Social and environmental impact of recent developments in machine learning on biology and chemistry research
Authors: Daniel Probst
Venue: Submitted to ICLR 2023 (2023)

Abstract: Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to a public research brain-drain, a narrowing of the research effort caused by a focus on deep learning, and the introduction of biases through a lack of sociodemographic diversity in data and personnel caused by recent developments in machine learning are a current topic of discussion and scientific publications. However, these discussions and publications focus mainly on computer science-adjacent fields, including computer vision and natural language processing or basic ML research. Using bibliometric analysis of the complete and full-text analysis of the open-access literature, we show that the same observations can be made for applied machine learning in chemistry and biology. These developments can potentially affect basic and applied research, such as drug discovery and development, beyond the known issue of biased data sets.

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SOCIAL AND ENVIRONMENTAL IMPACT OF RECENT DEVELOPMENTS IN MACHINE LEARNING ON BIOLOGY AND CHEMISTRY RESEARCH
Anonymous authors Paper under double-biee blind review
ABSTRACT
Potential societal and environmental effects such the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to public research brain-drain, narrowing of the research effort caused by focus on deep learning, and the introduction of biases through a lack of sociodemographic diversity data and personnel caused by recent developments in machine learning are current topic of discussion and scientific publications. However, these discussions and publications focus mainly computer science-adjacent fields, including computer vision and natural language processing or basic ML research. Using bibliometric analysis of the complete and full-text analysis of the open-access literature, we show that the same observations can be made for applied machine learning in chemistry and biology. These developments can potentially affect basic and applied research, such as drug discovery and development, beyond the known issue of biased data sets.
INTRODUCTION
The unprecedented progress of machine learning during the past two decades has been catalysed and remains driven by the development of increasingly powerful computer hardware. This progress is enabled by the ability of deep neural networks to scale exceptionally well with increasing data availability and model complexity compared to other approaches. Thus, they can be trained for linear regression small data sets and, with conceptually simple changes to the network architec- ture, for language translation or image generation on immense text corpora and image collections. While comparatively exceptional, deep neural networks at an exponential cost (Schwartz et al., (020),, leading to diminishing returns (Thompson et al, 2 Among the machine learning community, this has raised concern over the future direction of the field and growing exclusivity driven by ever-increasing hardware and energy costs (Schwartz al.) 2020: Thompson et al., 202T] Jurowetzki al. 2021). After a discourse on the intertwined recent history of deep learning and hardware advances, we will analyse the applicability of the most prominent concerns raised machine learning research to applied machine learning research in biology and chemistry. We have categorised these concerns under (socioeconimic scientific and
understood to still only scale linearly
environmental considertonncc
The hard- and software that catalysed rapid developments in machine learning In late 2002 and early 2003, the release of the Radeon 9700 and GeForce FX video cards introduced fully programmable graphies pipeline, extending and later replacing the existing fixed function pipelines. Unlike the fixed function pipeline, which allowed the user to only supply input matrices and pa- rameters to built-in operations, the programmable pipeline introduced the execution of user-written shader programs on the GPU (Contributors) 2015). This fundamental change allowed programmers and researchers to exploit the intrinsic parallelism of GPUs 2 years before Intel would introduce its first dual-core CPU. Within months of the availability of this new hardware and the accompany- ing APIs. researchers implemented linear algebra methods on GPUs and introduced programming frameworks use GPUs for general purposs computations (Thompson al. 2002
Krüger & West-

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ermann 2003). This rapid development marked the dawn of purposs computing on graphies processing units (GPGPU). In a presentation at ICS '08, Harris presented the successes of GPGPU by highlighting speed-up in molecular docking, N-body simulations, HD video stream transcoding, or image ceceessing-appliaan
in machine learning were not discussed. However, just one year later, the introduction of GPUs as general purpose processors catalysed the deep learning explosion of the early 2010s by allowing deep learning algorithms pioneered by Alexey Ivakhnenko in 1971 to be run within practical time on widely available consumer hardware when Rajat al. showed that GPUs outperform CPUs by an order of magnitude in large-scale deep unsupervised learning tasks (Ivakhnenko) 1971 Raina et al. 2009).
