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REVIEW 3 major objections 4 minor 83 references

Artificial Intelligence in Environmental Protection: The Importance of Organizational Context from a Field Study in Wisconsin

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The same satellite-based AI tool was judged useful by an advocacy group and not by the regulator that verified the same detections.

desk verdict The dual-organization field trial is a genuine contribution, but the headline 82% 'regulatory cracks' figure rests on an unverifiable AFO attribution and needs a sensitivity analysis before the paper's claims are as strong as they look. read the letter →

arxiv 2501.04902 v1 pith:AYSV5UI3 submitted 2025-01-09 cs.CY cs.HC

Nicolas Rothbacher , Kit T. Rodolfa , Mihir Bhaskar , Erin Maneri , Christine Tsang , Daniel E. Ho This is my paper · ORCID
classification cs.CYcs.HC
keywords AIevaluationorganizationalcontextenvironmentalenforcementsatelliteremotesensingCAFOregulationregulatorythresholdshuman-AIcollaborationfieldstudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper reports a field trial in Wisconsin where a satellite-imagery AI model that detects winter manure spreading was run concurrently by the state regulator (WDNR) and an environmental advocacy group (ELPC). Both organizations confirmed the model's detections at nearly identical rates, but they came to opposite conclusions about whether the tool was worth using: WDNR saw few clear violations and doubted the value, while ELPC valued the documentation of environmental risk even when the spreading was legal. The paper argues that organizational goals, not model accuracy, determine how AI tools are assessed in practice. It also finds that 82% of the spreading WDNR confirmed fell outside existing regulations, because of hard thresholds on facility size and application dates. The study matters because it shows AI can expose gaps in environmental law while raising the question of how to evaluate AI in institutional settings.

What carries the argument

The paper's conceptual engine is the 'regulatory Rashomon effect': concurrent field trials gave the same AI detections to a regulator and an advocacy group, so identical ground truth was filtered and judged by two different institutional mandates. Operationally, the tool is a fine-tuned YOLOv5 object detector that flags candidate land-application events in near-daily 3m/pixel Planet satellite imagery; detections were routed to WDNR only on permitted CAFO fields and after central-office desk review, while ELPC verifiers received the top-confidence detections within drivable range and field-checked them from public roads. The argument that regulation has arbitrary boundaries rests on two statutory thresholds—the February 1–March 31 winter ban and the 1,000-animal-unit CAFO definition—which are the specific legal machinery that lets most confirmed spreading escape enforcement.

What would settle it

Count the animal units at every facility that WDNR attributed a compliant post-February application to (for example, by cross-referencing permit records or estimating barn capacity from satellite imagery); if a substantial share of those facilities exceed 1,000 animal units, the 82% regulatory-gap figure would collapse, even if the organizational-context finding still stands.

Watch

Extended reading notes

Core claim

In concurrent February–March 2023 field trials, a YOLOv5-based computer vision model analyzing near-daily 3m/pixel Planet satellite imagery routed detections of land-applied manure to WDNR and to ELPC. Both organizations verified the presence of manure at similar rates (for example, about 35% for the highest-confidence detections), and on the few detections both followed up they agreed. Yet WDNR determined that only 11 of the 64 confirmed spreading events were clear violations of Wisconsin's winter-application rules; the rest were attributed to smaller AFOs below the 1,000-animal-unit CAFO threshold or to applications spread before February 1. ELPC, whose mission includes documenting environmental risk beyond current regulation, saw the tool as valuable for revealing the extent of winter spreading. The paper names this divergence the 'regulatory Rashomon effect': the same evidence, interpreted through different institutional mandates, yields opposite judgments of utility. It further reports that 82% of WDNR-confirmed dumping 'fell between these regulatory cracks,' and that a human pre-screening step substantially raised precision over the raw model.

Load-bearing premise

The claim that most confirmed winter spreading is legal depends on WDNR specialists' determinations that the post-February applications came from farms below the 1,000-animal-unit cutoff, which the paper could not verify directly from data.

