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Social Inclusion in Curated Contexts: Insights from Museum Practices
Machine Learning · Submitted 10 May 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Drawing on these changes and reflecting back on the AI world, we argue that the museum experience provides useful lessons for building AI with socially inclusive approaches, especially in situations in which both a collection and access to it will need to be curated or filtered, as frequently happens in search engines, recommender systems and digital libraries.
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The abstract, sentence by sentence
Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the design process, which lead to socially exclusive decisions and policies. Faced with similar challenges in dealing with an increasingly diversified audience, the museum sector has seen changes in theory and practice, particularly in the areas of representation and meaning-making. While rarity and grandeur used to be at the centre stage of the early museum practices, folk life and museums' relationships with the diverse communities they serve become a widely integrated part of the contemporary practices. These changes address issues of diversity and accessibility in order to offer more socially inclusive services. Drawing on these changes and reflecting back on the AI world, we argue that the museum experience provides useful lessons for building AI with socially inclusive approaches, especially in situations in which both a collection and access to it will need to be curated or filtered, as frequently happens in search engines, recommender systems and digital libraries. We highlight three principles: (1) Instead of upholding the value of neutrality, practitioners are aware of the influences of their own backgrounds and those of others on their work. By not claiming to be neutral but practising cultural humility, the chances of addressing potential biases can be increased. (2) There should be room for situational interpretation beyond the stages of data collection and machine learning. Before applying models and predictions, the contexts in which relevant parties exist should be taken into account. (3) Community participation serves the needs of communities and has the added benefit of bringing practitioners and communities together.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the design process, which lead to socially exclusive decisions and policies. | background 1.00 | 0.03 | 0.03 | hedged possibility 0.99 |
| s1 | Faced with similar challenges in dealing with an increasingly diversified audience, the museum sector has seen changes in theory and practice, particularly in the areas of representation and meaning-making. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | While rarity and grandeur used to be at the centre stage of the early museum practices, folk life and museums' relationships with the diverse communities they serve become a widely integrated part of the contemporary practices. | background 1.00 | 0.06 | 0.03 | asserted 1.00 |
| s3 | These changes address issues of diversity and accessibility in order to offer more socially inclusive services. | background 0.94 | 0.06 | 0.06 | asserted 1.00 |
| s4 | Drawing on these changes and reflecting back on the AI world, we argue that the museum experience provides useful lessons for building AI with socially inclusive approaches, especially in situations in which both a collection and access to it will need to be curated or filtered, as frequently happens in search engines, recommender systems and digital libraries. | contribution 0.93 | 0.43 | 0.84 | asserted 0.77 |
| s5 | We highlight three principles: (1) Instead of upholding the value of neutrality, practitioners are aware of the influences of their own backgrounds and those of others on their work. | contribution 0.89 | 0.22 | 0.12 | asserted 0.99 |
| s6 | By not claiming to be neutral but practising cultural humility, the chances of addressing potential biases can be increased. | contribution 0.48 | 0.15 | 0.08 | hedged possibility 0.93 |
| s7 | (2) There should be room for situational interpretation beyond the stages of data collection and machine learning. | contribution 0.84 | 0.11 | 0.07 | asserted 0.92 |
| s8 | Before applying models and predictions, the contexts in which relevant parties exist should be taken into account. | contribution 0.61 | 0.07 | 0.05 | asserted 0.98 |
| s9 | (3) Community participation serves the needs of communities and has the added benefit of bringing practitioners and communities together. | contribution 0.79 | 0.13 | 0.12 | asserted 1.00 |
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Original abstract
Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the design process, which lead to socially exclusive decisions and policies. Faced with similar challenges in dealing with an increasingly diversified audience, the museum sector has seen changes in theory and practice, particularly in the areas of representation and meaning-making. While rarity and grandeur used to be at the centre stage of the early museum practices, folk life and museums' relationships with the diverse communities they serve become a widely integrated part of the contemporary practices. These changes address issues of diversity and accessibility in order to offer more socially inclusive services. Drawing on these changes and reflecting back on the AI world, we argue that the museum experience provides useful lessons for building AI with socially inclusive approaches, especially in situations in which both a collection and access to it will need to be curated or filtered, as frequently happens in search engines, recommender systems and digital libraries. We highlight three principles: (1) Instead of upholding the value of neutrality, practitioners are aware of the influences of their own backgrounds and those of others on their work. By not claiming to be neutral but practising cultural humility, the chances of addressing potential biases can be increased. (2) There should be room for situational interpretation beyond the stages of data collection and machine learning. Before applying models and predictions, the contexts in which relevant parties exist should be taken into account. (3) Community participation serves the needs of communities and has the added benefit of bringing practitioners and communities together.
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As e.g. documented in Hsu et al. [ref] , the community citizen science approach can effectively include communities, engaging on concerns that demonstrably are relevant to them.
Empowering Local Communities Using Artificial Intelligence · Community participation · 1.00
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