NYC Heritage

Data Integrity

Do human-generated training datasets raise ethical and intellectual issues of authenticity, accuracy, authorship?

Data Interoperability

In combining heterogeneous collections, how do we integrate/evolve vocabularies, metadata schemas, ontologies? 

Classification Consequences

Who determines differentiations and sets standards? How might metadata variations or tagging errors be avoided?

GeoSpatial Narrative Gaps

How can location-based data fail to elevate hidden human-scale, micro-layers of cultural significance?

Mapping Traditional Cultures

Can oral-based indigenous knowledge be communicated or presented in text formats or cartesian coordinates?

Inclusion

How might institutional Cultural Heritage involve disconnected immigrant communities and disadvantaged groups?

LOD and Semantic Web

How might inadequacy of references or inaccessibility of raw data impact search criterion and queries?

Intellectual Property Rights

Do works of computational creativity have IP rights? Are these protected or forfeited to the public domain?

Algorithmic Bias

What happens when AI research reveals contradictions to accepted interpretations of value systems?

Thought Provokers

Is the author of the painting the AI-powered algorithm, the team(s) putting together the system,
or the author of the original paintings which were used as a training dataset?

These are just a few sample projects from innovators at the forefront of addressing AI and culture.
Their works fuel the conversation of ethical challenges faced with this emergence,
including discussions about how its rapid evolution can, should and must be handled.

For an overview of ethics guidelines in both cultural and other-industry spheres:
Ethics Guidelines and Partnerships.