Editorial extensions

If this is right

  • If AI tools are assessed through each organization's mandate, then accuracy benchmarks alone cannot predict whether a deployment will be judged a success; intended use must be part of the evaluation.
  • A human-in-the-loop pre-screening step, where domain experts review model outputs before field follow-up, raises precision above the raw model's, so deployed systems should budget for expert review.
  • Satellite-based detection can surface 450–1000% more violations than current complaint-driven processes, even when the absolute number of clear violations remains small.
  • Because most confirmed winter spreading is legally compliant under current thresholds, the same AI tool that helps enforcement also provides evidence that the law's bright lines (February 1; 1,000 animal units) may not match environmental risk.
  • For advocacy organizations, the tool can act as a force multiplier, turning confirmed detections into complaints or public-awareness campaigns that supplement limited regulator capacity.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the organizational divergence generalizes, then AI procurement decisions in government should specify not just accuracy targets but the decision the tool is meant to inform; the same model could be a success for an advocacy group and a failure for a regulator by design, not by accident.
  • The 82% figure, if it survives better data on facility sizes, implies that the environmental harm from winter spreading is dominated by unpermitted smaller farms, a policy target that neither current regulation nor most remote-sensing research addresses.
  • A testable extension would be to run the same detection pipeline with other state environmental agencies or with a regulator whose statute includes risk-based, graduated thresholds; the Rashomon effect predicts their usefulness ratings would move in the direction of the advocacy group's.
  • The paper's own evidence that pre-February spreading can be confirmed from historical satellite imagery suggests an inexpensive upgrade: filter out detections whose imagery time series shows application before the ban, which would raise the share of detections that are violations and could change WDNR's cost-benefit calculus.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper reports a concurrent field trial of a satellite-imagery computer vision model for detecting winter manure spreading, run with the Wisconsin Department of Natural Resources (WDNR) and the Environmental Law and Policy Center (ELPC) during February–March 2023. Both organizations verified detections at broadly similar rates and the model's confidence scores rank-ordered with ground truth, but the organizations diverged in their assessment of the tool: WDNR saw relatively few confirmed events as clear regulatory violations within its purview (11 of 64), while ELPC valued the documentation of environmental risk, including events that were compliant under current regulations. The paper introduces the label 'regulatory Rashomon effect' for this divergence and argues that the tool exposes gaps in existing law, reporting that 82% of confirmed WDNR detections fell 'between regulatory cracks' due to the February 1 temporal cutoff and the 1,000-animal-unit CAFO threshold.

Significance. If the headline findings hold, this is a valuable empirical contribution to the emerging literature on AI in environmental enforcement and organizational context in human-AI systems. The study is unusual in having two concurrent, independent field trials with the same model, with real verification effort (ELPC alone drove ~4,300 miles and spent 175 hours), and the authors have made the detection data and code public. The 'regulatory Rashomon effect' is a useful framing that goes beyond model-accuracy metrics. However, the central quantitative claim—the 82% 'regulatory cracks' figure, and WDNR's 'few clear violations' assessment—rests on attributions (pre-February timing and AFO vs. CAFO status) that the paper itself concedes cannot be fully verified. The qualitative organizational-context finding is plausible and interesting, but the paper's most cited number needs to be placed on firmer evidentiary ground.

major comments (3)
  1. [§3.2 and Supplementary Table S2] The paper counts all 27 detections 'reported to have been spread prior to February 1' as compliant, but its own manual imagery review substantiated only 'at least 18 of the 27 cases' as applied by February 1. Supplementary Table S2 lists 5 as 'February Application' and 4 as 'Unsure'. Counting all 27 as compliant inflates the 82% figure. Please recompute the compliant share excluding, or explicitly justifying, the 5 February applications and 4 unsure cases; at a minimum the paper should acknowledge that up to 9 of the 27 may not be pre-February events.
  2. [§3.2, Figure 4, and Discussion] The paper's headline 82% figure and WDNR's lukewarm assessment depend on the attribution of roughly 26 confirmed events to smaller, unregulated AFOs rather than CAFOs. The paper states this 'cannot be evaluated from the available data as directly' (§3.2). The interview-based evidence (calls to smaller farms, manure-type observations) is anecdotal and not recorded per detection. Because a substantial misattribution would change both the 82% figure and WDNR's cost-benefit calculus, the authors should either provide per-detection documentation of the AFO determination and its basis, or perform a sensitivity analysis showing how the headline percentages and the organizational-context interpretation change under alternative misattribution rates (e.g., 10%, 25%, 50%).
  3. [§3.2 and Discussion] The reported arithmetic does not appear to reconcile. If 64 events were confirmed, 11 were non-compliant, 27 were pre-February, and the remainder were AFOs, then the AFO share of post-February detections is roughly 26/(26+11) ≈ 70%, not the stated 62%. Conversely, 62% of the 37 post-February detections implies about 23 AFO events, leaving 30 pre-February events rather than 27. Please clarify the exact counts underlying Figure 4 and ensure the percentages in the text and abstract are consistent.
minor comments (4)
  1. [§2.2.2] Typo: 'Enviromental Law and Policy Center' should be 'Environmental Law and Policy Center'.
  2. [Throughout] The term 'dumping' is used for what is often a legal land application of manure; consider using 'manure spreading' or 'land application' in neutral descriptions to avoid conflating detection with illegality.
  3. [Figure 2B] The label 'Field Visible (Among Visited)' is clear, but the paper might also report how many visited detections could not be seen from public roads and whether that non-visibility is correlated with model confidence; the current panel suggests it is not, which is useful.
  4. [§4] The comparison between the model's predictive lift and 'random selection' (77x, 219x) is striking but would benefit from a clearer definition of the random-selection baseline, since the detection area is 6 km boxes around known CAFO sites, not a random statewide sample.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the field-trial claims rest on independent verification and organizational responses, not on fitted parameters or self-citation.