Hardware and energy requirements increase in machine learning research In 2010, Ciresan et al. (2010) introduced a multi-layer perceptron (MLP) with up to 12.11 million free parameters where forward and backward propagation were implemented on a GPU using NVIDIA's proprietary CUDA API introduced by Harris at ICS '08 two years before, speeding up the routines by a factor of 40. In their arXiv paper, they also report the computer's 's hardware specifications as "Core2 Quad 9450 2.66GHz processor, 3GB of RAM, and a GTX280 graphics card". The GTX 280 graphics card by NVIDIA was, at the time of the paper's 's writing, two years old and cost USD 893 when first released (adjusted for inflation). Equipped with this 2-year-old hardware that cost well below USD 1,000, Cireşan et al. were able improve upon the sate-of-theeart performance on the MNIST classification benchmark set four years prior by Ranzato et al. (2006). As they not only reported the hardware used but also the time it took to train the model, the power usage of the GPU and CPU, with a thermal design power (TDP) of 236 and 95 Watt, respectively, can be calculated as 114.5h x (236 + 95)W Wh. Seven years later, Vaswani et al. (2017) introduced the transformer architecture. The training used 8 NVIDIA Tesla PTOO GPUs, whose price was USD 55,100 100 at the time, and took 84 hours, resulting in overall energy usage of 84h x x 250W = 168kWh
Hardware and energy requirements explode in applied machine learning Applying the novel transformer architecture, NVIDIA reportedly trained the 345 million parameter BERT model in 2019, which was previously introduced by Google in the same year, 4 D servers (64 Tesla V100s) in 79.2 hours, with a maximum power usage of 12,000 Watt, resulting in a total power use of 3.8 MWh (79.2h x 4 x 12kW) (Devlin al., 2018). The cost of this system the time of training was USD 1,596,000. Alternatively, the BERT model could be trained on on-demand Google Cloud GPUs for USD 2.48 per GPU hour, resulting in total costs of USD 12,570 (2.48 64 79.2). The MT-NLG model presented by NVIDIA and Microsoft in 2021 represents the acceleration of hardware and energy cost in the field (Smith al. 2022). The 530 billion parameter model was trained on 560 DGX A100 servers a total of 4,480 NVIDIA A100 80GB Tensor Core GPUs 2,160 hours (Rajbhandari et al., 2022). The power usage of a cluster of 560 DGX A100 servers during 2,166 160 hours is 7.862 GWh (2,160 x 560 x 6.5kW). Taking the world average electricity price of USD 0.131 per kWh during December 2021, the total electricity bill for training MT-NLG was USD 1,029,922. The total cost of the hardware is hard to estimate as specialised network hardware is required to build such a cluster; however, the DGX A100 was priced at USD 199,000 on release, resulting in minimum total cost of USD 111, 111,440,000 (199,000 x 560). Training the model on on-demand Google Cloud GPUs for USD 2,141.82 per GPU month results in a total cost of USD 28,786,060.8 (3 4,480 x 2, 2,141.82).
Hardware and energy costs drive the de-democratization of machine learning The examples discussed above represent an increasing hardware and energy cost in conducting basic and applied deep learning-based machine learning research. The resulting diminishing returns and the environ- mental impact have previously been discussed by Thhmpson al. (202I) and Schwartz al. (2020). The development of increasing costs following a potential breakthrough stands in contrast to similar or even more disruptive changes other fields, such as CRISPR-Car Cas9 lowering costs in molecular biology or the ever-decreasing costs of genome and RNA sequencing (Ledford 2015) Wetterstrand] 2021 Gierahn et al.) 2017). While CRISPR-Cas and affordable sequencing has led to what has been called the democratization of access to sequencing and genome editing (Guernet & Grumolato 2017 McPherson] 2014 Srivathsan et al. 2019), cutting-edge machine learning research is becom- ing potentially increasingly expensive and exclusive (Ahmed & Wahed 2020). Indeed, in 2020 article on the most cited research articles, all mentioned machine learning research was conducted
by, or in collaboration with, OpenAI, Microsoft, and Alphabet (Kingma & Ba 2014 Ren et al.
