The Three Priorities
Adopted strategic objectives and action steps
The ASA Board of Directors adopted three strategic priorities at JSM 2026. Each priority includes a statement of rationale, a core objective, and concrete action steps with timelines.
Priority 1: Stakeholder Engagement & Advocacy
Stakeholder Engagement & Advocacy
Policymakers, industry, and the broader public do not well understand the statistics profession's unique contributions to responsible AI development. The distinctive value statisticians bring — principled uncertainty quantification, bias assessment, causal inference, robustness, reproducibility, data provenance, data quality, and validation — is precisely what responsible AI development most needs, and what the public least understands. This priority positions the ASA as an active, visible advocate for carrying the message that AI cannot be developed or deployed responsibly without statistical expertise.
- By December 31, 2026, the Board will identify members from key priority groups across policy, industry, funding, and the scientific community to form a task force.
- The task force will be charged with identifying target audiences and developing outreach strategies for each audience by JSM 2027.
- After Board review at JSM 2027, the ASA will implement the task force recommendations, using external expertise where needed — implementation to be completed within six months of JSM 2027.
Priority 2: Next-Generation Talent & Education
Next-Generation Talent & Education
Statistics training has not kept pace with where graduates are going or what the field needs. This priority highlights the need to leverage what makes statistical training distinctive. Teaching machine learning purely as a set of computational techniques, without a statistical lens, surrenders statistics' comparative advantage. Well-educated students will bring counterfactual reasoning and sensitivity to measurement that AI alone cannot replicate. The ASA will take concrete steps to shape how programs evolve, answering the question: What should a statistician entering the profession in the next decade be able to do?
- By JSM 2027, a task force convened by the ASA will develop and disseminate a white paper on statistics curriculum for the AI era. The curriculum will balance preparation in AI concepts and tools with an emphasis on statistical foundations, and offer concrete recommendations for leadership education in the ethical advancement and use of trustworthy AI across sectors.
- By end of 2027, the ASA will launch an outreach effort to encourage curriculum modifications consistent with the recommendations from the white paper.
Priority 3: Culture & Practices of Statistics
Culture & Practices of Statistics
Professional development matters — statisticians need new skills and fluency to engage confidently at the AI frontier. But culture is deeper than skills. It is about what the profession signals it values: in its publications, in its awards, in the language used to describe excellent work, and in how the ASA supports members navigating genuine uncertainty about their professional futures. The field needs to move from narrowly scoped methodological contributions toward end-to-end, system-level problem-solving.
- Develop certificate programs to help statisticians build key skills — including computational statistics, systems engineering, data architecture, and data governance.
- Revise the ASA's statement on the role of statistics in AI to more strongly reflect the contributions of the discipline.
- Develop technical communications that describe the practice of statistics as architects and guardians of AI technologies.
- Through journals, meetings, and awards, encourage statistical contributions to AI that combine methodological rigor with computation, empirical evaluation, and substantive impact.
- Develop recognition mechanisms to reward outstanding work in the use of statistics in AI.
How the five domains connect
All five domains, three strategic priorities
The Board's planning session explored five domains. The themes of Data Work and Infrastructure and Engaging with AI/ML Innovations are integrated into the three priorities — surfacing in the curriculum recommendations that address how students engage with AI/ML methods, in the advocacy campaign that positions statisticians as essential contributors to AI development, and in the professional development and culture work that equips members to engage at the technical frontier. The three priorities are not a narrowing of the conversation. They are an integration of it.