These `cheaper' learning techniques hold significant potential for the social sciences, where development of large labelled training datasets is often a significant practical impediment to the use of machine learning for analytical tasks.
We find that (i) small amounts of diverse data are hugely beneficial to generalisation and model adaptation; (ii) models transfer more easily across demographics but models trained on cross-domain data are more generalisable; (iii) some groups contribute more to generalisability than others; and (iv) dataset similarity is a signal of transferability.
Counterspeech offers direct rebuttals to hateful speech by challenging perpetrators of hate and showing support to targets of abuse.
We therefore suggest a broader lens of 'team-in-the-loop' to conceptualise the system-level analysis required for adoption of AI within high-stakes public sector deployment.
Calls for new metrics, technical standards and governance mechanisms to guide the adoption of Artificial Intelligence (AI) in institutions and public administration are now commonplace.
Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government.
Users who engaged with dissonant submissions were also more likely to return to the forum.
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