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Mnih al. 2013 Vaswani et al. 2017: Silver et al. 2016 Crew 2020), suggesting need for resources not available within academia. While the involvement of these companies, whose R&D budgets exceed those of most nations (Bajpai 2021 Ballard 2021 Uis 2022). contributed greatly to the advancement of machine learning, a potential technological dependency of academia on this commercially driven progress combined with brain-train from academia to industry may result in a narrowing of machine learning research (Jurowetzki et al., [022]] Klinger et al., 2020) Hagendorff & Meding, 2020). In the following paragraph we will discuss these developments and the concerns raised with focus on applied machine learning in chemistry and biology.
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Figure Fraction of applied machine learning publications in biology and chemistry. After an increase in the early 2000s and a plateau lasting for 10 years, the introduction of general-purpose GPUs and a renewed interest in deep neural networks making use this capable hardware sparked unprecedented growth of machine learning research in biology and chemistry in the mid-2010s.
SOCIOECONOMIC CONSIDERATIONS
Available research funding differs greatly between countries and institutions. While discussing the reasons and possible solutions to this situation are far beyond the scope of this research, it is im- portant to note that cost of research being driven by ever increasing hardware and electricity re- quirements has the potential to further drive inequity between nations, institutions, research groups, and individual researchers based on available funding (Gerhards et al.] 2018 Nielsen & Andersen 2021 Wahls 2018). In addition to direct funding, collaborations with leading big tech companies, which led to the most cited recent publications (Crew) 2020), are equally unevenly distributed among countries and institutions, further concentrating funding. Furthermore, the availability of necessary hardware to conduct Mlte-of-the-an deep learning research can depend on the current geopoliti- cal situation, possibly disproportionately affecting researchers in low- and middle-income countries negatively (Nellis & Lee (002).. In this section, we discuss social and economic concerns raised within the machine learning community, including machine learning divided society and that diminishing returns limit participation.
Machine learning in a divided society In a wider social context, the concentration of resources and talent needed develop and operate machine learning systems in high-income countries form the basis of what has been labelled colonialist Al (Birhane) 2020 Mohamed et al. 2020 Adams 202T). The effects of this process recently came light through the discovery of biases in models in
healthcare and policing, among others (Obermeyer et al. 2019 Hildebrandt et al. 2020). Causes of
these biases have been shown to go beyond biased data (Schwartz et al.) 2022 Mehrabi al. 2021).
These effects introduced above are of interest for the natural sciences, concretely biology and chem- istry, as deep learning-baat based applied machine learning plays an ever more critical role in both fields and issues being caused by said effects may affect crucial research such drug development or genetics. The increasing importance increasing share of machine learning related publications, which have approximately doubled within the past six years compared to the overall published literature in the respective fields (Fig- ure Tp. Here, the potential for the described societal and environmental effects within the scientific
machine learning in biology and chemistry is reflected by

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fields of biology and chemistry will be explored based on quantitative bibliometric analysis. The utilised data was downloaded from the OpenAlex index of scholarly works (Priem et al. (002)) The final data set contained 27,861 biology and 16,301 chemistry works filtered for machine learning topics. For a list of filter keywords, see Appendix A
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Figure 2: R&D funding of nation states and "big tech" companies. (a) R&D funding per researcher and year correlates well with countries GDP. (b) The amount of R&D funding per researcher and year differs greatly between countries. Comparing the R&D budgets per employee (includes non- research employees) of "big tech" companies places them among the top funded nations.
Diminishing returns that limit participation As current and future diminishing returns of deep learning basic and applied machine learning research threaten to drive costs up, the financial barrier to participating in the respective fields also increases (Thompson et al.) [022]] Schwartz et al.) 2020). Given the extreme disparities in R&D funding across countries (Figure 2 and institutions has great potential to also increase the exclusiveness of participating in state-of-the-an research. Indeed, potential downstream effects of such disparities have recently come in the focus fields such as health care, bioinformatics, and biometrics, where machine learning models are often biased towards persons of European descent (Obermeyer et al. 2019 Gupta et al.) [220 Celi et al., 2022 Dias & Torkamani] 2019, Kiseleva & Quinn 202 Drozdowski et al.) 2020 Hazirbas et al. 2022). While these issues are being tackled by increasing participation students, developers, and researchers of non-European descent and by debiasing data sets, it is being questioned whether these efforts alone can adcquately solve the bias bias-problens probleoobemmmmm and avert a future long-term dependence the global south on the global north (Birhane 2020 Bon et al., 2021].
