{"id":683,"date":"2024-10-02T11:16:28","date_gmt":"2024-10-02T03:16:28","guid":{"rendered":"https:\/\/impetusanimalwelfare.org\/?page_id=683"},"modified":"2025-01-14T14:51:05","modified_gmt":"2025-01-14T06:51:05","slug":"will-artificial-intelligence-transform-the-way-we-assess-animal-welfare","status":"publish","type":"page","link":"https:\/\/impetusanimalwelfare.org\/will-artificial-intelligence-transform-the-way-we-assess-animal-welfare\/","title":{"rendered":"Will artificial intelligence transform the way we assess animal welfare?"},"content":{"rendered":"\t\t
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Will artificial intelligence transform the way we assess animal welfare?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Current methods of assessing animal welfare <\/em><\/strong><\/p>\n

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Author: Sarah Babington<\/em><\/strong><\/p>\n

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In recent decades there has been a growing focus on ethical and sustainable farming practices of which animal welfare is a critical component. Although there is no formally agreed upon definition of animal welfare, there is some consensus that the welfare of an animal is influenced by their experience of the world which is influenced by both internal and external factors (Fernandes et al., 2021). Frameworks such as the Five Freedoms have been extensively used to help define and assess animal welfare in different contexts, with a focus on minimising and preventing negative experiences during an animal\u2019s lifetime (Duncan, 2019). More recent frameworks, like the Five Domains, have expanded upon the concept of the Five Freedoms to recognise the importance of mental state on an animal\u2019s overall welfare and the need to provide opportunities for positive experiences in addition to minimising negative experiences over an animal\u2019s lifetime (Mellor, 2016). <\/p>\n

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The assessment of animal welfare typically relies upon a collection of several measures that may be animal-based, resource-based, and management-based. Animal-based measures mainly include behavioural, physiological, and neurobiological indicators (for examples see Table 1) which are used to assess how an animal is responding to a situation, which can infer their experience and welfare state. While these current animal-based measures provide important information on the welfare of an animal, they have limitations as they can be non-specific, subjective, and mostly reflect negative rather than positive welfare states (Babington et al., 2024). Resource-based and management-based measures (for examples see Table 1) can also be used in addition to animal-based measures to understand the influence of external factors on animal welfare. <\/p>\n

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Table 1. <\/strong>Examples of measures used to assess animal welfare (adapted from (Babington et al., 2024).<\/span> <\/strong><\/p>\n<\/div>\n

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Type of measure <\/strong><\/td>\nExamples <\/strong><\/td>\n<\/tr>\n
Animal-based measures <\/em><\/td>\nBehavioural<\/em><\/td>\n Behavioural observations (e.g., stereotypies, aggression, behavioural diversity)Fear testsCognitive bias tests Preference and motivation testsQualitative Behavioural Assessment Facial expressions (e.g., grimace scale) Vocalizations<\/span><\/td>\n<\/tr>\n
Physiological <\/em><\/td>\nCatecholamines (e.g., epinephrine and norepinephrine) Glucocorticoids (e.g., cortisol and corticosterone)DehydroepiandrosteroneReproductive hormones (e.g., luteinizing hormone, gonadotrophin-releasing hormone)Fatty acid intermediates (e.g., prostaglandins)Metabolic enzymes (e.g., glucose, lactate dehydrogenase, creatine kinase)Inflammatory markers (e.g., serum amyloid A, C-reactive protein)Immune system markers (e.g., total white blood cells, IL-6, IgA)Cardiovascular function (e.g., heart rate, heart rate variability, blood pressure)Respiratory function (e.g., respiratory rate)Core body temperature <\/td>\n<\/tr>\n
Neurobiological <\/em><\/td>\nNeurochemicals (e.g., dopamine, serotonin, endogenous opioids)Brain activity via changes in blood flow with functional magnetic resonance imagingBrain activity via changes in blood oxygenation with functional near infrared spectroscopyElectrical brain activity with electroencephalography<\/td>\n<\/tr>\n
Resource-based measures  <\/em><\/td>\nHousing system Feed and water quality and quantity Space allowance Indoor housing environment (e.g., air quality, temperature and humidity, lighting programme) Outdoor or pasture environment Transport vehicle <\/td>\n<\/tr>\n
Management-based measures <\/em><\/td>\nPersonnel training and competency Handling practices Routine husbandry procedures (e.g., vaccinations, dehorning, castration) Biosecurity management Business Standard Operating Procedures <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

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The need for objectivity when assessing animal welfare<\/em><\/strong><\/p>\n

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Current animal welfare measures can be non-specific and subjective resulting in a need for new measures or ways of collecting data that are more objective and specific. Having objective and standardised measures when assessing animal welfare is critical to ensure consistent, reliable, and quantifiable data to inform decisions around management practices and regulatory changes. This is where technological advances can provide some advantages. Artificial intelligence systems have shown some promise in addressing some of the current limitations associated with how animal welfare is assessed. The various visual and sensor technologies available can reduce the reliance on human observation when assessing welfare, which not only addresses the risk of subjectivity but also provides personnel more time to focus on tasks they are uniquely qualified to perform. These technologies are also able to collect much larger amounts of data than ever before which artificial intelligence systems can analyse in real-time allowing for improved efficiency and ability to establish a welfare benchmark that progress can be tracked against or corrective actions can be implemented. <\/p>\n

Artificial intelligence systems <\/em><\/strong><\/p>\n

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Artificial intelligence encompasses various types of models, of which some of the ones commonly used in the context of animal welfare include machine learning, deep learning, and neural networks. Briefly, machine learning uses algorithms which are trained against large datasets to identify patterns and create predications and decision models that can then be applied to analyse new unseen datasets (Choi et al., 2020). Deep learning is a type of machine learning that can automatically recognise patterns in datasets from images, video, or audio, by using multilayered neural networks to mimic the decision-making process made by humans (Choi et al., 2020). Deep learning algorithms are often used for tasks too complicated for standard machine learning. Neural Networks are a type of machine learning that are used in deep learning algorithms, which are designed to process data like the neuronal system in the brain to mimic how decisions are made by humans (Choi et al., 2020). These different types of artificial intelligence may be used by themselves or in hybrid models where the different algorithms are integrated to minimise the limitations of the models or improve performance (Bao & Xie, 2022). <\/p>\n

Benefits and challenges of using artificial intelligence to assess animal welfare<\/em><\/strong><\/p>\n

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Artificial intelligence systems applied to assessing animal welfare are able to collect large amounts of data via images, video, audio, or sensor data which can then be analysed in real-time to identify patterns, specific factors, form predictions, and create decision-making models. Some of the possible benefits of applying artificial intelligence in farming systems to assess animal welfare include (Zhang et al., 2024): <\/p>\n

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