Understanding “Democratization” in NLP and ML Research (2024)

Abstract: Recent improvements in natural language processing (NLP) and machine learning (ML) and increased mainstream adoption have led to researchers frequently discussing the “democratization” of artificial intelligence. In this paper, we seek to clarify how democratization is understood in NLP and ML publications, through large-scale mixed-methods analyses of papers using the keyword “democra*” published in NLP and adjacent venues. We find that democratization is most frequently used to convey (ease of) access to or use of technologies, without meaningfully engaging with theories of democratization, while research using other invocations of “democra*” tends to be grounded in theories of deliberation and debate. Based on our findings, we call for researchers to enrich their use of the term democratization with appropriate theory, towards democratic technologies beyond superficial access.

## introduction as the influence of language technologies has grown, it has become increasingly popular to discuss "democratization" in natural language processing (nlp) and machine learning (ml) research @xcite ; for instance, openai has invested in a "democratic process for deciding what rules ai systems should follow" @xcite , anthropic has explored how "democratic processes can influence artificial intelligence (ai) development" @xcite , and hug-gingface has stated their mission to be to "democratize good machine learning" @xcite . indeed, a large number of nlp and ml papers mention terms related to democracy (see figure 1 ), thereby raising the question: what do we understand by "democracy" and "democratization" when we invoke them in research? so far, the treatment of democracy in nlp and ml literature, and particularly the term "democratization," has not been subject to careful investigation. our paper fills this gap by analyzing uses of "democratization" in nlp and ml papers, and their connections to democracy. we examine conceptualizations of these terms through a large-scale mixed-methods analysis of every use of "democra*" in papers published in the anthology of the asso- ## data using the semantic scholar api @xcite , we collect all papers published before november 24, 2023 in the acl anthology, icml, iclr, and neurips, that mention terms related to "democracy." we choose these venues, as they are top-tier nlp and ml conferences that influence practices in the field. we obtain 1,537 papers, which we filter for relevance, obtaining a final dataset of 506 papers and 916 excerpts for analysis. ## conceptualizations of democracy to understand how democracy and democratization are conceptualized by authors in nlp and ml papers, we inductively analyze our data for overarching themes, values, and concepts. we find that conceptualizations of democratization are distinct from democracy, and instead are closely related to access and financial costs. ## democratization in nlp and ml given that democratization in nlp and ml is markedly different from the other themes, we examine the politics of democratization in papers with this theme. specifically, we consider what is being democratized, how, and to what end? ## engagement with democratic theories given such a lack of consideration within the democratization theme, we examine how nlp and ml papers engage with literature on democracy to understand its influence on the conceptualizations in section 4. we argue that discussing democracy or democratization without connecting to established theories reflects subpar interdisciplinarity and citational praxis, and risks misrepresenting how grounded ai is in democratic values. ## democratic theories and nlp and ml as democratization has had a long history of study starting from 1100 bce in ancient phoenicia @xcite @xcite , in this section, we consider select theories of democracy as a basis for how nlp and ml research has understood and operationalized democracy. we argue that these theories can provide foundations for more democratic nlp and ml technologies by making democratic discussions representative and efficient, diversifying forums for democratic dialogues, and dismantling barriers to participation in democratic processes. deliberative democracies deliberation and inclusion in the democratic process are often highlighted as goals for democratic societies @xcite and technologies @xcite . indeed, in our surveyed papers, democratic deliberation often appears as a goal (see section 4). deliberative democracy is a form of democracy that emphasizes processes where participants can debate a particular object (e.g., a policy or technology) on its merits and make collective decisions about its implementation @xcite . deliberative democratic theory thus provides an avenue for obtaining more legitimacy of decisions by engaging wider publics in conversation about the use and application of research artifacts @xcite . ## discussion, conclusion, and recommendations our thematic and large-scale mixed-methods analyses show that democracy is used in nlp and ml with infrequent operationalization of democratization, vastly different views of what democracy means, and low levels of interdisciplinary engagement. overall, our results show that when invoking democracy, nlp and ml researchers only shallowly engage with the centuries of literature from philosophy and social science devoted to it. it is thus necessary that nlp and ml researchers describe what they mean by and how they intend to operationalize democratization, to avoid misrepresenting public control of ai and bolstering "utopian-idealistic" ai hype @xcite . in particular, researchers should reflect on what values and concepts they associate with democratization, how their understanding of democratization may be contested, and how their usage of "democratization" may be overloaded or overhyped. we also echo @xcite call to simply use the word "access" rather than "normatively loaded language" like "democratization" when discussing access-related questions. researchers should go beyond "'access' as the sole condition for participation" @xcite and discuss the processes for "democratic oversight and control" of their artifacts @xcite . to this end, they should explicate the causes, targets, methods, and goals of democratization, what it means for their research to be fully democratic, and which opportunities and limits to public participation and control emerge. moreover, when invoking democracy and related concepts, researchers should detail how their understanding is informed by underlying theory and ensure to draw from and cite relevant literature. conversely, if it is not, they should explicitly indicate this in their work. in both cases, researchers should reflect on where their conceptualizations fail with respect to their research and goals, and which challenges remain unresolved by their work. for example, when invoking democratization, researchers should explicitly note what remains unresolved in their goal of democratized technologies. some efforts, e.g., openai's call for democratic inputs to ai @xcite and anthropic ai's collective intelligence project (2024), appear to engage more deeply with definitions and implications of democratic ai, yet do not critically examine questions of power and control. similarly, @xcite operationalizes ai democratization in line with democratic traditions, including "parliamentary processes to debate and regulate artificial intelligence." however, on the whole, we must urgently "reflect on [our] engagement with other fields" @xcite . while engagement with democratic theory is a necessary precondition for research towards democratizing nlp and ml technologies, it is also necessary to address the hegemonic praxis of nlp and ml, and how it begets or hinders democratic technologies.