full rationale

This is an empirical observational study, not a derivation. The machine learning model was trained in prior published work [14] and is used here as a fixed decision-support tool; the present paper does not refit its parameters to the outcomes it reports. Detection verification was produced independently by ELPC field verifiers and WDNR specialists through site visits, imagery review, and follow-up with farms, then compared against model confidence scores. The organizational-context conclusion ('regulatory Rashomon effect') rests on qualitative interviews and the two organizations' divergent assessments of the same type of detections, not on the model's own outputs. The '82% of the dumping activity confirmed by WDNR fell between these regulatory cracks' figure depends on WDNR specialists' attribution of spreading events to smaller AFOs, an attribution the paper concedes 'cannot be evaluated from the available data as directly' (Section 3.2); that is an evidentiary limitation and a correctness risk, not circular reasoning, because the attribution is not defined in terms of the conclusion and was not fitted to produce the 82% figure. Self-citations to the authors' prior work establish the model's provenance and training data, but they are not load-bearing for the empirical findings reported here: the model's accuracy is validated against ground truth collected in the field during this study, and the organizational assessments come from independent human judgments. No prediction in the paper reduces by construction to its inputs, and no fitted parameter is renamed as a prediction. The paper is self-contained as an observational field study, so the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

This is an empirical study, so the ledger contains no mathematical free parameters. The three design choices listed shape the sample but are not fitted to the central claim. The axioms are the legal and ground-truth assumptions on which the compliance gap estimate rests. No new entities are introduced.

free parameters (3)
  • WDNR confidence threshold = 0.5
    Only detections with confidence >=0.5 were sent to WDNR. The threshold was selected during dry runs based on capacity and tolerance for false positives (Section 2.2.1, A.2.1).
  • Detection area side length = 6 km
    Inference ran over 6km x 6km boxes around permitted CAFO locations, chosen from partner conversations about likely hauling distances (Section 2.1).
  • ELPC participant radius = 25 km
    ELPC verifiers received the top 5 detections within 25km of them (Section 2.2.2). This shapes which detections were validated.
assumptions (4)
  • domain assumption Wisconsin administrative rule NR 243.14(6)(c) prohibits CAFOs from land-applying liquid waste in February and March, and solid waste on snow-covered or frozen ground, with exceptions.
    This legal framework defines what counts as a violation and underpins the compliance analysis (Section 1, 3.2).
  • domain assumption WDNR field determinations that a detected application was manure and that it was compliant are reliable ground truth.
    The 64 confirmed events and their compliance status come from WDNR specialists; the paper cannot independently verify AFO vs CAFO attribution (Section 3.2).
  • domain assumption ELPC verifiers' reports from public roads are valid indicators of manure presence.
    ELPC confirmation rates rely on volunteer verifiers seeing the field and identifying manure; visibility was 77% and the paper finds no strong selection by model score (Section 2.2.2).
  • domain assumption The satellite imagery and trained model from prior work [14] produce detections that correspond to actual manure application events.
    The study is a deployment of this model; the paper uses it as a fixed instrument and validates against field observations.

