The Values Encoded in Machine Learning Research
Authors: Abeba Birhane
Pratyusha Kalluri
Dallas Card
William Agnew
Ravit Dotan
Michelle Bao
Venue: NeurIPS 2021 Submitted (2021)
Keywords: values
machine learning
justification
negative consequences
corporate affiliations

Abstract: Machine learning (ML) currently exerts an outsized influence on the world, increasingly affecting communities and institutional practices. 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 present a rigorous examination of the values the field advances by quantitatively and qualitatively analysing 100 highly cited ML papers published at premier ML conferences, ICML and NeurIPS. We annotate key features of papers which reveal their values: how they justify their choice of project, which aspects they uplift, their consideration of potential negative consequences, and their institutional affiliations and funding sources. We find that societal needs are typically very loosely connected to the choice of project, if mentioned at all, and that consideration of negative consequences is extremely rare. We identify 63 values that are uplifted in these papers, and, of these, we find that papers most frequently justify and assess themselves based on performance, generalization, efficiency, researcher understanding, novelty, and building on previous work. We present extensive textual evidence and analysis of how these values are concretized. Notably, we find that each of these top values is 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.

29 To this end, we perform comprehensive analysis of 100 highly cited NeurlPS and ICMI. papers The Values Encoded in Machine Learning Research
Anonymous Author(s) Affiliation Address
email
Abstract
Machine learning (ML) currently exerts outsized influence the world, in- creasingly affecting communities and institutional practices. 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 present a rigorous examination of the values the field advances by quantitatively and qualitatively analysing 100 highly cited ML papers published at premier ML conferences, ICML and NeurIPS. We annotate key features of papers which reveal their values: how they justify their choice of project, which aspects they uplift, their consideration of potential negative consequences, and their institutional affili- ations and funding sources. We find that societal needs are typically very loosely connected the choice of project, if mentioned at all, and that consideration of negative consequences is extremely rare. We identify 67 values that are uplifted in these papers, and, of these, we find that papers most frequently justify and assess themselves based on performance, generalization, efficiency, researcher understand- ing, novelty, and building on previous work. We present extensive textual evidence and analysis of how these values are concretized. Notably, we find that each of these top values is 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.
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Introduction
Over the past few decades, MI has risen from relatively obscure research area an extremely influential discipline, actively being deployed in myriad applications and contexts around the world. The objectives and values of ML research are influenced by many f'ccoors, including the personal preferences of researchers and reviewers, other work in science and engineering, the interests 25 of academic institutions, funding agencies, and companies, and larger institutional and systemic
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26 pressures, including systems of oppression impacting who is able to do research. Together these
27 forces influence what research gets done and who benefits from this research. As such, is important
28 to document and understand the values of the field: what the field is prioritizing and working toward.
30 from four recent years spanning more than a decade.
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Our key contributions
follows:
32 (I) We develop a fine-grained annotation scheme for the detection of values in research papers, including identifying list of 67 values uplifted in ML research. To our knowledge, our annotation
34 scheme is the first of its kind, and opens the door to further qualitative and quantitative analyses.
35 (2) We use our annotation scheme to annotate 100 influential papers and extract their value commit-
36 ments, which reflect and shape the values of the field more broadly. Like the annotation scheme itself,
Submitted to 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Do not distribute.

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37 the resulting repository of annotated papers is valuable not only in the context of this paper, but also
38 as foundation for further qualitative or quantitative research
39 (3) We perform extensive textual analysis to understand some of the dominant values: performance, accuracy, otate-of-the-art (SOTA), quantitative results, generalization, efficiency, building on previous work, and novelty ($ ($ Our analysis indicates that while these values may seem on their face to be purely technical, they are nevertheless socially and politically charged: specifically, we argue that these values are defined and operationalized in ways that centralize power, i.c., disproportionally benefit and empower the already powerful, such as large corporations, while negatively impacting society's s least advantaged.
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(4) We present quantitative analysis of the affiliations and funding sources of these most influential 46
papers ()).. We find substantive and increasing presence of big tech corporations. For example, in 2008/09, 24% of these top cited papers had corporate affiliated authors, and in 2018/19 this statistic
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2 Methodology
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To understand the values of ML. research, we examined the most highly cited papers from NeurlPS 53 and ICML from the years 2008, 2009, 2018, and 2019. We chose to focus on highly cited papers
54 because they reflect and shape the values of the discipline, drawing from NeurlPS and ICML because
55 they the most prestigious of the long-running ML conferences Acceptance to these conferences
56 is a valuable commodity used to evaluate researchers, and submitted papers are explicitly written
57 so to win the approval of the community, particularly the reviewers who will be drawn from that
58 community. As such, these papers effectively reveal the values that authors believe are most valued
59 by that community. Citations largely indicate the approval of the community, and help to position these papers as influential exemplars of ML research. To avoid detecting only short-lived trends and enable comparisons over time, we drew papers from two recent years (2018/19) and from ten years earlier (2008/09). We focused on conference papers because they tend to follow standard format and allow limited space, meaning that researchers must make hard choices about what to emphasize. Collectively, we annotated 100 papers, analyzing over 3,500 sentences drawn from them. In the context of qualitative content analysis, this is a significant effort which allows us to meaningfully comment on the values central to ML.
