Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 3: Clear rejection
review_content: summary: The paper investigates ethical values stated in motivations and applications
in machine learning papers. The authors analyzed 100 highly cited ML papers
from four years of two major ML conferences using manual content analysis.
They provide statistics about the frequences of values found in those papers,
the top 5 being performance values, building on past work, generalization
values, efficiency values and quantitative evidence. For four of them they
provide examples and link them to literature that often does not finds them
unproblematic. Also observing that major tech companies currently contribute
heavily to ML research, the authors conclude that these values support
centrailzation of power instead of broader societal wanted agendas.

main_review: The paper is well written and easy to read. It does not make any methodological
contribution in the area of ML, but it studies ML as an object. It does not provide
any experimental results that could be replicated or falsified beyond some statistics.

The observations, i.e., the values found expressed in the papers as well as the
participation of large tech companies in ML, confirm likely general experience
and statistics, they hardly can be called novel. 

The interpretations that link specific techniques or utterances in the literature to
unethical behavior, to me mostly look like non-sequiturs. As an example: "choosing 
to use equal weights when averaging is a value-laden move which might deprioritize 
those underrepresented in the data or world" (p.5). When ML researchers average
say evaluation measures over instances with equal weights, they certainly do not
"deprioritize" anyone, but they report a statistics about their dataset. The authors
fail to provide evidence that ML researchers claim that their datasets are representative
beyond the context for which they have been collected (say, for the customers of
a company), e.g., for "the world". 

I could imagine an interesting panel discussion about these topics, but I do
not see this paper fit as a research contribution on an ML conference.

limitations_and_societal_impact: yes
review_title: null

final_score: rating: 7: Good paper, accept
review_content: 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.
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.

I 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:

1. 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?

2. 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.

3.  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.

4. 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.

5. 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.

6. 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.

*Update after end of discussion*

Thanks to the authors for their responses and appreciate their engagement. My score remains the same.
limitations_and_societal_impact: Limitations and societal impact are addressed. Please see above for comments around this.
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: 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. 
main_review: Strengths:

Significance: Some aspects presented in the paper can be useful in broader settings as listed below. 

1. 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. 
2. 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. 

Clarity:
The paper is presented fairly well written.  There are some minor grammatical issues which can be fixed (e.g. line 55 " they the most...")

Originality:
1. 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.

2. 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. 


Limitations:

While 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. 

Methodology:

1. 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. 

2.  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. 

3.  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.  

4. 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?

Takeaways: 
1. 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. 

Related work:
1. 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. 

a. Alan Chan, Approaching Ethical Impacts in AI Research: The Role of a Graduate Student

b. Tomo Lacovich, Does Deep Learning Have Politics?

c. Hang Yuan, Claudia Vanea, Federica Lucivero, Nina Hallowell, Training Ethically Responsible AI Researchers: a Case Study

d. Norman Makoto Su and David J. Crandall: The Affective Growth of Computer Vision

Illustrations: 
The 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. 
limitations_and_societal_impact: 
Please refer to the previous response for the points concerning limitations. 

Some suggestions for improvement:

1. It would be beneficial to include more annotators for every paper to not only make the method more robust, but also to broaden perspectives.

2. Regarding the annotator backgrounds: it might be beneficial to include someone from an HCI discipline also to better inform the process of annotation. 

3.  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?

4. It might be useful to have a section dedicated to related works to distinguish the current paper from prior relevant works.

5. 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_title: null

Decision: Reject