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Cite this review

Pith. "Pith review of Artificial Intelligence in Environmental Protection: The Importance of Organizational Context from a Field Study in Wisconsin." pith.science (2026). https://pith.science/paper/AYSV5UI3

@misc{pith2026250104902,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence in Environmental Protection: The Importance of Organizational Context from a Field Study in Wisconsin},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AYSV5UI3}},
  note         = {Machine review of arXiv:2501.04902}
}
read the original abstract

Advances in Artificial Intelligence (AI) have generated widespread enthusiasm for the potential of AI to support our understanding and protection of the environment. As such tools move from basic research to more consequential settings, such as regulatory enforcement, the human context of how AI is utilized, interpreted, and deployed becomes increasingly critical. Yet little work has systematically examined the role of such organizational goals and incentives in deploying AI systems. We report results from a unique case study of a satellite imagery-based AI tool to detect dumping of agricultural waste, with concurrent field trials with the Wisconsin Department of Natural Resources (WDNR) and a non-governmental environmental interest group in which the tool was utilized for field investigations when dumping was presumptively illegal in February-March 2023. Our results are threefold: First, both organizations confirmed a similar level of ground-truth accuracy for the model's detections. Second, they differed, however, in their overall assessment of its usefulness, as WDNR was interested in clear violations of existing law, while the interest group sought to document environmental risk beyond the scope of existing regulation. Dumping by an unpermitted entity or just before February 1, for instance, were deemed irrelevant by WDNR. Third, while AI tools promise to prioritize allocation of environmental protection resources, they may expose important gaps of existing law.

Figures

Figures reproduced from arXiv: 2501.04902 by the authors.

Figure 1
Figure 1. Example of satellite imagery (left) and ground-verified (right) detection of manure spreading in Grant County, WI. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Process metrics from the ELPC trial. (A) Follow-up rate by model score. (B) Among detections visited, ELPC verifiers were able [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Detection validation rates by model confidence for WDNR (A, B) and ELPC (C). (A) and (C) show the overall confirmation rate [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Determination of regulatory compliance for the 64 spreading events confirmed by WDNR. Only a small fraction (17%) were [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Example of satellite imagery confirming a manure application detected by our model as of February 2 had been spread in late [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

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Reference graph

Works this paper leans on

83 extracted references · 73 canonical work pages

  1. [1]

    Animal feeding operations in Wisconsin

    2022. Animal feeding operations in Wisconsin. https://www.ewg.org/interactive-maps/2022-wisconsin-afo/map/. Online; Accessed: March 22, 2022

    2022

  2. [2]

    Klaus Ackermann, Joe Walsh, Adolfo De Unánue, Hareem Naveed, Andrea Navarrete Rivera, Sun-Joo Lee, Jason Bennett, Michael Defoe, Crystal Cody, Lauren Haynes, et al. 2018. Deploying machine learning models for public policy: A framework. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 15–22

    2018

  3. [3]

    Kasun Amarasinghe, Kit T Rodolfa, Hemank Lamba, and Rayid Ghani. 2023. Explainable machine learning for public policy: Use cases, gaps, and research directions. Data & Policy 5 (2023), e5

    2023

  4. [4]

    Robert Anderson. 2016. The Rashomon Effect and Communication. Canadian Journal of Communication 41, 2 (2016), 249–270. https://doi.org/10. 22230/cjc.2016v41n2a3068 arXiv:https://doi.org/10.22230/cjc.2016v41n2a3068

    work page doi:10.22230/cjc.2016v41n2a3068 2016

  5. [5]

    Bardach, R.A

    E. Bardach, R.A. Kagan, and Twentieth Century Fund. 1982. Going by the Book: The Problem of Regulatory Unreasonableness . Temple University Press. https://books.google.com/books?id=GtO_QgAACAAJ

    work page 1982

  6. [6]

    Sandeep Battula, MNVSS Kumar, Santosh Kumar Panda, Uma Maheshwara Rao, Ganesh Laveti, and B Mouli. 2020. Online ocean monitoring using edge IoT. In Global Oceans 2020: Singapore–US Gulf Coast . IEEE, 1–7

    work page 2020

  7. [7]

    Scott A Bradford, Eran Segal, Wei Zheng, Qiquan Wang, and Stephen R Hutchins. 2008. Reuse of concentrated animal feeding operation wastewater on agricultural lands. Journal of Environmental Quality 37, S5 (2008), S–97

    work page 2008

  8. [8]

    Leo Breiman. 2001. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statist. Sci. 16, 3 (2001), 199 – 231. https://doi.org/10.1214/ss/1009213726

    arXiv 2001

Show all 83 references
  1. [9]

    Oliver R Browne, Ludovica Gazze, Michael Greenstone, and Olga Rostapshova. 2023. Man vs. machine: Technological promise and political limits of automated regulation enforcement. The Review of Economics and Statistics (2023), 1–36

    2023

  2. [10]

    JoAnn Burkholder, Bob Libra, Peter Weyer, Susan Heathcote, Dana Kolpin, Peter S Thorne, and Michael Wichman. 2007. Impacts of waste from concentrated animal feeding operations on water quality. Environmental health perspectives 115, 2 (2007), 308–312

    2007

  3. [11]

    Enzo R Campagnolo, Kammy R Johnson, Adam Karpati, Carol S Rubin, Dana W Kolpin, Michael T Meyer, J Emilio Esteban, Russell W Currier, Kathleen Smith, Kendall M Thu, et al. 2002. Antimicrobial residues in animal waste and water resources proximal to large-scale swine and poultr...