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67 In more detail, we began by creating an annotation scheme (see below), and then used it to manually annotate each paper. examining the abstract, introduction, discussion, and conclusion: (1) We examined the chain of reasoning by which each paper justified its contributions, which we call the justificatory chain, rating the extent to which papers used technical or societal problems to justify or motivate their contributions. (2) We carefully read the text of these sections, individually annotating
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any and all values from our list that were uplifted or exhibited by each sentence Fl (3) We documented
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73 the extent to which the paper included a discussion of potential negative impacts.
74 Manual annotation was necessary, both to create the list of values, and to obtain and understand
75 the values present in each paper. Automated approaches, such as keyword searches, would run
76 the risk of systematically skewing the results towards values which are easy to identify, potentially
77 missing or mischaracterizing values which are exhibited in more nuanced ways, or those which were
78 not anticipated. The qualitative approach was key for analyzing the values as well, as it requires a
79 subtle understanding of how the values function in the text and understanding of taken for granted assumptions underlying the values, which methods such as keyword matching would fail to capture.
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81 To assess consistency, 40% of the papers were annotated by two annotators. The intercoder consensus
82 on values in these papers achieved a Cohen kappa coefficient of 61%, which indicates substantial agreement 39 Furthermore, we used several established strategies increase consistency, including
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We include our full of annotations as supplementary material, along with CC BY-NC-SA license. 2 At the time of writing, these two venues, along with ICLR (2013-presen), comprised the top 3 conferences according to index (and h5-mediam in the Al category on Google Scholar, by large margin.
"value" that widespread in philosophy of science in theorizing about values in sciences. In this approach, a value of an entity is a property that is desirable for that kind of entity. For example, speed can be described as valuable in an antelope 28 Well-know scientific values include accuracy. consistency, scope, simplicity, and fruitfulness 25
'We use conceptualization
See 27 for critical discussion of these values.

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Aumte coe becsts anseeie tot bubll sxcrest
Saciaiy Perrad
Connuations
Lontrace
Wirto Soose
taarsss be Recimeetbene cherss
Dnstritutee
(tares)
Rercs Decrat)
Figure 1: Proportion of annotated papers that uplifted each value.
84 recoding data coded early in the process
23 and conducting frequent discussions and assessments of
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the coding process, code list, and annotation scheme 24
86 To create the list of values (see Figure D we followed best practices in manual content analysis. (I)
We began with list of values we expected to be relevant based on prior knowledge, augmenting this list with seven ethical principles from existing literature [ 16 (2) We randomly selected a subset of 10 papers for initial annotation, searching for the values on the list sentence by sentence and adding new values as needed. (3) Through discussion, we revisited all values and produced a final list. (4) We annotated the full set of papers using this list of values, meeting regularly to discuss difficult examples. (5) For the final analysis presented here, we combined some related values into clusters (via consensus), such that they could be discussed together (but are treated separately in the appendix). Formally stated, we establish our codes (short phrases that represent the relevant essence of information, in this case the list of values) using the an inductive-deductive approach. The deductive component involves starting with codes established in existing literature, which ensures we note and can speak to values of interest, including established ethical principles. The inductive component involves the discovery of codes from the data, and impedes inappropriately biased or pre- concevede findings by focusing on emergent codes
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The composition of our team also confers validity to our work. We are a diverse team, including undergraduate, graduate, and post-graduate researchers from machine learning, NLP, robotics, cogni- tive science, and philosophy. This diversity minimizes intra-disciplinary biases, affords the unique combination of expertise required read the values in ML papers, allows meaningful engagement with relevant work in other fields, and enables best practices including continually clarifying the procedure, ensuring agreement, vetting consistency, reannotating, and discussing themes [24] Other methods of manual annotation, such as crowd sourcing, lack these ad advattgges.
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Quantitative Summary
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In Figure we plot the prevalence of values in 100 annotated papers. The top values are: performance (87% of papers), building on past work (79%), generalization (79%), efficiency (73%), quantitative evidence (72%), and novelty (63%). Values related to user rights and stated in ethical principles appeared very rarely if at all: none of the papers mentioned autonomy, justice, or respect for persons. In Table (top). we show the distribution of justification scores. Most papers only justify how they achieve their internal, technical goal; 71 % don't make any mention of societal need or impact, and only 3% make what we considered to be rigorous attempt at connecting their research socictal needs. In Table | (bottom), we show the distribution of negative impact discussion scores. One annotated paper included a discussion of negative impacts and second mentioned the possibility: none of the remaining 98 papers contained any reference to potential negative impacts. In Figure 3
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we show stated ties (funding and affiliations) of paper authors to different institutions. Comparing
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papers written in 08/09 to those authored 18/19, ties to corporations nearly doubled to 79% of all annotated papers, ties to big tech multiplied over fivefold to 58%, while ties to universities declined
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to 81% % putting corporations nearly on par with universities in the most cited ML. research. In the
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next sections, we present extensive qualitative examples and analysis of our findings, with additional analyses in the Appendix.
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Table 1: Annotation scheme and results for justificatory chain (top) and negative impacts (bottom).
