Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning
Societal Impact Statement Future 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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-09-01
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| Series: | Plants, People, Planet |
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| Online Access: | https://doi.org/10.1002/ppp3.70007 |
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| author | Charles Lawson Elizabeth Englezos Michelle Rourke |
| author_facet | Charles Lawson Elizabeth Englezos Michelle Rourke |
| author_sort | Charles Lawson |
| collection | DOAJ |
| description | Societal Impact Statement Future 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. Summary 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. 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. 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). 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. |
| format | Article |
| id | doaj-art-f3219e6bfd814cd5b15cb9f8b1ad920c |
| institution | Kabale University |
| issn | 2572-2611 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plants, People, Planet |
| spelling | doaj-art-f3219e6bfd814cd5b15cb9f8b1ad920c2025-08-20T04:57:23ZengWileyPlants, People, Planet2572-26112025-09-01751485149710.1002/ppp3.70007Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learningCharles Lawson0Elizabeth Englezos1Michelle Rourke2Griffith Law School Griffith University Southport Queensland AustraliaGriffith Law School Griffith University Southport Queensland AustraliaGriffith Law School Griffith University Nathan Queensland AustraliaSocietal Impact Statement Future 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. Summary 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. 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. 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). 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.https://doi.org/10.1002/ppp3.70007access and benefit sharingartificial intelligenceconvention on biological diversityexplainabilitygenetic resourcesinformation |
| spellingShingle | Charles Lawson Elizabeth Englezos Michelle Rourke Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning Plants, People, Planet access and benefit sharing artificial intelligence convention on biological diversity explainability genetic resources information |
| title | Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning |
| title_full | Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning |
| title_fullStr | Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning |
| title_full_unstemmed | Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning |
| title_short | Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning |
| title_sort | access and benefit sharing biological materials for machines artificial intelligence machine learning and deep learning |
| topic | access and benefit sharing artificial intelligence convention on biological diversity explainability genetic resources information |
| url | https://doi.org/10.1002/ppp3.70007 |
| work_keys_str_mv | AT charleslawson accessandbenefitsharingbiologicalmaterialsformachinesartificialintelligencemachinelearninganddeeplearning AT elizabethenglezos accessandbenefitsharingbiologicalmaterialsformachinesartificialintelligencemachinelearninganddeeplearning AT michellerourke accessandbenefitsharingbiologicalmaterialsformachinesartificialintelligencemachinelearninganddeeplearning |