[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122412-en":3,"doc-seo-122412-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122412,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Access and benefit sharing biological materials for machines - Artificial intelligence, machine learning and deep learning","Global regulation of access and benefit sharing (ABS) for biological materials increasingly extends to the information and knowledge used alongside those materials. Artificial intelligence systems introduce existential challenges across ABS frameworks as data for training, testing/verification, and application must be governed fairly and equitably. The paper explores explainability to support fair benefit sharing and addresses ownership negotiation for input data and the tracing of data through AI models.","Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning  \nAuthor  \nLawson , C , Englezos , E , Rourke , M  \nPublished 2025  \nJournal Title Plants People Planet  \nVersion  \nVersion of Record (VoR)  \nDOI  \n10.1002/ppp3.70007  \nRights statement  \n© 2025 The Author(s) . Plants , People , Planet published by John Wiley & Sons Ltd on behalf of New Phytologist Foundation. This is an open access article under the terms of the Creative Commons Attribution License , which permits use , distribution and reproduction in any medium , provided the original work is properly cited.  \nDownloaded from  \n[https://hdl.handle.net/10072/436149](https://hdl.handle.net/10072/436149)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nReceived: 13 November 2024 Revised: 17 January 2025 Accepted: 6 February 2025  \nDOI: 10.1002/ppp3.70007  \nRES EARCH A RTICLE  \nAccess and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning  \nCharles Lawson 1  | Elizabeth Englezos 1  | Michelle Rourke 2   \n1Griffith Law School, Griffith University, Southport, Queensland, Australia 2Griffith Law School, Griffith University, Nathan, Queensland, Australia  \nCorrespondence  \nCharles Lawson, Griffith Law School, Griffith University, Parklands Dr, Southport, Queensland 4222, Australia  \nEmail: [c.lawson@griffith.edu.au](c.lawson@griffith.edu.au)  \nSocietal Impact Statement  \nFuture research and development of biological materials for foods, feeds, fibres, materials and medicines will increasingly rely on information and knowledge using Artificial Intelligence (AI) applications for detecting patterns to make useful decisions. The access and use of this information and knowledge is increasingly being regulated under international laws according to an ideal that delivers money and other benefits from the uses of the information and knowledge. We conclude these issues will require specific attention to ensure the ideals of fair and equitable benefit sharing are sustainable and can deliver real benefits.  \nSummary  \n• The global regulation of access and benefit sharing (ABS) biological materials is starting to impose complex rules for governing information and knowledge about those materials. Artificial Intelligence (AI) poses existential challenges for the research and development of those materials for foods, feeds, fibres, materials and medicines under these ABS schemes.  \n• We speculate these challenges are in three distinct scenarios: (1) data used to train the AI models; (2) data used to test (and verify) the AI models; and (3) data used in applying the models to reveal useful patterns. Building in ‘explainability’ to the AI algorithms may be a solution, at least in part, to delivering on fair and equitable ABS.  \n• We then posit two issues for ABS: (1) negotiating the ownership status to use the input data with each data owner for training, testing (and verifying) and using the models (although a further complication here is that most data is without an owner because it is already open and free as a public domain without intellectual property restrictions); and (2) following the data per se through the AI models (explainability) .  \n• We conclude that how ABS will be addressed in developing and applying AI models will require careful consideration to avoid the apparent dulling effects of current ABS regulation and the potentially significant consequences this may have for biology-based research and the commercialisation of biology-based products and services.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2025 The Author(s) . Plants, People, Planet published by John Wiley & Sons Ltd on behalf of New Phytologist Foundation.  \n","cbCainv6vsUGSAM1","https://ap.wps.com/l/cbCainv6vsUGSAM1","pdf",1655343,1,14,"English","en",105,"# Summary\n## ABS regulation and AI challenges\n## Explainability and equitable benefit sharing\n## Ownership of input data and traceability through AI models\n# Introduction\n## International ABS frameworks and instruments","[{\"question\":\"为什么人工智能会对生物材料获取与惠益分享（ABS）构成挑战？\",\"answer\":\"ABS法规需要对与生物材料相关的信息与知识进行治理，而AI在训练、测试/验证与应用过程中依赖不同类型数据，使监管复杂化并带来根本性困难。\"},{\"question\":\"文中提出的“可解释性（explainability）”如何帮助ABS？\",\"answer\":\"文章认为在一定程度上将可解释性融入AI算法，有助于更好地满足公平与平等的惠益分享理想。\"},{\"question\":\"ABS实施中涉及哪些关键问题，尤其与数据相关？\",\"answer\":\"文中提出两类核心问题：其一是为训练、测试/验证及使用模型而与数据所有者协商输入数据的权属；其二是通过AI模型对数据本身进行追踪与说明。\"}]","Access and benefit sharing biological materials for machines - Artificial intelligence, machine learning and deep learning | PDF",1785810499,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"access-and-benefit-sharing-biological-materials-for-machines-artificial-intelligence-machine-learning-and-deep-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/access-and-benefit-sharing-biological-materials-for-machines-artificial-intelligence-machine-learning-and-deep-learning/122412/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么人工智能会对生物材料获取与惠益分享（ABS）构成挑战？","Question",{"text":75,"@type":76},"ABS法规需要对与生物材料相关的信息与知识进行治理，而AI在训练、测试/验证与应用过程中依赖不同类型数据，使监管复杂化并带来根本性困难。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"文中提出的“可解释性（explainability）”如何帮助ABS？",{"text":80,"@type":76},"文章认为在一定程度上将可解释性融入AI算法，有助于更好地满足公平与平等的惠益分享理想。",{"name":82,"@type":73,"acceptedAnswer":83},"ABS实施中涉及哪些关键问题，尤其与数据相关？",{"text":84,"@type":76},"文中提出两类核心问题：其一是为训练、测试/验证及使用模型而与数据所有者协商输入数据的权属；其二是通过AI模型对数据本身进行追踪与说明。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]