Justificatory Chain Condition
% of Papers
Doesn't t rigorously justify how it achieves technical goal Justifies how it achieves technical goal but no mention of societal need States but does not justify how it connects to societal need States and somewhat justifies how it connects to a societal need States and rigorously justifies how it connects to a a societal need
1%
71%
16%
9%
3%
Negative Impacts Condition
% of Papers
Doesn't i mention negative potential
98%
Mentions but does not discuss negative potential
1%
Discusses negative potential
1
Deepens our understanding of negative potential
0%
Qualitative Analysis of Justifications and Negative Potential
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4.1
Justificatory Chain
Papers typically motivate their projects by appealing to the needs of the ML research community, but rarely mention potential societal benefits. Research-drivee needs of the ML community include researcher understanding (e.g., understanding the effect of pre-training theoretically understanding multi-layer networks) as well as more practical research problems (e.g.. improving efficiency of models for large datasets, creating new benchmark for NLP tasks). Some papers do appeal to needs of the broader society, such as building models with realistic assumptions, catering to more languages, or understanding the world. However, even when societal needs are mentioned as part of the justification of the project, the connection is often loose. Almost no papers explain how their project is meant to promote a social need they identify by giving the kind of rigorous justification that is typically expected of and given for technical contributions.
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performaaneerrobustness..
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4.2
Negative Potential
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Two of the 100 papers discussed potential harms, whereas the remaining 98 did not mention them all. The lack of discussion of potential harms is especially striking for papers which deal with socially contentious application areas, such as surveillance and misinformation. For example, the annotated corpus includes a paper advancing the identification of people in images, a paper advancing face-ssapping and paper advancing video synthesis. These papers contained no mention of the stul-st negative potential of facial surveillance, DeepFakes, or misleading videos, respectively.
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Furthermore, among the two papers that do mention negative potential, the discussions were mostly abstract and hypothetical, rather than grounded in the negative potential of their specific contributions. For example, authors may acknowledge "possible unwanted social biases" when applying the model real-world setting, without discussing the social biases encoded in the authors' proposed model.
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Stated values
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The dominant values in ML research, e.g., accuracy or efficiency, may seem purely technical. However, the following analysis of several of these values shows how they can become politically loaded in the process of prioritizing and operationalizing them: sensitivity to the way that they are operationalized, and the fact that they are uplifted all, reveals value-laden assumptions that
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are often taken for granted and may negatively impact the broader society fl We thus challenge a conception of prevalent values as politically neural by considering alternatives to their dominant conceptualization that may be equally or more intellectually interesting or more socially beneficial. We have encouraged ourselves, and now encourage the reader, to remember that values once held to be intrinsic, obvious, or definitional have been in many cases transformed over time.
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Siiilar points have been made by philosophers of science in the context of the natural and social sciences
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Table 2: Random examples of performance, the most common emergent value.
"Our model significantly outperforms SVM's, and also outperforms convolutional neural nets when given additional unlabeled data produced by small translations of the training images." "
"We show in simulations on synthetic examples and on the IEDB MHC-I binding dataset, that our approach outperforms well-know convex methods for multi-task learning, as well as related non-convex methods dedicated to the same problem."
"Furthermore, the learning accuracy and performance of our LGP approach will be compared with other important standard methods in Section 4, e.g., LWPR [8]. standard GPR [1], sparse online Gaussian process regression (OGP) [5] and u-support vector regression (u-SVR) [11], respectively."
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To provide a sense of what the values we discuss look like in context, we include three randomly selected examples of sentences annotated for each (Tables 2-5), with additional examples in the Appendix. Note that most sentences are annotated with multiple values, but this is not shown here
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5.1 Performance
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Performance, accuracy, and achieving SOTA form the most common cluster of related values in annotated papers. While it might seem intrinsic for the field to care about performance, it is important to remember that models are not simply "well-performing" or "accurate" in the abstract but always in relation to and as quantified by some metric on some dataset. Examining prevalent choices of operationalization reveals political aspects of performance values. First, we find that performance values are consistently and unquestioningly operationalized as correctness averaged across individual predictions, giving equal weight to each instance. However, choosing to use equal weights when averaging is a value-laden move which might deprioritize those underrepresentaaed in the data or world, as well as societal and evaluee needs and preferences. Extensive research in ML fairness and related fields has considered alternatives, but we found no such discussions among the most-cited papers we examined.
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Choices of datasets are revealing. They are often driven purely by past work, so as to demonstrate improvement over a previous baseline (see also $5.4p. Another common justification for using a certain dataset is applicability to the "real world". Assumptions about how to characterize the real world may also be value-laden. One common assumption is the availability of very large datasets. However, presupposing the availability of large datasets is power centralizing because it encodes favoritism to those with resources to obtain and process them [15]. Further overlooked assumptions include that the real world is binary or discrete, and that datasets come with a predefined ground- -tuth label for each example, presuming that a true label always exists "out there" independent of those carving it out, defining and labelling it. This contrasts against marginalized scholars' calls for ML models that allow for non- -onaaiiass,,, plural truths, contextual truths, and many ways of being
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The prioritization of performance values also requires scrutiny. Valuing these properties is so entrenched in the field that generic success terms, such as "success", "progress", or "improvement" are often used as synonyms for performance and accuracy. However, one might alternatively invoke generic success to mean increasingly safe, consensual, or participatory ML that reckons with impacted communities and the environment. fact, "performance" itself is general success term that could have been associated with properties other than accuracy and SOTA.