In respect to machine learning in biology and chemistry, we make the assumption that an increase in the correlation between available funding and the share of machine learning literature published in each field would suggest a potential for exclusiveness and a measure for inequity. In order to iden- tify possible increase in correlation between available R&D funding and applied machine learning in biology and chemistry, as well as the impact of deep learning-baid based machine learning, 137 ,56 affiliations from coonnfomaticsscooouutaiiioo ics/computational chemistry publications were analysed for the periods between 2000 to 2016 and 2017 to 2021 (Figure 3). Only countries with least 100 publications per year during the specified periods were included, and outliers were removed using a z-score cut-off of +3. Plotting the frac- tion of machine learning publications amongst the entire published literature for the chosen topics from biology and chemistry for each country against the countries' available funding per researcher shows an increasing, however as of yet insignificant, correlation. While the average fraction of ma- chine learning literature increased by 19.3% (21.7% 41%) and 9% (20.4% to 29 29.4%) in biology and chemistry, respectively, the Gini coefficient of the fraction of machine learning publications remained stable in biology (0.287 to 0.287) and decreased in chemistry (0.343 to 0.304). The fund-
biology and 80,206 affiliations from cheminformat-
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Figure 3: Fraction of applied machine learning papers in biology and chemistry as function of R&D funding per researcher and year. (a) Biology 2000-2016, (b) chemistry 2000-2016, (c) biology 2017-2021, (d) chemistry 2017-2021. While not significantly so, a potential trend is emerging where the fraction of applied machine learning publications (of each fields total) correlates with the funding per researcher and year.
ing per researcher and year is average of the available data between 2000 and 2021 for each country- with an average Gini coefficient of 0.479 (0.452-0.497) over all stratifications.
3 SCIENTIFIC CONSIDERATIONS
Different concerns with a potential effect on the scientific community have been raised within the machine learning field. In this section, analyse the most prominent of these concerns including the privatization of machine learning, a possible academic brain-drain, citation incquality, and narrowing of research.
The privatization of machine learning Beyond Universities, technology companies are major contributors to basic and applied deep learning-based machine learning research across multiple fields. Prominent examples are AlphaFold by DeepMind, BERT by Google, 3D human pose esti- mation by Facebook and Google, or DALL-E by OpenAl (Jumper et al. 2021 Devlin et al., 2018 Ramesh et al. 2021). In addition, the hardware company NVIDIA, which GPUs and CUDA frame- work power most basic and applied deep learning research (with the notable exception of research conducted by Google and DeepMind, both using Google's 's TPUs), is active in both basic research and deep learning applied to computer graphics (Karras et al.) 2020 Wang et al.) 2019). The involve- ment of these corporations in research significant, as their respective R&D budgets are higher than those of most countries (Figure 2p). For 2020 (2021 for NVIDIA), the R&D spending of Google (now Alphabet) was USD 27. .757 that of Facebook USD 18.45B, and that of NVIDIA USD 3.92B and were higher than that of 120, 116, and 101 countries, respectively (Bajpai 2021 Ballard (021) For Google and Facebook, this includes countries such as Belgium, Spain, India, or the Russian Fed- eration (Uis 2022). The availability of this large amounts of funding for machine learning research in industry can have three significant effects on public university research:
(i) During the past two decades, there has been an unprecedented brain drain of professors from university industry, development that has been termed the "privatization of AI research(-ers)" (Gofman Jin 2019 Jurowetzki et al.) 2021).
(ii) Industry collaborations that provide access to significant amounts of compute are often limited to clite institutions in countries where funding is already comparatively high, fur- ther increasing the divide resource availability between institutions within a country and globally (Jurowetzki al. 2021 Ahmed & Wahed 2020).
(iii) A narrowing of research driven by universities aligning with commercial interest through collaborations, a reliance of universities on models, methods, and hardware developed in industry, and the above mentioned brain-drain. (Klinger et al. 2020 Golman & Jin 2019).
Potential consequences of these developments have been shown to include stagnation of (method- ical) diversity in machine learning research with preference for hungry deep learning methods (Klinger et al. 2020). In addition, analysis of research pre-
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sented at NeurIPS, CVPR, and ICML showed that researchers in the field often fail to report flicts of interest, that publications from the industry gather significantly higher citations than those from academia, and that industry publishes trending machine learning topics two years before academia on average (Hagendorff & Meding 2020).
