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Tobias Baer explores a new skill that AI is developing and the growing ability of AI to steer humans by evoking strong emotional reactions. 

There is life beyond #ChatGPT – and this post definitely is about something else. Namely, I want to draw your attention to something much bigger that is developing in the world of AI – even bigger than Large Language Models (the technology behind ChatGPT)! Yet it explains why ChatGPT feels differently from, say, a predictive credit score. I call this new skill that AI is developing Emotive AI.

With #EmotiveAI I want to denote the growing ability of Artificial Intelligence to steer humans by provoking strong emotional reactions. Just as the definition of AI itself is constantly evolving – the more sophisticated the most advanced tools are, the more simpler automation tasks are excluded –, also the border between basic predictive models and Emotive AI are fuzzy. This doesn’t matter: also the difference between a “strong personality” and an annoying or charismatic person is fluid, and yet, there are undoubtedly charismatic leaders as well as psychopaths who have an uncanny skill to influence (or manipulate) our behaviours.

Emotive AI combines two important elements: The identification of personality as a set of psychological attributes required to tailor messages (and other actions) to optimize their ability to influence us (in what we think and do) – this is also called affective computing or Emotion AI –, and an iterative set-up where a system learns about us and takes us on an influencing journey over multiple interactions.

When Cambridge Analytica used Facebook likes to classify users by personality and then matched them with ads optimized for different personality types, it created an early demonstration of this first element of Emotive AI. It is also an example of a malicious application of psychology as it resulted in a manipulation of users powerful enough to allegedly influence at least two public votes (one presidential election and one far-reaching plebiscite). A positive and benevolent example of statistical modelling of psychological attributes is credit scoring where psychometric scorecards offer innovative ways to give the still billions of unbanked consumers responsibly access to credit.

These predecessors of Emotive AI are limited to a one-shot decision problem, however – there is a single decision (e.g., which ad to show or whether to approve a loan) that takes the individual’s behavioural patterns (e.g., a propensity to overspend) as a static attribute.

Key points include:

• Debt management
• Counseling
• Building an emotive AI system

Read the full article, Emotive AI, on LinkedIn.