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5.2 Generalization
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A common way of appraising the merits of one's work in ML is to claim that it generalizes well. Typically, generalization is understood in terms of performance or accuracy: model generalizes when it achieves good performance on a range of samples, datasets, domains, or applications. Uplifting generalization raises two kinds of questions. First, which datasets, domains, or applications show that the model generalizes well? Typically, a paper shows that a model generalizes by showing that it performs well on multiple tasks or datasets. However, the choice of particular tasks and datasets is
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'To avoid the impression that there is anything unusual or special about these randomly chosen example sentences, omit attribution, but include list of all annotated papers in the Appendix.
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Table 3: Random examples of generalization, the third most common emergent value.
"The range of applications that come with generative models are vast, where audio synthesis [55] and semi sumi-supervised classification [38. 31, 44] are examples hereof."
"Furthermore, the infinite limit could conceivably make sense deep learning, since vver-parametrization seems to help optimization a lot and doesn't 't hurt generalization much |Zhang et al., 2017]: deep neural nets with millions of parameters work well even for datasets with 50k training examples."
"Combining the optimization and generalization results, we uncover a broad class of learnable functions, including linear functions, two-layer neural networks with polynomial activation <(z) = x² or cosine activation, etc."
Table 4: Random examples of efficiency, the fourth most common emergent value.
"Our model allows controllable yet efficient generation of an entire news article not just the body, also the title, news source, publication date, and author list."
"We show that Bayesian PMF models can he efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset, which consists of over 100 million movie ratings."
"In particular, our EfficientNet-B7 surpasses the best existing GPipe accuracy (Huang et al., 2018), but using 8.4x fewer parameters and running 6. faster on inference."
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rarely justified; the choice of tasks can often seem arbitrary, and authors rarely present evidence that their results will generalize to more realistic settings, or help to directly address societal needs.
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Second, uplifting generalization itself reveals substantive assumptions. The prizing of generalization means that there is an incentive to harvest many datasets from a variety of domains, and to treat these the only datasets that matter for that space of problems. Generalization thus prioritizes distilling every scenario down to a common set of representations or affordances, rather than treating
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each setting as unique. Critical scholars have advocated for valuing context, which stands at the opposite side of striving for generalization [14. Others have argued that this kind of totalizing lens (in which model developers have unlimited power to determine how the world is represented) leads
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to representational harms, due to applying a single representational framework to everything 13
Finally, the belief that generalization is even possible implicitly assumes conservative approach in which new data will be sufficiently similar to previously seen data. When used in the context of MI. the assumption that the future resembles the past is also normative and often problematic as past societal stereotypes and injustice can be encoded in the process 33.. Furthermore, to the extent that predictions are performative (35). especially predictions that are enacted, those ML models which deployed to the world will contribute to shaping social patterns. Yet, no papers attempt to counteract this quality or acknowledge its presence.
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5.3 Efficiency
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Efficiency is another common value in ML research. Abstractly, saying that a model is efficient typically means saying that the model uses less of some resource, such as time, memory, energy, or number of labeled examples. In practice however, efficiency is commonly referenced to imply scalability: more efficient inference method allows you do inference in much larger models or
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on larger datasets, using the same amount of resources. This is reflected in our value annotations, where 72% of papers mention valuing efficiency, but only 14% of those value requiring few resources. In this way, valuing efficiency facilitates and encourages the most powerful actors to scale up their computation to ever higher orders of magnitude, making their models even less accessible those without resources to use them and decreasing the ability to compete with them. Alternative usages of efficiency could encode accessibility instead of scalability, aiming to create more equitable conditions
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for ML research.
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Novelty and Building on Past Work
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Most authors devote space in the introduction to positioning their paper in relation to past work, and describing what is novel. Mentioning past work serves to signal awareness of related publications, to

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Table 5: Random examples of building on past work and novelty, the second and sixth most common emergent values, respectively.
Building on past work
"Recent work points towards sample complexity possible reason for the small gains in robustness: Schmidt et al. [41] show that in a simple model, learning classifier with non-trivial adversarially robust accuracy requires substantially more samples than achieving good 'standard' accuracy."
"Experiments indicate that our method is much faster than state of the art solvers such as Pegasos, TRON, SVMperf, and a recent primal coordinate descent implementation."
"There is a large literature on GP (response surface) optimization." "
Novelty
"In this paper, we propose video-to-video synthesis approach under the generative adversarial learning framework." "
"Third, we propose a novel method for the listwise approach, which call ListMI.F."
"The distinguishing feature of our work is the of Markov chain Monte Carlo (MCMC) methods for approximate inference in this model."
Toch AMlllatio other Allilaatonn
58%
77%
Corporate Aetllation
13%
00-09
18/19
Figure 2: Corporate and Big Tech author affiliations.
establish the new work as relevant to the community, and to provide the basis upon which to make 228
claims about what is new. Novelty is sometimes suggested implicitly (e.g.. "we develop" or "we propose"), but frequently it is emphasized explicitly (e.g. "a new algorithm" or "a novel approach") ").
