Towards an Objective Test of Machine Sentience.

Abstract:

This paper discusses the notion of sentience in machines, and proposes an approach to analyze it objectively.

It draws insight from studies in Affective Neuroscience which map Functional neuroimaging data on a subject’s brain activity, to their emotional states.
It then outlines a procedure to obtain useful information about possible sentience in a given machine/AI model.

It is hoped that this research inspires more work aimed towards structuring an objective test of sentience in machines.

View Paper on Researchgate

Assessing Model Behaviour on Extreme Counterfactuals.

Abstract:

This paper outlines reported issues that stem from applying statistical models to extreme counterfactuals – data points which are far outside the scope of what the model was trained on.

It then proposes a technique to estimate model behaviour on these out-of-convex-hull datapoints, and rank models based on their performance.

View paper on Researchgate.