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Figure 4: Transitions by researchers between education and industry. (a) In biology, the overall transitions between academia and industry remain largely balanced the observed period. (b) In chemistry, we observe recent steady increase of transitions from academia to industry. (c) Starting in 2016, there is steady net flow from acadamia Tech, spiking 2021. (d) similar but less pronounced spike can be observed in chemistry.
In order to assess the existence of such effects in biology and chemistry, the available data was la- belled by affiliation-type Industry affiliations were labelled with either Tech, Pharma, Chem, and Other. Tech includes the large technology companies (Alphabet, Microsoft, Meta, Nvidia, Amazon, IBM, Tencent, Baidu, and Twitter) and their subsidiaries. Pharma includes pharmaceutical com- panies such Novartis, Pfizer, and Bayer. Chem includes chemical and petrochemical companies such as BASF, Exxon, and Lonza Other includes companies not falling into the other categories, such Schrödinger, General Electrics, or Siemens. All other affiliations were labelled with None.
Academic brain-drain Following the approach by Jurowetzki et al. (2021), transitions between academia and industry were defined as year-over-year change in affiliation (if there were industry as well as academic affiliations within the same year, the mode was chosen as the overall affiliation). In the results, based 92,496 and 54,243 authors of biology and chemistry articles, respectively, trends that are similar to those observed by Jurowetzki et al. (2021) can be noticed (Figure 4). Over the past six years, chemistry saw an unprecedented increase net transitions of machine learning practitioners from academia to industry (Figure 4b), while overall transitions in biology remain slightly skewed towards industry (Figure 4h). However, biology, and to lesser degree chemistry, saw a significant jump in academia to Tech transitions in 2021 (Figure 4c.d). Interestingly, the data shows lower baseline of academia industry transitions compared the data presented by Jurowetzki et al. 22021), suggesting either fewer transitions from academia to industry overall more likely, tendency in the fields to stop publishing after leaving academia.
Citation inequality The observation by Hagendorff & Meding (2020) of significantly higher rates of citations of papers authored by industry-aftiliate authors can be replicated for machine learning in chemistry and partially for machine learning in biology. In biology, citation rates by Pharma, Tech, and Other are significantly higher than those in academia (with mean of 6.31, 21 21.64, and 4.13 versus 3,03 citations per year, respectively. Figure 54). In chemistry, the citations per year are significantly higher for Pharma, Chem, Tech, and Other, compared to academia (with a mean of 4.46, 4.00, 8.71, and 3.62 versus 2.99, respectively. Figure 5k). Breaking down the affiliations

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Figure 5: Industry affiliations and the effect citations. For both biology (a) and chemistry (g), publications with industry affiliations gather a higher amount citations per year. This effect is, in both fields, most pronounced for Tech affiliations. Breaking down the industry affiliations by country (Figure 5p-l for biology, h-1 for chemistry), it is little surprising that private research generally takes place in countries with high ranking universities and highly developed industries.
by country, it is little surprising that private research generally takes place in countries with high- ranking universities and highly developed industries (Figure 5 for biology, h-1 for chemistry).
narrowing of research A narrowing machine learning research would certainly have the po- tential to lead to negative downstream effects in the natural sciences. In addition, there is also the potential for a narrowing of applied methods independently from developments in machine learning rescarch. Such narrowing of applied methods can be driven by the same factors narrowing in basic machine learning research. These factors include demand-side economies of scale or a bandwagon effect, early fortuitous events that result in lock-in of and previous investments in suitable resources such as hardware or personnel, and social dilem- mas (Klinger al., 2020). To explore a potential narrowing of machine learning methods utilised in biology and chemistry, articles have been labelled with the following broad categories: Dimen- sionality Reduction//eattue Selection. Genetic/Evolutionany: Neural Networks, Other/Unsseciffed, Regression, Pnatistti/Probabllisttt and SVM. Analysing the temporal development, the results show that the largely deep learning- based Neural Networks in biology (Fiuure 6/)) and Neural Networks together with RF/Boosting in chemistry (Figure 6b) are mainly responsible for the recent explosive growth of machine learning literature the two fields. However, the continuous and roughly linear growth of the other categories implies that the fast-growing methods do not seem to grow the ex- pense of methods from other categories. Plotting the mean of the citations per year for each category shows that the publication of highly cited biology articles utilising Neural Networks predates and is more sustained (Figure 66) than the publication of highly cited chemistry articles utilising methods from the same category (Figure 6d).