    2002

  4. [12]

    Hao-Fei Cheng, Logan Stapleton, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhi- wei Steven Wu, and Haiyi Zhu. 2022. How child welfare workers reduce racial disparities in algorithmic decisions. In Proceedings of the 2022 CHI ...

    2022

  5. [13]

    Ben Chugg, Brandon Anderson, Seiji Eicher, Sandy Lee, and Daniel E Ho. 2021. Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms. International Journal of Applied Earth Observation ...

    2021

  6. [14]

    Ben Chugg, Nicolas Rothbacher, Alex Feng, Xiaoqi Long, and Daniel E. Ho. 2022. Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act. In Proceedings of the 31st ACM International Conference on Information & Know...

    2022

  7. [15]

    Wai Tong Chung, Bassem Akoush, Pushan Sharma, Alex Tamkin, Ki Sung Jung, Jacqueline Chen, Jack Guo, Davy Brouzet, Mohsen Talei, Bruno Savard, et al. 2023. Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data. arXiv preprin...

    2023 arXiv

  8. [16]

    Jörg Claussen, Tobias Kretschmer, and Philip Mayrhofer. 2013. The effects of rewarding user engagement: The case of Facebook apps. Information Systems Research 24, 1 (2013), 186–200

    2013

  9. [17]

    Matthew Collins and John Curtis. 2018. Bunching of residential building energy performance certificates at threshold values. Applied Energy 211 (2018), 662–676

    2018

  10. [18]

    Marie P Defina and Leonard Wetherbee. 1997. Advocacy and law enforcement: Partners against domestic violence. FBI L. Enforcement Bull. 66 (1997), 22

    1997

  11. [19]

    Joseph J Doyle Jr. 2007. Child protection and child outcomes: Measuring the effects of foster care.American Economic Review 97, 5 (2007), 1583–1610

    2007

  12. [20]

    David Freeman Engstrom and Amit Haim. 2023. Regulating Government AI and the Challenge of Sociotechnical Design. Annual Review of Law and Social Science 19 (2023)

    2023

  13. [21]

    Jonathan S Feinstein. 1989. The safety regulation of US nuclear power plants: Violations, inspections, and abnormal occurrences. Journal of political Economy 97, 1 (1989), 115–154

    1989

  14. [22]

    Riccardo Fogliato, Maria De-Arteaga, and Alexandra Chouldechova. 2022. A case for humans-in-the-loop: Decisions in the presence of misestimated algorithmic scores. A vailable at SSRN (2022)

    2022

  15. [23]

    Charles P Gerba and James E Smith. 2005. Sources of pathogenic microorganisms and their fate during land application of wastes. Journal of environmental quality 34, 1 (2005), 42–48

    2005

  16. [24]

    Cynthia Giles. 2022. Next Generation Compliance: Environmental Regulation for the Modern Era . Oxford University Press. https://doi.org/10.1093/ oso/9780197656747.001.0001 arXiv:https://academic.oup.com/book/44438/book-pdf/49945790/9780197656754_web.pdf

    2022

  17. [25]

    Government Accountability Office. 2021. Clean Water Act: EPA Needs to Better Assess and Disclose Quality of Compliance and Enforcement Data . 13 RegLab

    2021

  18. [26]

    Cassandra Handan-Nader and Daniel E Ho. 2019. Deep learning to map concentrated animal feeding operations. Nature Sustainability 2, 4 (2019), 298–306

    2019

  19. [27]

    Cassandra Handan-Nader, Daniel E Ho, and Larry Y Liu. 2021. Deep learning with satellite imagery to enhance environmental enforcement. In Data Science Applied to Sustainability Analysis . Elsevier, 205–228

    2021

  20. [28]

    Travis A Hanselman, Donald A Graetz, and Ann C Wilkie. 2003. Manure-borne estrogens as potential environmental contaminants: a review. Environmental science & technology 37, 24 (2003), 5471–5478

    2003

  21. [29]

