Developing responsible AI practices at the Smithsonian Institution
Applications of artificial intelligence (AI) and machine learning (ML) have become pervasive in our everyday lives. These applications range from the mundane (asking ChatGPT to write a thank you note) to high-end science (predicting future weather patterns in the face of climate change), but, becaus...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Pensoft Publishers
2023-10-01
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| Series: | Research Ideas and Outcomes |
| Subjects: | |
| Online Access: | https://riojournal.com/article/113334/download/pdf/ |
| _version_ | 1827782180292526080 |
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| author | Rebecca Dikow Corey DiPietro Michael Trizna Hanna BredenbeckCorp Madeline Bursell Jenna Ekwealor Richard Hodel Nilda Lopez William Mattingly Jeremy Munro Richard Naples Candace Oubre Drew Robarge Sara Snyder Jennifer Spillane Melinda Jane Tomerlin Luis Villanueva Alexander White |
| author_facet | Rebecca Dikow Corey DiPietro Michael Trizna Hanna BredenbeckCorp Madeline Bursell Jenna Ekwealor Richard Hodel Nilda Lopez William Mattingly Jeremy Munro Richard Naples Candace Oubre Drew Robarge Sara Snyder Jennifer Spillane Melinda Jane Tomerlin Luis Villanueva Alexander White |
| author_sort | Rebecca Dikow |
| collection | DOAJ |
| description | Applications of artificial intelligence (AI) and machine learning (ML) have become pervasive in our everyday lives. These applications range from the mundane (asking ChatGPT to write a thank you note) to high-end science (predicting future weather patterns in the face of climate change), but, because they rely on human-generated or mediated data, they also have the potential to perpetuate systemic oppression and racism. For museums and other cultural heritage institutions, there is great interest in automating the kinds of applications at which AI and ML can excel, for example, tasks in computer vision including image segmentation, object recognition (labelling or identifying objects in an image) and natural language processing (e.g. named-entity recognition, topic modelling, generation of word and sentence embeddings) in order to make digital collections and archives discoverable, searchable and appropriately tagged.A coalition of staff, Fellows and interns working in digital spaces at the Smithsonian Institution, who are either engaged with research using AI or ML tools or working closely with digital data in other ways, came together to discuss the promise and potential perils of applying AI and ML at scale and this work results from those conversations. Here, we present the process that has led to the development of an AI Values Statement and an implementation plan, including the release of datasets with accompanying documentation to enable these data to be used with improved context and reproducibility (dataset cards). We plan to continue releasing dataset cards and for AI and ML applications, model cards, in order to enable informed usage of Smithsonian data and research products. |
| first_indexed | 2024-03-11T15:28:59Z |
| format | Article |
| id | doaj.art-4c58a3fc4f594ec3a9e7409750eaa3fc |
| institution | Directory Open Access Journal |
| issn | 2367-7163 |
| language | English |
| last_indexed | 2024-03-11T15:28:59Z |
| publishDate | 2023-10-01 |
| publisher | Pensoft Publishers |
| record_format | Article |
| series | Research Ideas and Outcomes |
| spelling | doaj.art-4c58a3fc4f594ec3a9e7409750eaa3fc2023-10-27T08:11:47ZengPensoft PublishersResearch Ideas and Outcomes2367-71632023-10-01911710.3897/rio.9.e113334113334Developing responsible AI practices at the Smithsonian InstitutionRebecca Dikow0Corey DiPietro1Michael Trizna2Hanna BredenbeckCorp3Madeline Bursell4Jenna Ekwealor5Richard Hodel6Nilda Lopez7William Mattingly8Jeremy Munro9Richard Naples10Candace Oubre11Drew Robarge12Sara Snyder13Jennifer Spillane14Melinda Jane Tomerlin15Luis Villanueva16Alexander White17Data Science Lab, Office of the Chief Information Officer, Smithsonian InstitutionNational Museum of American History, Smithsonian InstitutionData Science Lab, Office of the Chief Information Officer, Smithsonian InstitutionNational Museum of American History, Smithsonian InstitutionBioinformatics Research Center, North Carolina State UniversityDepartment of Biology, San Francisco State UniversityNational Museum of Natural History, Smithsonian InstitutionSmithsonian Libraries and Archives, Smithsonian InstitutionData Science Lab, Office of the Chief Information Officer, Smithsonian InstitutionNational Air and Space Museum, Smithsonian InstitutionSmithsonian Libraries and Archives, Smithsonian InstitutionNational Museum of African American History and Culture, Smithsonian InstitutionNational Museum of American History, Smithsonian InstitutionOffice of Digital Transformation, Smithsonian InstitutionData Science Lab, Office of the Chief Information Officer, Smithsonian InstitutionNational Museum of Asian Art, Smithsonian InstitutionDigitization Program Office, Office of the Chief Information Officer, Smithsonian InstitutionData Science Lab, Office of the Chief Information Officer, Smithsonian InstitutionApplications of artificial intelligence (AI) and machine learning (ML) have become pervasive in our everyday lives. These applications range from the mundane (asking ChatGPT to write a thank you note) to high-end science (predicting future weather patterns in the face of climate change), but, because they rely on human-generated or mediated data, they also have the potential to perpetuate systemic oppression and racism. For museums and other cultural heritage institutions, there is great interest in automating the kinds of applications at which AI and ML can excel, for example, tasks in computer vision including image segmentation, object recognition (labelling or identifying objects in an image) and natural language processing (e.g. named-entity recognition, topic modelling, generation of word and sentence embeddings) in order to make digital collections and archives discoverable, searchable and appropriately tagged.A coalition of staff, Fellows and interns working in digital spaces at the Smithsonian Institution, who are either engaged with research using AI or ML tools or working closely with digital data in other ways, came together to discuss the promise and potential perils of applying AI and ML at scale and this work results from those conversations. Here, we present the process that has led to the development of an AI Values Statement and an implementation plan, including the release of datasets with accompanying documentation to enable these data to be used with improved context and reproducibility (dataset cards). We plan to continue releasing dataset cards and for AI and ML applications, model cards, in order to enable informed usage of Smithsonian data and research products.https://riojournal.com/article/113334/download/pdf/artificial intelligencemachine learningGLAMg |
| spellingShingle | Rebecca Dikow Corey DiPietro Michael Trizna Hanna BredenbeckCorp Madeline Bursell Jenna Ekwealor Richard Hodel Nilda Lopez William Mattingly Jeremy Munro Richard Naples Candace Oubre Drew Robarge Sara Snyder Jennifer Spillane Melinda Jane Tomerlin Luis Villanueva Alexander White Developing responsible AI practices at the Smithsonian Institution Research Ideas and Outcomes artificial intelligence machine learning GLAM g |
| title | Developing responsible AI practices at the Smithsonian Institution |
| title_full | Developing responsible AI practices at the Smithsonian Institution |
| title_fullStr | Developing responsible AI practices at the Smithsonian Institution |
| title_full_unstemmed | Developing responsible AI practices at the Smithsonian Institution |
| title_short | Developing responsible AI practices at the Smithsonian Institution |
| title_sort | developing responsible ai practices at the smithsonian institution |
| topic | artificial intelligence machine learning GLAM g |
| url | https://riojournal.com/article/113334/download/pdf/ |
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