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Figure 6: Number of publications by machine learning method category. (a) Number of publications by machine learning method category biology. (b) Number of publications by machine learning category in chemistry. (c) Mean citations/year by year and machine learning category in biology. (d) Mean citations/year by year and machine learning category in chemistry.
4 ENVIRONMENTAL CONSIDERATIONS
In their publication "Green AI", Schwartz al. 2020) showed that the carbon footprint of deep learning has been growing continuously and suggest making efficiency an evaluation criterion in addition accuracy. They argue that this ever-increasing need for more compute would make deep learning- braningg-based machine learning research not only environmentally problematic but also less inclusive due the previously discussed unequal distribution of access to compute.
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Figure 7: Hardware and energy use of applied machine learning in biology and chemistry. (a) GPU models used by biology and chemistry researchers combined, showing an increase in the fraction of machine learning-dedicated Tesla series GPUs used, while the share of consumer and enthusiast oriented GPU series is shrinking. extracted from open access literature shows general increase in electricity use per publication.
(b) The data on training times and exact GPU model that could be
The quantitative exploration of the environmental impact of applied machine learning in biology and chemistry has been complicated by the low availability of information regarding the hardware used and the time spent train the models. From total of 27,861 biology and 16,301 chemistry articles, 14,365 (51.6% and 4,184 (25.7%) were downloadable as open access articles. From these downloaded articles, the GPU model could be extracted from 646 (4.5%) biology articles and from
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243 (5.8%) chemistry articles, and the GPU model well as the training time from 56 (0.4%) and 14 (0.3%), respectively. Based on this sparse data, the statistical power of the analysis is relatively low. However, certain trends in the GPU model-use can be observed between 2018 and 2021 (Figure 7p): The fraction Nvidia Titan GPUs has been steadily declining during the entire period. Consumer- focused GPUs from the GeForce 10, 20, and 30 series continue be used in research, with the fraction of GeForce 20 GPUs increasing. In contrast, GeForce 10 series cards experienced decline starting in 2019, However, the most important trend is the increase in the fraction of Tesla GPUs.
As newer GPU models are becoming more energy efficient (Speiko et al. 2021), efficiency remains a second-class metric compared to accuracy in applied machine learning publications in biology and chemistry. This situation is reflected in the lack of an increase in reported hardware use training times (Figure 7p). Indeed, the available data shows general increase in power usage per publication based on the reported GPU model, the number of GPUs used, and the training time. The values are an estimation and only repreent the training time of the final model.
5 CONCLUSION
The societal and environmental impacts of recent developments in machine learning are actively being discussed in machine learning research and closely adjoined fields such natural language processing image processing. However, the effects driven by and experienced within applied ma- chine learning in the natural sciences received little attention. This exploratory study into the social and environmental impact of recent developments in machine learning on biology and chemistry research brings to light the following insights:
(i) The introduction of deep learning methods has caused a rapid increase in the share of machine learning-related articles in biology and chemistry literature.
(ii) There is a potential emerging trend towards an increase in inequity in applied machine learning research conducted in biology and chemistry based on available funding when corrected for overall publications in the respective field. While not yet statistically sig- nificant, the trend should be monitored and countered. There is, however, a significant difference between citation metrics of education vs industry affiliated researchers, with university-affiliated research receiving fewer citations.
(iii) Based on transition patterns researchers between academia and industry, big tech compa- nies seem to get increasingly involved in conducting applied machine learning research in biology and chemistry. Given the vast resources and R&D spending of these corporations (even compared to nations), the concerns of potential brain-drain away from academia and a narrowing of the research conducted are also warranted in biology and chemistry.
(iv) As of yet and based on the number of publications, the growth of neural networks -bsee deep learning research did not happen at the expanse of other, generally less compute- intensive, categories of machine learning methods. However, citation metrics for neural network- based deep learning methods have recently spiked compared to other categories. While this data does not show a narrowing of the use of methods, continuous monitoring of these metrics to avoid potential method and technology lock-in, especially as investments in specialized hardware continue, may be warranted.
