Wisdom usually refers to practical judgment, the capacity to bring knowledge to bear on values, uncertainty, competing interests, consequences, and the common good (Baltes & Staudinger, 2000; Sternberg, 1998). The idea has taken shape in many intellectual and cultural traditions. Cross-cultural studies place different weight on knowledge, experience, discretion, emotional balance, relational judgment, and cultivated conduct, and work on Asian, Middle Eastern, and South American traditions shows how varied these understandings are (Takahashi & Bordia, 2000; Yang & Intezari, 2019). For AI governance, this range changes the questions institutions ask. Relational perspectives draw attention to shared duties and effects on communities. Traditions that value discretion and restraint also make limits on deployment, delegation, and automation proper subjects of ethical review. AI and Wisdom begins from this wider landscape and keeps the central questions open: what should be pursued, under which conditions, according to which values, and with responsibility to whom.
Artificial intelligence began with a simple bet: that parts of intelligent behavior could be represented, tested, and eventually carried out by machines. That bet became a research program, and then a field (Turing, 1950; McCarthy et al., 2006). Today’s systems write, recognize patterns, predict, recommend, and participate in decisions across private and public institutions. They now shape work and judgment across organizational boundaries, with consequences for rights, accountability, transparency, safety, authority, and governance (UNESCO, 2021; OECD, 2024).
AI and Wisdom brings these traditions into one conversation, with a clear remit: AI ethics and AI governance. AI ethics examines the values and judgments that should guide how intelligent systems are built and used. AI governance examines the norms, institutions, rules, and decision structures through which authority and responsibility are organized. The journal studies how normative reasoning and institutional design inform one another across the AI lifecycle.
The journal focuses on the normative and institutional questions that arise during the development, deployment, evaluation, and governance of AI. These include responsibility, fairness, transparency, autonomy, safety, rights, accountability, regulation, standards, institutional design, oversight, and the allocation of authority. Researchers may examine these questions through particular technologies, organizations, jurisdictions, or cultural settings when AI ethics or AI governance remains the central subject.
That remit sets the journal’s boundary. We publish philosophical, empirical, legal, institutional, policy, and interdisciplinary work when the main problem concerns the ethics or governance of AI. Application domains can provide the setting for a study, while the object of inquiry remains the normative and institutional questions raised by AI. We want a venue where ethical reasoning and governance research sharpen one another, and where claims about responsible AI meet both argument and evidence.
AI ethics has moved quickly, from scattered declarations to a dense collection of principles, standards, and policy frameworks. High-level values often converge. Their interpretation, implementation, and enforcement still vary (Jobin et al., 2019). The practical question is how ethical commitments acquire institutional force through professional duties, governance mechanisms, and accountable decision processes. Responsible AI needs a credible route from principle to practice (Mittelstadt, 2019).
Governance deals with the norms, bodies, and mechanisms that organize risk, authority, obligation, and accountability. It connects technical design with ethical standards, organizational practice, and law, so these elements can support one another (Gasser & Almeida, 2017; Dafoe, 2023). International frameworks now apply this logic across the AI lifecycle, with requirements for oversight, accountability, transparency, robustness, and careful rollout (UNESCO, 2021; OECD, 2024).
We see ethics and governance as connected levels of inquiry. Ethics clarifies what is at stake, why a harm or benefit matters, and what responsibility can be defended. Governance asks who can act on those judgments, which rules and institutions carry them forward, how decisions can be reviewed or challenged, and whether the arrangement works in practice. Strong research makes this connection visible and examines both the principles and the mechanisms that put them to work.
We welcome research that treats AI ethics and governance as objects of inquiry and explains its method clearly. Possible approaches include normative and conceptual analysis, comparative legal and policy research, algorithmic and institutional audits (Raji et al., 2020), case studies of governance decisions, qualitative fieldwork, survey and experimental research, mixed methods, and comparisons across jurisdictions or cultural contexts. Methodological variety is useful when the method fits the question, the evidence is transparent, and the analysis supports a clear claim about ethical judgment or governance. The journal can accommodate particular topics and technologies while keeping AI ethics and governance at its center.