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This combined focus on novelty and building on recent work establishes a continuity of ideas, and might expected to contribute to the self-correcting nature of science (2)]. However, this is not always the case [21 and attention to the ways novelty and building past work are implemented reveals value commitments. In particular, we find clear emphasis on technical novelty, rather than critique of past work, or demonstration of measurable progress societal problems, has previously been observed 40. Although introductions sometimes point out limitations of past work (so to further emphasize the contributions of their own paper), they are rarely explicitly critical of other papers terms of methods or goals. Indeed, papers uncritically reuse the same datasets for years decades to benchmark their algorithms, even if those datasets fail to represent more realistic contexts in which their algorithms will be used [6]. Novelty is denied to work that rectifies socially harmful aspects of existing datasets in tandem with strong pressure benchmark them and thereby perpetuate their use, enforcing a fundamentally conservative bent to ML. research.
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Corporate Affiliations and Funding
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Our analysis shows substantive and increasing corporate presence in the most highly-cited papers. In 2008/09, 24% of the top cited papers had corporate affiliated authors, and in 2018/19 this statistic almost tripled, to 71%. Furthermore, we also find much greater concentration of few large tech firms, such as Google and Microsoft, with the presence of these "big tech" firms 4 increasing
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more than fivefold, from 11 % to 58% (see Figure 2). The number of most influential papers with corporate ties, by author affiliation or funding, published dramatically increased from 43% in 2008/09
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to 79% in 2018/19. In addition, we found paramount domination of elite universities in our analysis
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as shown in Figure S Of the total papers with university affiliations, we found 82% were from clite universities (defined as the top 50 universities by QS World University Rankings, following
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Percent of Papers
Deepmind
80
Facebook
60
Nvidia
Microsoft
Google
20
09-99910-19
10-0011--19 Big Tech
00--9611-159 Military
18--0918-11 Nonprofit
18-091e-19
00-0911-19 08-09h8-19
n0 19-0911-11 108-08188155
Research Agencies Institute
University
Elite University
Company
University
Figure 3: Corporate affiliations and funding ties. Non-N.A. Universities are those outside the U.S. and Canada.
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past work [!.. These findings are consistent with previous work indicating a pronounced corporate presence in MI. research. In analysis of 171, ,3394 peer-reviewed papers from 57 major computer science conferences, Ahmed and Wahed ( show that the share of papers that have at least one
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corporate affiliated co-author increased from 10% in 2005 for both ICML and NeurIPS to 30% and
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35% % respectively in 2019. Our analysis shows that corporate presence is even more pronounced in
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those papers from ICML and NeurIPS that end up receiving the most citations.
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The influence of powerful players in ML research is consistent with field-wide value commitments that centralize power. Others have also argued for causal connections. For example, Abdalla and Abdalla 2 argue that the strategies that big tech uses to sway and influence academic and public discourse, closely resemble that of Big Tobacco. Moreover, examining the prevalent values of big tech, critiques have repeatedly pointed out that objectives such as efficiency, scale, and wealth accumulation 33 T9 drive the industry large, often the expense of individuals rights, respect for persons, consideration of negative impacts, beneficence, and justice. The top stated values of MI. that we presented in this paper such as performance, generalization, and efficiency not only enable and facilitate the realization of big tech's objectives, they also suppress values such as beneficence, justice, and inclusion. "state-of-the-an
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large image dataset, for example, is instrumental for large scale models, further benefiting ML researchers and big tech in possession of huge computing power large image dataset that considers negative consequences and is built on the foundations of individual rights and respect for persons, on the other hand, is one that would start with gaining informed consent from the data subject and is considerate of contextual norms over scalability [19]. However, in the current climate where values such as efficiency and scale are priority, informed consent is perceived costly and time consuming, evading social needs.
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Discussion
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ML research is often perceived as value-neutral, and emphasis is placed on positive applications or potential. This fits into a historical strain of thinking which has tended frame technology as "neutral", based on the notion that new technologies can be unpredictably applied for both beneficial and harmful purposes 4 Ironically, this claim of neutrality frequently serves as an insulation from critiques of AI and as a permission to emphasize the benefits of Al 38, 41
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Although it is rare to see anyone explicitly argue in print that ML is neutral, related ideas are part of contempo- rary conversation, including these canonical claims: long term impacts are too difficult to predict; sociological impacts are outside the expertise or purview of ML researchers 20); critiques of Al expertis are really misdirected critiques of those deploying AI with bad data ("garbage in, garbage out"), again outside the purview of many AI researchers; and proposals such as broader impact statements represent merely "bureaucratic constraint" [ A recent qualitative analysis of broader impact statements from NeurIPS 2020 similarly observed that these statements leaned towards positive consequences (often mentioning negative consequences only briefly and in some cases not at all), emphasized uncertainty about how a technology might be used, or simply omit any discussion of societal consequences altogether B1
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Importantly, there foundational understanding in Science, Technology, and Society Studies (STSS), Critical Theory, and Philosophy of Science that science and technologies are inherently value-laden, and these values are encoded in technological artifacts, many times contrast field's formal research criteria, espoused consequences, ethics guidelines 44 10 g
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There is a long

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tradition of exposing and critiquing such values in technology and computer science. Foundationally, Winner 44 introduced several ways technology can encode political values. This work is closely
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related to Rogaway 37 who notes that cryptography has political and moral dimensions and argues
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for a cryptography that better addresses societal needs. Weizenbaum 42 argued in 1976 that the
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computer has from the beginning been fundamentally conservative force which solidified existing
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power. In place of fundamental social changes, the computer renders technical solutions that allow existing power hierarchies to remain intact.
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Our paper extends these critiques to the field of ML. It is a part of a rich space of interdisciplinary critiques and alternative lenses used to examine the field. Works such as BC 9
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critique AI, ML, and data using decolonial lens, noting how these technologies replicate colonial power relationships
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and values, and propose decolonial values and methods. Others B B2 14 examine technology and
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data science from an anti-racist and intersectional feminist lens, discussing how our infrastructure has largely been built by and for white men; D Ignazio and Klein [14] present a set of alternative principles and methodologies for an intersectional feminist data science. Similarly, Kalluri (2)
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denotes that the core values of ML are closely aligned with the values of the most privileged and outlines a vision where ML models are used to shift power from the most to the least powerful. Dotan and Milli [SI argue that the rise of deep learning is value-laden, promoting the centralization of power among other political values. Many researchers, as well as organizations such as Data for Black Lives, the Algorithmic Justice League, Indigenous AI, Black in AI, and Queer in AI, work on continuing to uncover particular ways technology in general and ML in particular can encode and amplify racist, sexist, queerphobic, transphobic, and otherwise marginalizing values ||] 46].
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We present this work part in order to expose the contingency of the present state of the field; it could be otherwise. For individuals, communities, and institutions wading through difficult-to- to--in-doww values of the field, as well as those striving toward alternative values, it is a useful tool to have a characterization of the way the field is now, for understanding, shaping, dismantling, or transforming what is, and for articulating and bringing about alternative visions.
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As with all methods, our chosen approach (careful reading of important sections of highly-cited papers) has limitations. Most notably, this approach does not automatically scale or generalize to other data, which limits our ability to draw strong conclusions about other conferences or different years. Similarly, this approach is less reproducible than fully automated approaches, and for both
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our final list of values and specific annotation of individual sentences, different researchers might make somewhat different choices. However, given the overwhelming presence of certain values, the high agreement rate among annotators, and the similarity of observations made by our team, we strongly believe other researchers taking a similar approach would reach similar conclusions about
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what values are most frequently uplifted by the most influential papers in this field. Lastly, we cannot claim to have identified every relevant value in ML. However, by including important ethical values identified by past work, and specifically looking for these, we can confidently assert their relative absence in this set of papers, which we take to be representative of influential work in ML.
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Conclusion and Future Work
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We reject the vague conceptualization of the discipline of ML as value-neutral. Instead, we argue that the discipline of ML is inherently value-laden. Our analysis of highly influential papers in the discipline shows that the discipline not only favors the needs of research communities and large firms over broader social needs, but also that it takes this favoritism for granted. The favoritism manifests in the choice of projects, the lack of considcration of potential negative impacts, and the prioritization and operationalization of values such as accuracy, generalization, efficiency, and novelty. All of these overwhelmingly disfavor socictal needs, usually without any discussion or acknowledgment. Moreover, we uncover overwhelming and increasing presence of big tech and clite universities in highly cited papers, which is consistent with a system of
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malue-commitment
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The upshot is that the discipline of ML is not value-neutral. It is socially and politically loaded, valuing and promoting conservative needs at the cost of individuals rights, respect for persons and justice; it increasingly concentrates power in the hands of few already powerful actors; it poses 346 a threat to society's s most marginalized by neglecting the potential harms of socially contentions applications of ML.
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References
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Checklist
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1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
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contributions and scope? [Yes]
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(b) Did you descrihe the limitations of your work? [Yes] See Discussion.
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(c) Did you discuss any potential negative societal impacts of your work? [Yes] Included in the Appendix.
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(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
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2. If you are including theoretical results...
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(a) Did you state the full set of assumptions of all theoretical results? [N/A]
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(b) Did you include complete proofs of all theoretical results? [N/A]
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3. If you ran experiments..
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(a) Did you include the code, data, and instructions needed to reproduce the main exper- imental results (either in the supplemental material or as URL)? [Yes] Included in supplementary zipfile for experiments in appendix.
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(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] Included in appendix.
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(c) Did you report error bars (e.g., with respect the random seed after running experi- ments multiple times)? [No]
(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Included in appendix.
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4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
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(a) If your work uses existing assets, did you eite the creators? [Yes] Full listing of annotated papers is given in the appendix.
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(b) Did you mention the license of the assets? [Yes] See Footnote 1.
(c) Did you include any new assets either in the supplemental material as URL? [Yes] Included in supplementary zipfile.
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(d) Did you discuss whether and how consent was obtained from people whose data you're using/curating? [Yes]] Discussed in Appendix A.2. Additional Methodological Details. (c) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [Yes] Discussed in Appendix A.2. Additional Methodological Details.
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5. If you used crowdsoureing or conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A]
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(b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, applicable? [N/A]
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(c) Did you include the estimated hourly wage paid to participants and the total amount spent participant compensation? [N/A]
12


Reviews:
[{"review_id": "sLFDDX-xcP", "reviewer": "Reviewer_fbBP", "summary": "The authors have done a great job of identifying the predominant values in ML research as represented in Neurips and ICML communities. They have identified 67 values and annotated 100 papers over two time periods - 2018/2019 and 2008/2009. They conclude based on their analysis that the dominant values represented are performance, building on past work (in a limited sense of improvement in methodology and performance), generalization, efficiency, quantitative results, and novelty. One of the main arguments the paper makes is that ML is not value-neutral. The authors hope that this will spur conversations in the community about orienting ML towards humanity, and society.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "I really enjoyed reading this paper and it was refreshing to see a meta-research article that focuses on important issues that ML research is facing these days. It will be interesting to see this meta-research template adopted and periodical studies like this are conducted every 5 or 10 years.\n\nI appreciate the authors for their painstaking and thorough analysis and careful annotations. Annotating 3500 sentences manually is a significant effort which is appreciated. The methodology adopted here looks fine to me, and the annotations seem to be meticulously done by the authors. My comments are below, and it will be great if the authors clarify:\n\n1. The values picked up by the authors also denote what they think are the most important to analyze - this choice by itself is not neutral, even though it is inspired by previous research in this area. Thoughts on this? During the discussions, did you neglect any potential value for any reason?\n\n2. Even though the annotating team may be diverse in some respects, it may have some shared beliefs that may contribute to some biases. This can be discussed if possible. I do agree that this sort of an intricate exercise cannot be using current crowdsourcing methodologies since it will be impossible to reach meaningful conclusions.\n\n3.  Have you attempted to contextualize the observations in section 3? For example, authors of papers proving abstract theorems may not any clue about their social impact, and it may be far too imaginative to be plausible if they attempt to do so (at least in some cases). However, for papers that develop an image classification method, this may be very much within bounds. There is some mention of this in Sec. 4.2 (paragraph 1), but more details will help.\n\n4. It can also be noted somewhere that the conferences value novelty (and its associated values) more than anything else. It is both top down and bottom up. This is changing slowly though in the recent years. For example, only in recent years conferences have gotten serious about encouraging authors to think about societal impacts of their work. Do you see that papers in recent years have values corresponding to this amplified compared to the older papers? A stratification along the two time periods may help in understanding this.\n\n5. Have you thought about providing some recommendations based on your study? How can the authors of the papers be incentivized to think more about some of the values in the tail of Figure 1? To me it seems like appropriate incentives can move the field in the right direction although it has to be done at the correct pace.\n\n6. Regarding paragraph 1 in Section 5.1: Again some contextualization will help here. ML fairness is a fairly new field and papers in the past decade may not be aware of such ideas. What is more serious is if recent papers that have access to protected group level information do not choose to report fairness measures with respect to those groups.\n\n*Update after end of discussion*\n\nThanks to the authors for their responses and appreciate their engagement. My score remains the same.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Limitations and societal impact are addressed. Please see above for comments around this.", "review_text": "", "extra_scores": {"ethical_concerns": "None", "needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4"}, "extra_text": {}}, {"review_id": "CMMMLEncInW", "reviewer": "Reviewer_JeHZ", "summary": "The paper examines about hundred highly cited machine learning papers published in ICML and NeurIPS conferences  to uncover the \"values\" embedded in them. In particular, the authors proposed a fine grained annotation scheme for detecting values in ML research, and identify about 67 such values by studying abstract, introduction, discussion, and conclusions sections of the papers under consideration. Using this annotation scheme, they annotated the papers and identified \"performance\", \"accuracy\", \"state of the art\", \"quantitative results\", \"generalization\", \"novelty\" as some dominant values in these papers. The authors also carried out a quantitative analysis of the information pertaining to funding resources and affiliations. The authors argue that all of the values encoded in these papers are defined in a way such that they support power centralization. ", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n\nSignificance: Some aspects presented in the paper can be useful in broader settings as listed below. \n\n1. The paper provides an interesting observation concerning some factors that influence and govern ML research. The authors identify about 67 such factors (termed as values in the paper) informed by studies across disciplines. Some of these values can be useful for other research studies. \n2. The authors introduce a manual annotation scheme to analyze the motivation for the problem in the papers---the authors refer to this as the justification chain and enlist various possible chains. Similarly, they provide a list of possibilities based on whether negative impacts of the research works have been provided or not. Such annotation schemes can be beneficial checklists. \n\nClarity:\nThe paper is presented fairly well written.  There are some minor grammatical issues which can be fixed (e.g. line 55 \" they the most...\")\n\nOriginality:\n1. The authors introduce a new (manual) annotation scheme based on the notion of \"values\". The values themselves ( 67 in number) are identified by means of elaborate discussions between the annotators and analysis of the papers under consideration. The values are identified as entities which can be conceptualized with some desirable properties.\n\n2. A textual analysis of some of the dominant values such as \"performance\", \"building on past research\", \"novelty\", etc. is provided with illustrations from these papers.  