(v) Data that allows an estimation of the environmental impact of applicd machine learning in biology and chemistry is scarce due to a failure to report used hardware and spent training time in the vast majority of articles. This scarcity shows the need for an effort by both authors and publishers introduce measures concerning reporting these metrics, especially as their importance goes beyond monitoring the environmental impact. Nevertheless, based on the available data, trend towards more specialized hardware and overall increase in energy use per paper could be observed.
Overall, these insights are similar to the ones made in machine learning research in general and warrant action, given that research topics with high societal impact, such as genetics or drug development, can be affected. While each topic may be discussed in detail in future publications, this overview provides the basis for discussion and draws attention to the crucial interfaces between socicty, the economy, the natural sciences, and machine learning.

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REPRODUCIBILITY STATEMENT
All data used this study is freely accessible. The code to reproduce that data as well the figures can be found in the following anonymised repository: https://
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A APPENDIX
A.1
OPENALEX QUERIES
The OpenAlex database was queried for works of type journal-article and proceediins-arttilee filtered on machine learning concepts and biology or chemistry concepts.
proceddgssi and
The query for machine learning works in biology was htt.tpps///pppp..eaaalxxxx
www.gpa....ssollooooooeoeoeoooor C56328972 C
|
C177801218
C :
The query for machine learning works in chemistry was httppps:///gg...peaaalloooo
|
C203014093 or page oagaap/ooccoooiiiicareaeaaaaaaar
The query for all biology works was:
C32309587/1118802221 02700000000000000077772223333310
The query for all chemistry works was:
C54355233 62561678820000511111111111333363
C89423630 35222222000000002211111112111110
C55493867 35535977222242aaaataaaaarrreeeer
proceed ngs- wwc.eppaaaseeppppeeoooaaaattttao
Machine learning concepts
Artificial Intelligence: C154945302
Machine Learning: C1 C119857082
Deep Learning: C108583219
Artificial Neural Network: C50644808
Genetic Algorithm: C8880873
Evolutionary Algorithm: C159149176
Simulated Annealing: C126980161
Expert System: C58328972
Reinforcement Learning: C97541855
Support Vector Machine: C12267149
Multilayer Perceptron: PercepayerPerreetrroon C179717631
Convolutional Neural Network: C81363708
Recurrent Neural Network: C147168706
Boosting: C46686674
Random Forest: C169258074
Biology concepts
Biology: C86803240
15

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Bioinformatics: C60644358
Genetics: C34355233
Computational Biology: C70721500
Biostatistics: C140556311
Molecular Biology: C153911025
Evolutionary Biology: C78458016
Cell Biology: C95444343
Microbiology: C89423630
Immunology: C20301409)
Metabolomics: C21565614
Proteomics: C461 1723
Omics: C157585117
Structural Biology: C191 120209
Biochemistry: C55493867
Protein Folding: C204328495
Chemistry concepts
Drug Discovery: C74187038
Drug Design: C64903051
Molecule: C32909587
Chemical Reaction: C177801218
Chemical Space: C99726746
chEMBL.: C63222358
Virtual screening: C103697762
Drug Design: C19207 1366
QSAR: C164126121
ADME: C69366308
Chemistry: C185592680
Computational Chemistry: C147597530
Molecular Dynamics: C59593255
Catalysis: C161790260
Organic Chemistry: C178790620
Pharmacology: C98274493
16


Reviews:
[{"review_id": "tXuo3ALmsDI", "reviewer": "Reviewer_dCqg", "summary": "", "questions": "", "limitations": "", "rating": 5, "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": "", "summary_of_the_paper": "\nThe authors go over trends in the research and use of machine learning\nand in particular deep learning in the applied sciences, via analysis\nof the open-access literature (bibliometrics and/or full-text analyses),\nand highlight areas for concern. The findings are somewhat similar to\nprior results of the analyses of recent trends in machine learning R&D \nand its related fields.\n", "summary_of_the_review": "The paper reports on trends in ml and how it has been developing in\nthat past decade, specially in biology and chemistry.  Several of\nthese trends are certainly concerning (the de-democratizatin of ml\nwork).\n\nHowever, I don't think that ICLR is the venue for this publication,\njust as literature reviews are highly useful, but not relevant in this\nconference. The paper does not have a technical contribution.\n", "strength_and_weaknesses": "Strengths:\n\n-- A variety of useful insights and takeaways into how  ml (in particular large-scale deep ml) is impacting other\n   scientific fields, in particular biology and chemistry, and several\n   trends are estimated from analysis of published research  (societal/environmental/energy impact).  \n\nWeaknesses:\n\n-- No or very little technical contributions.  The ICLR venue may not to be\n   relevant for this report.