ai-cultures.github.io

The Cultures in AI/AI in Culture workshop was a one-day virtual event held on December 9 as part of NeurIPS 2022. Its structure combined two named panel sessions—"AI Curating Culture" and "Cultures of AI"—with a reviewing process and a defined presentation format, and it was designed as a cross-disciplinary conversation pairing NeurIPS/ML researchers with experts in HCI and the social sciences. The page evidence confirms these components but does not list a full hour-by-hour agenda, so the details below reflect what the site actually documents.

Core Format at a Glance

The Two Panel Sessions

The workshop's intellectual structure is organized around a two-way relationship between AI and culture, and the panels mirror that split.

Panel 1: AI Curating Culture

This panel addresses the direction of AI shaping culture—how AI-driven tools for art, music, and language curate content and shape consumption patterns. If you are interested in recommendation systems, generative art, or content production tools, this is the session aligned with that interest.

Panel 2: Cultures of AI

This panel addresses the reverse direction: the cultures that shape AI production. The workshop description ties this to how human values and biases enter systems through annotation practices, training data pipelines, model design choices, and organizational incentives. Readers concerned with where bias comes from in model development will find this panel the closer match.

Reviewing and Presentation Format

The page lists both a "Reviewing" section and a "Presentation Format" section, which indicates the workshop ran a reviewed submission process and specified how accepted work was presented. The available page evidence names these sections but does not reproduce their contents—so specific review criteria, acceptance numbers, or talk versus poster allocations cannot be confirmed from the source. If you need those particulars, treat the original site sections as the authoritative place to check rather than assuming a format.

Who the Structure Was Built For

The format is explicitly cross-disciplinary. The workshop "brings together experts on AI development from the NeurIPS and ML community and places them in conversation with experts on human biases and cultural theories from HCI and the social sciences." The stated rationale is that foundational AI/ML research "lacks a deeper understanding of what culture is," and that culturally responsive systems require thinking about both the cultures shaping AI and the impact of AI on global cultures.

This matters for how you read the schedule: the panels are not parallel tracks for separate audiences but deliberate pairings of technical and social-science perspectives on the same question.

Practical Takeaways

• If you want the agenda structure: expect two themed panels plus reviewed presentations, delivered virtually on December 9.
• If you want to cite the workshop: the two panel titles and the AI–culture bidirectional framing are directly supported by the page.
• If you need session times, speakers, or review outcomes: these are not in the available evidence; consult the site's Reviewing and Presentation Format sections directly.

The site at ai-cultures.github.io is the official page for Cultures in AI/AI in Culture, a cross-disciplinary workshop held virtually on December 9 as part of NeurIPS 2022. It is not a software tool, dataset, or ongoing publication venue: it is a workshop record that explains the event's theme, program structure, and participation format. It is most useful to readers who want to understand the workshop's framing of the relationship between AI and culture, or who are looking for the agenda and format of that specific 2022 event.

What the workshop was about

The workshop's central premise is a two-way relationship:

• Culture shapes AI. Human values and biases enter AI systems through annotation practices, training data pipelines, model design choices, and larger organizational incentives. The organizers argue that AI/ML research often lacks a deep account of what culture is, which limits its ability to diagnose biases that development unconsciously globalizes.
• AI shapes culture. AI-driven tools for art, music, and language production curate content and shape consumption patterns, and AI-generated art in particular gives AI research enormous cultural impact.

The stated goal was to put NeurIPS and ML researchers in conversation with experts on human biases and cultural theory from HCI and the social sciences, so that culturally sensitive and responsive AI systems become easier to reason about.

Who it was for

The workshop was aimed at a mixed audience rather than a single discipline:

Program structure shown on the page

The page lists two panels and two participation details:

• Panel 1: AI Curating Culture — covers the direction in which AI tools select, generate, and shape cultural content.
• Panel 2: Cultures of AI — covers the direction in which the cultures of AI-producing institutions and communities shape the technology.
• Reviewing — a reviewing process is listed, indicating that submissions were evaluated.
• Presentation Format — the page specifies how accepted work was presented.

The page does not publish pricing, registration fees, or attendance requirements, so those details should not be assumed from the site alone.

How to use the page

1. Open ai-cultures.github.io and read the description section first; it states the workshop's motivation and scope in full.
2. Use the panel headings to see how the organizers split the AI–culture relationship into two directions.
3. Check the Reviewing and Presentation Format sections if you are trying to understand how the workshop was run rather than its intellectual content.

A practical limitation to keep in mind: this is a 2022 workshop page, so it documents a past event. If you need current calls for papers, deadlines, or recordings, this site is a starting reference rather than a live source, and you would need to check NeurIPS 2022 workshop materials or the organizers' later work.

The relationship matters because AI systems encode the cultural perspectives of the people and institutions that build them, and those encoded assumptions then spread at scale through content creation and recommendation tools. The Cultures in AI/AI in Culture workshop (a NeurIPS 2022 workshop, held virtually on December 9) frames this as a two-way problem: culture shapes AI, and AI reshapes culture. Understanding either direction is a prerequisite for diagnosing the biases that AI development may be globalizing without explicit intent.

Culture shapes AI in concrete, traceable ways

The workshop description identifies specific mechanisms through which human values and biases enter AI systems:

• Annotation practices — how labelers interpret and categorize data
• Training data pipelines — what gets included, excluded, or over-represented
• Model design choices — the assumptions built into architecture and objectives
• Larger organizational incentives — what institutions reward and prioritize

Because AI development and deployment "is shaped by the particular perspectives of the people and institutions it emerges from," a system's behavior is never culturally neutral. The workshop's stated concern is that without understanding the cultures shaping AI-driven technology, researchers "cannot fully respond to or diagnose the biases we are unconsciously globalizing through AI development."

AI shapes culture through creation and curation

The influence runs in the other direction as well. AI-driven technologies actively shape human experience through art, music, and language production tools that curate content and shape consumption patterns. As AI researchers become involved in designing systems with enormous cultural impact — AI-generated art is the example the workshop highlights — the stakes of getting the cultural dimension right increase.

Why foundational AI/ML research is the gap

The workshop's premise is that early conversations in the field have begun to question AI's impact on global cultures and the Western-centric cultural biases embedded in AI development, but that foundational AI/ML research "lacks a deeper understanding of what culture is and the ways in which it relates to AI research and development." The proposed remedy is cross-disciplinary: pairing NeurIPS and ML community experts with experts on human biases and cultural theories from HCI and the social sciences, so that researchers from outside machine learning can contribute perspectives and methods the ML community does not typically draw on.

What this means in practice

For a researcher or practitioner, the workshop's framing suggests a few checkable questions about any AI system:

1. Who built it, and what perspectives did they bring? The description treats this as a design input, not an afterthought.
2. Where do cultural assumptions enter the pipeline? Trace annotation, data collection, and design decisions rather than treating bias as a property of outputs alone.
3. What cultural effect does deployment have? Content generation and recommendation are named as the primary channels through which AI shapes consumption and experience.

The workshop's own answer to "why does this matter" is that producing culturally sensitive, responsive, and competent AI systems requires thinking about both the cultures shaping AI production and the impact those systems have on global cultures — and that this requires a richer definition of culture than ML research currently operates with.

The workshop centers on a two-way relationship: how culture shapes AI development, and how AI systems in turn shape culture. According to the workshop's own description, it is a cross-disciplinary event held virtually as part of NeurIPS 2022 on December 9, bringing together AI/ML researchers and experts in HCI and the social sciences. Its topics are best understood as four connected threads, described below.

How Culture Enters AI Systems

The workshop treats AI development as something shaped by the particular perspectives of the people and institutions behind it. The description identifies several concrete channels through which human values and biases become embedded:

• Annotation practices — the judgments of the people labeling data
• Training data pipelines — what data is collected and how it is processed
• Model design choices — decisions made during system construction
• Larger organizational incentives — the pressures and priorities of the institutions building AI

The stated concern is that foundational AI/ML research "lacks a deeper understanding of what culture is and the ways in which it relates to AI research and development." Without that understanding, the workshop argues, researchers cannot fully diagnose the biases being "unconsciously globalized" through AI development.

How AI Shapes Cultural Production and Consumption

The reverse direction receives equal attention. The workshop points to AI-driven tools for art, music, and language production that curate content and shape consumption patterns. AI-generated art is named specifically as an area of enormous cultural impact now that AI researchers are directly involved in designing such systems. This makes the question of AI's influence on global cultures a live research topic rather than a distant concern.

The Two Named Panels

The workshop's agenda is organized around two panels, which indicate where the discussion is concentrated:

The page lists these panel titles but does not enumerate individual speakers or talks in the available material, so specific presentations should be checked against the workshop site directly.

Why the Workshop Frames Culture Broadly

The description defines culture as "a set of worldviews, beliefs and social practices" — a deliberately broad framing. The stated goal is to complexify the field's understanding of culture, on the reasoning that producing "culturally sensitive, responsive, and competent AI systems" requires thinking about both the cultures shaping AI production and the effects those systems have on global cultures. The workshop positions itself as bridging the NeurIPS/ML community with outside expertise, noting that researchers from beyond machine learning "bring key perspectives and method[s]" that the ML community alone does not supply.

Practical Details for Assessing Relevance

• Format: virtual, held as part of NeurIPS 2022 on December 9
• Audience: cross-disciplinary — AI/ML researchers plus HCI and social science scholars
• Structure signals: the page also lists "Reviewing" and "Presentation Format" as sections, indicating a peer-reviewed submission process with defined presentation formats

If your interest is in bias and values in ML pipelines, content recommendation and generation, or the social-science side of AI, the workshop's scope overlaps directly. If you need the specific accepted papers, speaker lineup, or submission deadlines, those are not contained in the page excerpt above and should be confirmed on the workshop site itself.