    Karl G. Heider. 1988. The Rashomon Effect: When Ethnographers Disagree. American Anthropologist 90, 1 (1988), 73–81. https://doi.org/10.1525/aa. 1988.90.1.02a00050 arXiv:https://anthrosource.onlinelibrary.wiley.com/doi/pdf/10.1525/aa.1988.90.1.02a00050

    1988 doi

  22. [30]

    Miyuki Hino, Elinor Benami, and Nina Brooks. 2018. Machine learning for environmental monitoring. Nature Sustainability 1, 10 (2018), 583–588

    2018

  23. [31]

    Daniel E Ho. 2017. Does peer review work? An experiment of experimentalism. Stan. L. Rev. 69 (2017), 1

    2017

  24. [32]

    Ho, Becky Elias, and Driscoll Sharon

    Daniel E. Ho, Becky Elias, and Driscoll Sharon. 2016. Improving Governance by Peer Review: Food Safety and Beyond. https://law.stanford.edu/ 2016/07/06/improving-governance-by-peer-review-food-safety-and-beyond/

    2016

  25. [33]

    Matus Hodul, Anders Knudby, Brigid McKenna, Amy James, Charles Mayo, Moira Brown, Delphine Durette-Morin, and Stephen Bird. 2023. Individual North Atlantic right whales identified from space. Marine Mammal Science 39, 1 (2023), 220–231

    2023

  26. [34]

    Sébastien Houde. 2022. Bunching with the stars: How firms respond to environmental certification. Management Science 68, 8 (2022), 5569–5590

    2022

  27. [35]

    International Joint Commission. 2019. Oversight of Animal Feeding Operations for Manure Management in the Great Lakes Basin. (2019)

    2019

  28. [36]

    Abolfazl Jaafari, Eric K Zenner, Mahdi Panahi, and Himan Shahabi. 2019. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and forest meteorology 266 (2019), 198–207

    2019

  29. [37]

    Piyush Jain, Sean CP Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, and Mike D Flannigan. 2020. A review of machine learning applications in wildfire science and management. Environmental Reviews 28, 4 (2020), 478–505

    2020

  30. [38]

    Seongeun Jeong, Marc L Fischer, Hanna Breunig, Alison R Marklein, Francesca M Hopkins, and Sebastien C Biraud. 2022. Artificial intelligence approach for estimating dairy methane emissions. Environmental science & technology 56, 8 (2022), 4849–4858

    2022

  31. [39]

    Glenn Jocher. 2020. Ultralytics YOLOv5. https://doi.org/10.5281/zenodo.3908559

    2020 doi

  32. [40]

    Saakshi Kapoor, Mukesh Kumar, and Manisha Kaushal. 2023. Deep learning based whale detection from satellite imagery. Sustainable Computing: Informatics and Systems 38 (2023), 100858

    2023

  33. [41]

    Arpan Kumar Kar, Shweta Kumari Choudhary, and Vinay Kumar Singh. 2022. How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production (2022), 134120

    2022

  34. [42]

    S. D. Klausner, P. J. Zwerman, and D. F. Ellis. 1976. Nitrogen and Phosphorus Losses from Winter Disposal of Dairy Manure.Journal of Environmental Quality 5, 1 (1976), 47–49

    1976

  35. [43]

    Kristin M Kostick-Quenet and Sara Gerke. 2022. AI in the hands of imperfect users. npj Digital Medicine 5, 1 (2022), 197

    2022

  36. [44]

    David Lauer. 2021. Facebook’s ethical failures are not accidental; they are part of the business model. AI and Ethics 1, 4 (2021), 395–403

    2021

  37. [45]

    Wisconsin State Legislature. [n. d.]. NR 243.14 Nutrient management. https://docs.legis.wisconsin.gov/code/admin_code/nr/200/243/ii/14. Online; Accessed May 9, 2022

    2022

  38. [46]

    Mark A Lemley and Bhaven Sampat. 2012. Examiner characteristics and patent office outcomes. Review of economics and statistics 94, 3 (2012), 817–827

    2012

  39. [47]

    Phillip S Levin, Steven A Gray, Christian Möllmann, and Adrian C Stier. 2020. Perception and Conflict in Conservation: The Rashomon Effect. BioScience 71, 1 (10 2020), 64–72. https://doi.org/10.1093/biosci/biaa117 arXiv:https://academic.oup.com/bioscience/article- pdf/71/1/64/...

