Training AI models to deliver better human outcomes

Jurisdiction: Australia

For an outcome create two AI models based on contrasting incentive systems and examine the differing impacts on the defined outcome.

Most uses of AI today are in the pursuit of a goal without clear and consistent incentives or limitations of power. In this challenge, we want to see how different incentive systems lead to better, or worse, human outcomes.

We want to examine clearly distinguishable incentive systems and quantify the effect of these approaches on citizens.

Teams must choose any open dataset and apply their models to an outcome for citizens.

Then define any two different incentive systems such as economic, human or environmental measures. For instance, traditional financial incentives (more is good) vs the NSW Government Human Service Outcomes Framework (https://www.facs.nsw.gov.au/resources/human-services-outcomes-framework) or the New Zealand Living Standards (https://treasury.govt.nz/information-and-services/nz-economy/living-standards).

You can use one of these or make up your own, we just want to see, when set with a common challenge, whether different incentive systems get different results.

> Eligibility Must make use of any open dataset and apply two different incentive systems

Entry: Challenge entry is available to all teams in Australia.

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