Some valuable insights from each of these analyzed values are also provided. \n\n\nLimitations:\n\nWhile the paper is both interesting and insightful, there are several aspects which can be significantly improved to address some of the paper's current shortcomings. Some issues of concern are listed below along with some possible suggestions for improvement. \n\nMethodology:\n\n1. The authors argue why a manual annotation is better than an automated analysis, and also mention that the benefit obtained from an automated method was limited.  However, given that at most only two individuals annotated some 40% of the papers, the annotations may be limited in their purview/analysis.  The inter-annotator agreement coefficient provided in the paper is therefore not a reliable indicator of the annotation consistency. \n\n2.  The argument related to choice of papers, years, and conference is somewhat limited. Especially given that the emphasis on ethics, negative consequences, societal impacts and the like have been more pronounced in recent years, it would be beneficial to compare works across years to observe the trends. Even for the years analyzed, it is not clear how the trends shifted, or which years were marked by substantial changes, etc. These aspects are essential for understanding if certain external events and interventions from the community helped or caused harm, and in what way. \n\n3.  The distinction between some of the values enlisted is not clear. For example, what is the difference between \"useful\", and \"practical\"? it would be helpful to include examples and definitions or how these values were conceptualized. This also raises the question of how these values were identified and distinguished from each other- more detailed description of the process needs to be provided.  \n\n4. For point 4 in the justification chain, how was the aspect \"somewhat\" defined? In other words, what was the protocol to identify if the paper somewhat justifies its connection to a social need? And in this context, how was it ensured that annotators' bias did not affect the annotation process?\n\nTakeaways: \n1. It is important to outline how these observations can inform the ML community in improving things. There have been quite a few works ( some of which the authors mention) that already uncover issues of power imbalances, corporate dominance, funding aspects, and other motivational factors for driving a research. In the light of these, it would be beneficial to provide how the lessons from this study can help in bringing about positive change.  That there is power centralization in ML pipelines is a widely evident and accepted notion, therefore it is not clear what additional lesson can be inferred by the paper in its current form. \n\nRelated work:\n1. Some relevant literature is not cited, and it is also important to distinguish how the paper is different from some of these other papers. Some of these papers also inform perspectives of graduate students ( like those of the authors in the study) and what roles they can play. \n\na. Alan Chan, Approaching Ethical Impacts in AI Research: The Role of a Graduate Student\n\nb. Tomo Lacovich, Does Deep Learning Have Politics?\n\nc. Hang Yuan, Claudia Vanea, Federica Lucivero, Nina Hallowell, Training Ethically Responsible AI Researchers: a Case Study\n\nd. Norman Makoto Su and David J. Crandall: The Affective Growth of Computer Vision\n\nIllustrations: \nThe examples provided in Table 2-5 need some explanations---perhaps, how the particular description highlights a gap and exacerbates a negative value. That way, these tables can be more compelling. ", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "\nPlease refer to the previous response for the points concerning limitations. \n\nSome suggestions for improvement:\n\n1. It would be beneficial to include more annotators for every paper to not only make the method more robust, but also to broaden perspectives.\n\n2. Regarding the annotator backgrounds: it might be beneficial to include someone from an HCI discipline also to better inform the process of annotation. \n\n3.  Provide some guidelines or suggest some potential solution pathways towards overcoming some of the issues raised in the paper. For e.g., what proactive steps need to be taken by various stakeholders in order to stop the dominance of the values proposed in the paper?\n\n4. It might be useful to have a section dedicated to related works to distinguish the current paper from prior relevant works.\n\n5. It would be helpful to elaborate on the distinction of the various values, and the process of identifying these as distinct from each other. ", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "5"}, "extra_text": {}}, {"review_id": "9riHaUmdByx", "reviewer": "Reviewer_CG7U", "summary": "The paper investigates ethical values stated in motivations and applications\nin machine learning papers. The authors analyzed 100 highly cited ML papers\nfrom four years of two major ML conferences using manual content analysis.\nThey provide statistics about the frequences of values found in those papers,\nthe top 5 being performance values, building on past work, generalization\nvalues, efficiency values and quantitative evidence. For four of them they\nprovide examples and link them to literature that often does not finds them\nunproblematic. Also observing that major tech companies currently contribute\nheavily to ML research, the authors conclude that these values support\ncentrailzation of power instead of broader societal wanted agendas.\n", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper is well written and easy to read. It does not make any methodological\ncontribution in the area of ML, but it studies ML as an object. It does not provide\nany experimental results that could be replicated or falsified beyond some statistics.\n\nThe observations, i.e., the values found expressed in the papers as well as the\nparticipation of large tech companies in ML, confirm likely general experience\nand statistics, they hardly can be called novel. \n\nThe interpretations that link specific techniques or utterances in the literature to\nunethical behavior, to me mostly look like non-sequiturs. As an example: \"choosing \nto use equal weights when averaging is a value-laden move which might deprioritize \nthose underrepresented in the data or world\" (p.5). When ML researchers average\nsay evaluation measures over instances with equal weights, they certainly do not\n\"deprioritize\" anyone, but they report a statistics about their dataset. The authors\nfail to provide evidence that ML researchers claim that their datasets are representative\nbeyond the context for which they have been collected (say, for the customers of\na company), e.g., for \"the world\". \n\nI could imagine an interesting panel discussion about these topics, but I do\nnot see this paper fit as a research contribution on an ML conference.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "yes", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2.5"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The scores of the three reviews are 3, 5, and 7. There is less disagreement than these numbers suggest; all reviewers find the paper worthy but not surprising or deep. As the area chair, I agree. I do have experience with social science research, and this paper lacks insightfulness or originality from that perspective, so I recommend rejection. The paper will eventually be published somewhere, but it won't have great impact.
Decision: Reject