    2020 doi

  40. [48]

    Chasalow, and Sarah Riley

    Karen Levy, Kyla E. Chasalow, and Sarah Riley. 2021. Algorithms and Decision-Making in the Public Sector.Annual Review of Law and Social Science 17, 1 (2021), 309–334. https://doi.org/10.1146/annurev-lawsocsci-041221-023808 arXiv:https://doi.org/10.1146/annurev-lawsocsci-041221-023808

    2021 doi

  41. [49]

    Theodore W Lewis and Joseph C Makarewicz. 2009. Winter application of manure on an agricultural watershed and its impact on downstream nutrient fluxes. Journal of Great Lakes Research 35 (2009), 43–49

    2009

  42. [50]

    Jian Liu, Peter JA Kleinman, Helena Aronsson, Don Flaten, Richard W McDowell, Marianne Bechmann, Douglas B Beegle, Timothy P Robinson, Ray B Bryant, Hongbin Liu, et al. 2018. A review of regulations and guidelines related to winter manure application. Ambio 47, 6 (2018), 657–670

    2018

  43. [51]

    Xueping Liu, Wenfeng Zhang, Yuanan Hu, Erdan Hu, Xiande Xie, Lingling Wang, and Hefa Cheng. 2015. Arsenic pollution of agricultural soils by concentrated animal feeding operations (CAFOs). Chemosphere 119 (2015), 273–281

    2015

  44. [52]

    Michael A Mallin and Lawrence B Cahoon. 2003. Industrialized animal production—a major source of nutrient and microbial pollution to aquatic ecosystems. Population and Environment 24, 5 (2003), 369–385

    2003

  45. [53]

    Michael A Mallin, Matthew R McIver, Anna R Robuck, and Amanda Kahn Dickens. 2015. Industrial swine and poultry production causes chronic nutrient and fecal microbial stream pollution. Water, Air, & Soil Pollution 226, 12 (2015), 1–13

    2015

  46. [54]

    J.L. Mashaw. 1983. Bureaucratic Justice: Managing Social Security Disability Claims . Yale University Press. https://books.google.com/books?id= d2sc2Mea9LEC

    1983

  47. [55]

    Jerry L Mashaw. 1978. Social security hearings and appeals: A study of the Social Security Administration hearing system. (No Title) (1978)

    1978

  48. [56]

    Anu Mittal. 2009. Concentrated animal feeding operations: EPA needs more information and a clearly defined strategy to protect air and water quality from pollutants of concern . DIANE Publishing. 14 Artificial Intelligence in Environmental Protection

    2009

  49. [57]

    J Carlos Molina-Molina, Marouane Salhaoui, Antonio Guerrero-González, and Mounir Arioua. 2021. Autonomous marine robot based on AI recognition for permanent surveillance in marine protected areas. Sensors 21, 8 (2021), 2664

    2021

  50. [58]

    Ehsan Mosadegh and Anne W Nolin. 2022. A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery. Remote Sensing 14, 19 (2022), 4979

    2022

  51. [59]

    Yingfei Mu, Edward A Rubin, and Eric Zou. 2021. What’s Missing in Environmental (Self-)Monitoring: Evidence from Strategic Shutdowns of Pollution Monitors. Working Paper 28735. National Bureau of Economic Research. https://doi.org/10.3386/w28735

    2021 doi

  52. [60]

    Keeve E Nachman, Jay P Graham, Lance B Price, and Ellen K Silbergeld. 2005. Arsenic: a roadblock to potential animal waste management solutions. Environmental Health Perspectives 113, 9 (2005), 1123–1124

    2005

  53. [61]

    Rohit Nishant, Mike Kennedy, and Jacqueline Corbett. 2020. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management 53 (2020), 102104

    2020

  54. [62]

    New York City Department of Health & Mental Hygiene. 2016. What to Expect When You’re Inspected: A Guide for Food Service Operators. , 3, 4, 14-15 pages. https://www.nyc.gov/assets/doh/downloads/pdf/rii/blue-book.pdf

    2016

  55. [63]

    Government Accountability Office

    U.S. Government Accountability Office. 2005. Nursing Homes: Despite Increased Oversight, Challenges Remain in Ensuring High-Quality Care and Resident Safety

    2005

  56. [64]

    Samir Passi and Solon Barocas. 2019. Problem formulation and fairness. In Proceedings of the conference on fairness, accountability, and transparency . 39–48

    2019

  57. [65]

    D Raj Raman, Elizabeth L Williams, Alice C Layton, Robert T Burns, James P Easter, Adam S Daugherty, Michael D Mullen, and Gary S Sayler. 2004. Estrogen content of dairy and swine wastes. Environmental Science & Technology 38, 13 (2004), 3567–3573

