  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.
