

Taxonomy of Ethical AI Governance

The authors propose a taxonomy dividing the field into four critical areas, each addressing a distinct aspect of ethical AI:

1. Exploring Ethical Dilemmas: This involves developing tools that allow AI systems to understand human preferences in ethical dilemmas. Examples include software solutions like GenEth and Moral Machine, which leverage human inputs to codify ethical principles. GenEth focuses on expert reviews, while Moral Machine takes advantage of crowdsourced data.
2. Individual Ethical Decision Frameworks: These frameworks are designed for single-agent systems, providing mechanisms for judging both its own actions and those of other agents under specific contexts. Approaches such as MoralDM combine rule-based reasoning with analogical reasoning to navigate ethical dilemmas. The use of structure mapping optimizes the process when data scales, offering a promising way to handle large sets of precedent cases.
3. Collective Ethical Decision Frameworks: This area extends the scope to multiple-agent systems, facilitating a collective consensus on ethical action. Social norm-based frameworks allow decentralized governance, bringing norms into decision-making processes. Meanwhile, the integration of preference aggregation models enriches collective decision-making capability.
4. Ethics in Human-AI Interactions: This dimension ensures that AI influences human behavior ethically, with frameworks designed to uphold principles such as autonomy, beneficence, and justice as established by the Belmont Report.

Key Insights and Implications

The paper provides critical insights into the technical methodologies underpinning ethical AI. It highlights concrete techniques like CP-net formalism for balancing endogenous agent preferences with ethical principles and layering of ethical shaping within reinforcement learning frameworks. These allow agents to dynamically adapt their actions within morally acceptable boundaries.

The implications of the research are profound. On a practical level, it supports the development of AI systems that can operate harmoniously with societal norms, thereby increasing trust and adoption. Theoretically, it propels the discourse on ethics in AI, encouraging interdisciplinary collaborations that bridge technology and philosophical insights.

Speculation on Future Developments

Looking to the future, several directions may emerge from this body of research. Firstly, expanding the data collection efforts similar to the Moral Machine project to incorporate a more diverse range of cultural contexts will be crucial. Transfer learning might play a role in modeling these varied ethical perspectives.

Secondly, there is a need for an adaptive and globally unified regulatory framework addressing AI ethics—a challenging area given rapid technological advancements and geopolitical variances. Collaborative efforts across disciplines will be essential.

The paper suggests integrating ethical reasoning directly into AI education and curricula, advocating for an increased emphasis on ethical principles alongside technical training. By enabling AI practitioners to understand diverse ethical frameworks, there is potential to advance the development of ethical AI technologies systematically.

In conclusion, "Building Ethics into Artificial Intelligence" serves as a technical roadmap for ethical AI integration, offering schemes critical for societal acceptance and effective deployment of AI systems. The paper provides a solid foundation, prompting further innovations and discussions in the field of AI ethics.