    2004

  58. [66]

    Jaya Ramji-Nogales, Andrew I Schoenholtz, and Philip G Schrag. 2007. Refugee roulette: Disparities in asylum adjudication. Stan. L. Rev. 60 (2007), 295, 334

    2007

  59. [67]

    Caleb Robinson, Ben Chugg, Brandon Anderson, Juan M Lavista Ferres, and Daniel E Ho. 2022. Mapping industrial poultry operations at scale with deep learning and aerial imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022)

    2022

  60. [68]

    Caleb Robinson, Anthony Ortiz, Juan M Lavista Ferres, Brandon Anderson, and Daniel E Ho. 2021. Temporal cluster matching for change detection of structures from satellite imagery. In ACM SIGCAS Conference on Computing and Sustainable Societies . 138–146

    2021

  61. [69]

    Reva Schwartz, Apostol Vassilev, Kristen Greene, Lori Perine, Andrew Burt, Patrick Hall, et al . 2022. Towards a standard for identifying and managing bias in artificial intelligence. NIST special publication 1270, 10.6028 (2022)

    2022

  62. [70]

    Daniele Silvestro, Stefano Goria, Thomas Sterner, and Alexandre Antonelli. 2022. Improving biodiversity protection through artificial intelligence. Nature sustainability 5, 5 (2022), 415–424

    2022

  63. [71]

    Venkatesh Sivaraman, Leigh A Bukowski, Joel Levin, Jeremy M Kahn, and Adam Perer. 2023. Ignore, trust, or negotiate: understanding clinician acceptance of AI-based treatment recommendations in health care. In Proceedings of the 2023 CHI Conference on Human Factors in Computing...

    2023

  64. [72]

    Tom Slee. 2020. The incompatible incentives of private-sector AI. The Oxford Handbook of Ethics of AI (2020), 106–123

    2020

  65. [73]

    Stacy Sneeringer and Nigel Key. 2011. Effects of size-based environmental regulations: Evidence of regulatory avoidance. American Journal of Agricultural Economics 93, 4 (2011), 1189–1211

    2011

  66. [74]

    MS Srinivasan, Ray B Bryant, Michael P Callahan, and Jennifer L Weld. 2006. Manure management and nutrient loss under winter conditions: A literature review. Journal of soil and water conservation 61, 4 (2006), 200–209

    2006

  67. [75]

    Megan T Stevenson and Jennifer L Doleac. 2022. Algorithmic risk assessment in the hands of humans. A vailable at SSRN 3489440 (2022)

    2022

  68. [76]

    Planet Team. 2017. Planet Application Program Interface: In Space for Life on Earth. https://api.planet.com

    2017

  69. [77]

    USEPA. 2003. National Pollutant Discharge Elimination System permit regulation and effluent limitation guidelines and standards for concentrated animal feeding operations (CAFOs); final rule. Fed. Regist. 68, 29 (2003), 7176–7274

    2003

  70. [78]

    Michael Veale and Irina Brass. 2019. Administration by algorithm? Public management meets public sector machine learning. Public management meets public sector machine learning (2019)

    2019

  71. [79]

    Max Weber. 2019. Economy and society: A new translation . Harvard University Press

    2019

  72. [80]

    Zihan Wei, Miki Verma, Yuchen Wu, Sarfaraz Alam, Brandon Anderson, Daniel Ho, and Jenny Suckale. 2021. Attributing Sources of Surface Water Pollutants in the Maumee River Basin Using Network Modeling. In AGU Fall Meeting Abstracts, Vol. 2021. H14A–05

    2021

  73. [81]

    Mark R Williams, Gary W Feyereisen, Douglas B Beegle, Robert D Shannon, Gordon J Folmar, and Ray B Bryant. 2011. Manure application under winter conditions: Nutrient runoff and leaching losses. Transactions of the ASABE 54, 3 (2011), 891–899

    2011

  74. [82]

    Matthew D Zinn. 2002. Policing environmental regulatory enforcement: cooperation, capture, and citizen suits. Stan. Envtl. LJ 21 (2002), 81

    2002

  75. [83]

    refugee roulette

    Eric Yongchen Zou. 2021. Unwatched Pollution: The Effect of Intermittent Monitoring on Air Quality. American Economic Review 111, 7 (July 2021), 2101–26. https://doi.org/10.1257/aer.20181346 15 RegLab A APPENDIX A.1 Additional Related Work A.1.1 Frontline Regulator Discretion ...

    2021 doi

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