Peer review serves several related purposes: it checks methods and evidence, catches errors, tests inferences, improves arguments, and informs editorial judgment. Its institutional form has changed over time. Review now appears in anonymous and open formats, before and after publication, with software increasingly involved in the process. Independent scholarly scrutiny remains central to the quality and credibility of published work (Horbach & Halffman, 2018; Nature Portfolio, n.d.-b).
AI already supports parts of publishing, including plagiarism and compliance checks, reviewer matching, language assistance, triage, and experiments in review support (Checco et al., 2021). Large language models extend these uses to summarization, structured critique, literature comparison, reference checking, and draft review text. They also create risks, including fabricated references, generic criticism, bias, confidentiality breaches, and overconfident methodological advice (Hosseini & Horbach, 2023). At AI and Wisdom, any assisted evaluation must remain open to examination, challenge, verification, and attribution.
Peer review at AI and Wisdom rests on independent scholarly scrutiny and editorial responsibility. Technologies may support defined tasks within approved workflows. Reviewers and editors remain accountable for the assessments and decisions they make, and the journal will review its procedures as publishing practices evolve.
Transparency and authorship both depend on attributable responsibility. Authors should disclose significant AI involvement, identify the system and delegated task when it matters, and explain how key outputs were verified (International Committee of Medical Journal Editors [ICMJE], 2026; PLOS, n.d.). Current standards reserve authorship for people who accept responsibility for the accuracy, integrity, originality, approval, and defense of the work (ICMJE, 2026). Substantial machine contributions should remain visible through methods, contribution statements, provenance records, or supplementary materials.
Confidentiality requires clear boundaries. Unpublished manuscripts, reviewer reports, proprietary data, personal data, and sensitive material should stay inside journal-approved workflows unless confidentiality and data governance can be assured (ICMJE, 2026; Nature Portfolio, n.d.-a; PLOS, n.d.). Data handling, retention, access, and any AI-assisted editorial processing must follow clear safeguards throughout review.
Editorial independence must withstand technological and commercial pressure. AI research operates within a field shaped by proprietary models, uneven access to compute, platform dependence, investment, institutional prestige, and intense media attention. These forces can influence which problems appear urgent and which evidence looks impressive. AI and Wisdom will judge significance by evidence, reasoning, fit, and contribution. Market visibility, model scale, institutional name, and proximity to a hype cycle will not determine scholarly worth.
Ethical evaluation requires more than a checklist. Responsible AI scholarship must attend to context, trade-offs, power, participation, auditability, traceability, oversight, and an institution’s capacity to detect and correct harm (UNESCO, 2021). AI and Wisdom will assess ethical claims against their consequences, governance arrangements, affected communities, the allocation of responsibility, and the mechanisms available for correction.
Editorial responsibility also concerns which findings a journal makes visible and how readily published claims can be checked. AI and Wisdom welcomes well-designed null and negative findings, replication failures, failed interventions, and evidence that establishes boundary conditions. Preregistration is encouraged for confirmatory empirical studies. For empirical and computational research, reproducibility materials will normally be required, subject to legitimate ethical, legal, privacy, security, proprietary, and community-governance constraints. Authors should provide a research compendium containing the data, code, analytic materials, model details, and documentation needed to evaluate or reproduce reported results, or a transparent availability statement that explains any restricted access.
AI and Wisdom was founded to support a scholarly community committed to careful, plural, and accountable inquiry into AI ethics and AI governance. We invite work that tests the assumptions behind ethical claims, examines the consequences of governance mechanisms, and remains open to disagreement across disciplines, regions, and intellectual traditions. The journal brings normative judgment, institutional evidence, and responsibility into the same scholarly conversation.
A journal also participates in the governance of knowledge. The questions it makes visible, the evidence it rewards, and the standards it enforces help shape a field. AI and Wisdom will examine its own procedures with the same care it asks authors to bring to AI systems. We invite a community willing to debate difficult questions, report failure alongside success, correct its practices, and develop forms of governance that carry ethical commitments into action. We have always sought wisdom. This journal is one place to pursue that search together.