Stereotypical thinking is one of the major barriers to awareness. Generalisations can help to make sense of the world but they can also seriously mislead us...
A young man who had been badly injured in a car accident has been brought into a hospital's accident and emergency department. The doctor determines that emergency brain surgery is required. Accordingly, the brain surgeon is paged. Upon seeing the patient, the surgeon exclaims, 'My God, I can't operate on that boy! He's my son!'
That is so, but the surgeon is not the boy's father. How can the apparent contradiction be explained?
Answer
The answer, of course, is that the surgeon is the boy’s mother. Although there are many women doctors — and many of them prominent specialists — our cultural stereotyping tells us that doctors are men and nurses are women. The story could have just as easily have been about a nurse attending a patient because of the large number of male nurses.
Variation
In a similar story two Native Americans — a tall one and a much shorter one — arrive at a fort to negotiate with the US Marshals about the land the Native Americans are occupying. They look very much alike and, in fact, they are closely related. The short native is the son of the tall native, but the tall native is not the father of the short one. What is their relationship?
Questions
What would a robot’s answer be to this puzzle?
Does Artificial Intelligence have stereotypical thinking?
Paid subscribers get access my complete collection of over 150 icebreakers and exercises.
Yip - I fell for the first one. Such a relevant lesson here - especially in the times we live in!!
This is a very good question. The Xzistor cognitive architecture gives rise to agents that start off their lives utilizing stereotypical thinking at every opportunity i.e. if it collided with a green door resulting in a painful experience, it will re-evoke those same 'fear' emotions for any new green objects. It will thus treat any green object as a potential source of Deprivation, until it has learnt which specific green item generate other (better) emotions like eating a green apple. The agent will therefore have to learn from experience that its stereotypical view of a green object is too 'biased' and should be updated. As the agent learns it will do these updates but remain in this 100% stereotypical mode when encountering new objects. Interestingly, the tutor can 'teach' the Xzistor agent to refine the manner in which it judges new objects. For instance where the agent is fearful of a Teddy Bear because it was bitten by a dog, the tutor can force the agent to play with the Teddy Bear and update its emotional tagging from negative (Deprivation) to positive (Satiation). The tutor can even later introduce punitive measures to force the agent not engage in stereotypical thinking e.g. where it could harm humans. The above is of course no different from how humans start their lives (and thinking) and slowly learn that stereotypical thinking is not always helpful and can in many cases be harmful.
So, the Xzistor agent is born with zero morals as these will have to be learnt from social interactions.
Other AI systems that do not feel emotions and do not have to learn like humans, e.g. LLM can be pre-programmed using 'rules' to try and filter out behaviors portraying biased behavior e.g. use the word 'chair person' in stead of 'chairman', use the word 'plus size' in stead of 'fat', etc. This could go a far way in reducing stereotypical outputs. The learning material for these LLMs can also be sanitized before the time to avoid exposing the LLM to biased information that could lead to harmful textual constructs. But whereas the Xzistor agents will in time acquire an emotional contextual understanding of the potential harm that can be caused to humans and other AGI, those systems that do not learn through operant conditioning based on an emotional value system e.g. LLMs will only abide by a myriad of restrictive rules and never grow a subjective awareness or emotional conscience that will drive unbiased behaviors outside of simple rules.
Just my take on things! :-)