The Values Encoded in Machine Learning Research (2106.15590v2)

Abstract: Machine learning currently exerts an outsized influence on the world, increasingly affecting institutional practices and impacted communities. It is therefore critical that we question vague conceptions of the field as value-neutral or universally beneficial, and investigate what specific values the field is advancing. In this paper, we first introduce a method and annotation scheme for studying the values encoded in documents such as research papers. Applying the scheme, we analyze 100 highly cited machine learning papers published at premier machine learning conferences, ICML and NeurIPS. We annotate key features of papers which reveal their values: their justification for their choice of project, which attributes of their project they uplift, their consideration of potential negative consequences, and their institutional affiliations and funding sources. We find that few of the papers justify how their project connects to a societal need (15\%) and far fewer discuss negative potential (1\%). Through line-by-line content analysis, we identify 59 values that are uplifted in ML research, and, of these, we find that the papers most frequently justify and assess themselves based on Performance, Generalization, Quantitative evidence, Efficiency, Building on past work, and Novelty. We present extensive textual evidence and identify key themes in the definitions and operationalization of these values. Notably, we find systematic textual evidence that these top values are being defined and applied with assumptions and implications generally supporting the centralization of power.Finally, we find increasingly close ties between these highly cited papers and tech companies and elite universities.

Overview of "The Values Encoded in Machine Learning Research"

The paper "The Values Encoded in Machine Learning Research" critically examines the underlying values present in the field of ML, particularly as reflected in research publications from premier conferences such as ICML and NeurIPS. The authors propose that, contrary to the often implicitly held belief that ML and technological advancements are value-neutral, the research in this field is inherently laden with specific values. By employing a rigorous annotation scheme applied to 100 influential ML papers, the paper dissects the extent to which these papers incorporate or overlook various societal values.

Methodological Approach

The paper introduces a novel annotation framework designed to extract value commitments from ML research documents, which include justifications for research, reflections on potential negative consequences, and affiliations. The authors analyze 100 highly-cited papers from ICML and NeurIPS over two periods: 2008-2009 and 2018-2019. The paper identifies and annotates key values reflected in these papers, such as performance metrics, generalization abilities, efficiency, and novelty. A diverse team conducted extensive qualitative and quantitative analysis to identify prevalent values and investigate ties to corporate and institutional influences.

Key Findings

1. Values in ML Research: The paper identifies 59 values frequently referenced in ML research. However, performance, generalization, quantitative evidence, efficiency, building on past work, and novelty are the most frequently cited. Ethical considerations, such as autonomy and justice, are conspicuously rare or absent entirely.
2. Justification and Negative Impacts: The vast majority of the papers (68%) focus exclusively on technical challenges without connecting their research to societal needs. Only a minuscule portion (1%) references potential negative implications of their work.
3. Institutional and Corporate Ties: The paper reports a marked increase in corporate affiliations, particularly from "big tech" companies, in recent highly-cited papers. These ties are mirrored by a decrease in explicit examinations of the broader societal implications of ML research.

Theoretical and Practical Implications

This paper challenges the assumption of value-neutrality in ML by demonstrating that current research is shaped by and perpetuates specific values. By systematically associating ML advances with performance and novelty at the expense of broader societal contexts or ethics, the field risks amplifying power imbalances and overlooking potential negative outcomes. The paper's findings have significant implications for ongoing debates around the ethical use of AI and the responsibilities of researchers in considering the broader impacts of their work.

Looking forward, these insights suggest a need for re-examining the prioritization of values within ML research. Paradigm shifts may be required to integrate more comprehensive evaluations of societal impacts and explore ethical frameworks alongside technical advances. Acknowledging and addressing these biases could lead to more equitable and just applications of ML technologies—facilitating a balance between technical innovation and societal well-being.

Conclusion

The paper "The Values Encoded in Machine Learning Research" serves as a critical reflection on the prevailing values within ML research as articulated through influential conference publications. It provocatively illustrates how such research prioritizes technical performance and speed, often at the expense of societal considerations. The authors advocate for increased awareness and intentionality in addressing these values, suggesting potential paths forward for the discipline to realign its objectives in concert with broader societal needs and ethical concerns. As machine learning systems continue to impact diverse aspects of society, this reflective process becomes paramount for fostering responsible and inclusive technological progress.