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}, {"review_id": "r8Pqrh_nPBB", "reviewer": "Reviewer_RDG6", "summary": "", "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": "", "summary_of_the_paper": "Paper studying social and environmental impact of recent developments in ML (with a focus on DL) on biology and chemistry research.", "summary_of_the_review": "Interesting paper studying social and environmental impact of recent developments in ML (with a focus on DL) on biology and chemistry research.\n\nIt falls within the remit of the conference, as the call for papers includes work on \"societal considerations of representation learning \nincluding fairness, safety, privacy, and interpretability, and explainability\"\n\nEven if interesting and certainly relevant, my main qualm with this study is that it lacks coherent structure. A lot of ideas and information\nare thrown in the pot, but they are barely organized at all, jumping from a topic to the next, almost without the respite of a paragraph ending.\nThis is compounded by a rather unirthodox referencing system.\n\nI definitely like the content, but it seems to me that it would require a different venue in which these ideas could breath, be it a jounal position paper or a book chapter.\n", "strength_and_weaknesses": "Strengths:\nLots of information and very interesting topic\nWeaknesses:\nMinimally coherent structure. At some points it almost seems like a stream of thoughts dump.\nMixed topics\nUnorthodox information source referencing", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "r-GWKER-L2", "reviewer": "Reviewer_6kvg", "summary": "", "questions": "", "limitations": "", "rating": 8, "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": "", "summary_of_the_paper": "The paper draws attention to the societal and environmental impacts of machine learning research in chemistry and biology. The paper specifically makes a contribution in the following components such as: environmental considerations, citation inequality, academic brain-drain, and scientific considerations:\n\nThe study makes the following key assertions:\n\n* During the past two decades, there has been an  unparalleled shift of professors from university to industry. \n* chemistry saw an increase in transitions of machine learning practitioners from academia to industry, while overall transitions in biology remain slightly skewed towards industry \n* Technology companies drive machine learning research. \n* More citations in chemistry and biology are generated from Pharm, tech, and “other” companies compared to academia. \n* In terms of environmental impact, the data was limited and as a result the  quantitative analysis of the environmental impact of  machine learning research in biology and chemistry was limited. ", "summary_of_the_review": "\nThe paper presents the critical interfaces of social and environmental impact of machine learning research specifically in chemistry and biology.  The findings reported in the paper are applicable to other fields as well.  This work provided a basis for discussion as key fields such as drug development, genetics, and cancer research can be affected as well. Overall, this overview provided essential knowledge for the advancement of machine learning research in biology and chemistry. \n\n\n", "strength_and_weaknesses": "Strength: results reported were consistently compared with those in literature. \n\nWeakness 1: The sentences are just too long. Need to be broken down. For example, this sentence is too long : “Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to a public research brain-drain, a narrowing of the research effort caused by a focus on deep learning, and the introduction of biases through a lack of sociodemographic diversity in data and personnel caused by recent developments in machine learning are a current topic of discussion and scientific publications.\n\nWeakness 2: A simple figure highlighting all the steps/methods that were followed in generating the paper findings is missing.\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "-WqqhTE8Er", "reviewer": "Reviewer_s4UR", "summary": "", "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": "", "summary_of_the_paper": "\nIn this paper, the authors perform several quantitive studies for understanding the social and environmental impact when applying machine learning methods for biology and chemistry research. Some of authors observations include a potential inequity increase in applied machine learning research in biology and chemistry, and big companies tend to be actively involved in machine learning based research.\n\n", "summary_of_the_review": "\nThis paper is not proposing some new tasks or introducing some new methods. Although the topic and the authors' findings are interesting, it seems this paper is not suitable for a conference like ICLR.", "strength_and_weaknesses": "\nThe topic is of importance and in general interesting.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject