Unraveling the Hidden Environmental Impacts of AI Solutions for Environment

Ligozat, Anne-Laure; Longhi, Julien; Bugeau, Aurélie; Combaz, Jacques

doi:10.48550/arxiv.2110.11822

Cited by 4 publications

(6 citation statements)

References 18 publications

(33 reference statements)

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“…We argue that choosing the term "risk" assessment could entail only a preemptive approach toward assessing AI systems' negative impacts while introducing less-used terminology. Finally, we also point out that many of the known negative impacts of AI systems are currently documented in the form of "impacts" in many publications that present ethical assessments [101][102][103][104][105][106].…”

Section: Defining Risk and Impact With Algorithmic Impact Assessmentmentioning

confidence: 99%

Crossing the principle–practice gap in AI ethics with ethical problem-solving

Corrêa,

Santos,

Galvão

et al. 2024

AI Ethics

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The past years have presented a surge in (AI) development, fueled by breakthroughs in deep learning, increased computational power, and substantial investments in the field. Given the generative capabilities of more recent AI systems, the era of large-scale AI models has transformed various domains that intersect our daily lives. However, this progress raises concerns about the balance between technological advancement, ethical considerations, safety measures, and financial interests. Moreover, using such systems in sensitive areas amplifies our general ethical awareness, prompting a re-emergence of debates on governance, regulation, and human values. However, amidst this landscape, how to bridge the principle–practice gap separating ethical discourse from the technical side of AI development remains an open problem. In response to this challenge, the present work proposes a framework to help shorten this gap: ethical problem-solving (EPS). EPS is a methodology promoting responsible, human-centric, and value-oriented AI development. The framework’s core resides in translating principles into practical implementations using impact assessment surveys and a differential recommendation methodology. We utilize EPS as a blueprint to propose the implementation of an Ethics as a Service Platform, currently available as a simple demonstration. We released all framework components openly and with a permissive license, hoping the community would adopt and extend our efforts into other contexts. Available in the following URL https://nkluge-correa.github.io/ethical-problem-solving/.

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Section: Defining Risk and Impact With Algorithmic Impact Assessmentmentioning

confidence: 99%

Crossing the principle–practice gap in AI ethics with ethical problem-solving

Corrêa,

Santos,

Galvão

et al. 2024

AI Ethics

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“…At the same time the ML researchers began to realize that training models with more and more parameters required a lot of energy and, as a consequence, GHG emissions, questioning the complete environmental impacts of AI methods for the environment (Schwartz et al, 2020). Based on this concern, Ligozat et al (2021) propose to study the possible negative impact of AI systems often presented as a solution to climate change, presenting different methodologies used to assess this impact, in particular life cycle assessment. For instance, recent advances in large Transformer models have raised public concerns on their environmental footprint at the time of designing and developing the models .…”

Section: Green Aimentioning

confidence: 99%

Machine Learning methods in climate finance: a systematic review

Alonso-Robisco

Carbó

2023

Documentos De Trabajo

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Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area.

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“…Perhaps most similar to our work, EnergyVis [41] is an interactive tool for visualizing and comparing energy consumption of ML models as a function of hardware and physical location (U.S. state), given metadata about a model's energy use per epoch. Other studies have gone beyond simply tracking the emissions from training models, aiming to quantify the emissions resulting from manufacturing computing hardware [15], the broader impacts of sustainable AI [49], and the methodologies used to assess those impacts [21,26]. Building upon this research, efforts have also been made to certify systems as being socially-and environmentally-conscious [14], working towards comparing both the environmental costs and potential benefits of AI models in order to paint a more holistic picture of AI.…”

Section: Related Workmentioning

confidence: 99%

“…emissions that indirectly result from all other business activities, such as those associated with the upstream raw materials extraction, manufacturing, and delivery of cloud-based IT asset infrastructure such as servers from suppliers to be used in a cloud provider's datacenters). Both of these types of emissions warrant discussion and debate by the AI community-and indeed some work has begun on the subject, e.g., [21,26]-but we are missing a more concrete structure for categorizing, quantifying and mitigating the different scopes of emissions in our field. This would involve the active participation of specific stakeholders to establish the tooling and reporting required to better estimate these aspects, which is a challenge in itself.…”

Section: Future Directionsmentioning

confidence: 99%

“…While it is not yet known what proportion of data center use is for training artificial intelligence (AI) models, it is undeniable that AI and its sub-fields have grown dramatically in recent years, with no sign of slowing down [39,43]. While a number of papers have addressed the CO 2 emissions produced by AI (e.g., [23,26,33,34]), the extent and provenance of CO 2 emissions in the field is still under-explored. Nonetheless, a common theme of previous work is that it aims to estimate the emissions produced by training AI models, or carrying out the accompanying neural architecture search (NAS) process, based on coarse measures such as CO 2 emissions of electricity used in the region where the computations were carried out (e.g., [42]), or post-hoc analyses using information that is not publicly available (e.g., [34]).…”

Section: Introductionmentioning

confidence: 99%

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Measuring the Carbon Intensity of AI in Cloud Instances

Dodge

Prewitt

Combes

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint. As a result, recent scholarship has called for better estimates of the greenhouse gas impact of AI: data scientists today do not have easy or reliable access to measurements of this information, which precludes development of actionable tactics. We argue that cloud providers presenting information about software carbon intensity to users is a fundamental stepping stone towards minimizing emissions.In this paper, we provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions by using location-based and time-specific marginal emissions data per energy unit. We provide measurements of operational software carbon intensity for a set of modern models covering natural language processing and computer vision applications, and a wide range of model sizes, including pretraining of a 6.1 billion parameter language model. We then evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform: using cloud instances in different geographic regions, using cloud instances at different times of day, and dynamically pausing cloud instances when the marginal carbon intensity is above a certain threshold. We confirm previous results that the geographic region of the data center plays a significant role in the carbon intensity for a given cloud instance, and find that choosing an appropriate region can have the largest operational emissions reduction impact. We also present new results showing that the time of day has meaningful impact on operational software carbon intensity.Finally, we conclude with recommendations for how machine learning practitioners can use software carbon intensity information to reduce environmental impact.

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Unraveling the Hidden Environmental Impacts of AI Solutions for Environment

Cited by 4 publications

References 18 publications

Crossing the principle–practice gap in AI ethics with ethical problem-solving

Crossing the principle–practice gap in AI ethics with ethical problem-solving

Machine Learning methods in climate finance: a systematic review

Measuring the Carbon Intensity of AI in Cloud Instances

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