We may be departing from our usual bee discussion s here but I trust the thread has some relevance and may veer back on course.  Also, for those who are not knee-deep into AI, this may be of interest because it is not going away and is an interesting tool to research bee questions and dig up references.

Is it fallible?  For sure. I found some Achilles heels with only a cursory probing.  Just the same, it makes fascinating conversation.

I fed the previous post in to the maw of Perplexity just for fun. Here is what came out on the first round:
https://www.perplexity.ai/search/What-would-an-OT7Jm85TSMen0QMFndt01A

Here is the response.

An truly unbiased AI system would be extremely difficult, if not impossible, to achieve in practice. Bias arises from the data used to train AI models as well as the assumptions and perspectives of the humans designing the systems. However, there are strategies that can help minimize bias:

Reducing Bias in AI

Diverse and Representative Training Data
AI models learn patterns from the data they are trained on. Using diverse, representative, and carefully curated training datasets can reduce bias stemming from skewed or narrow data sources.

Bias Testing and Auditing
Implementing processes to systematically test for different types of bias (gender, racial, age etc.) in model outputs. External auditing can also identify biases.

Algorithmic Debiasing Techniques
Using techniques like adversarial debiasing, calibrated equalized odds, and learning fair representations to mitigate bias during the model training process.

Human-in-the-Loop Systems
Having human oversight and the ability to override AI system decisions, especially for high-stakes domains like healthcare and criminal justice.

Multidisciplinary Approach
Involving ethicists, domain experts, and affected communities in AI development to gain diverse perspectives and identify blind spots.

Regulatory Compliance
Adhering to emerging AI regulations and guidelines around trustworthy, fair and non-discriminatory AI systems.
While techniques like these can reduce bias, you make a fair point that truly eliminating all bias is likely an unrealistic goal. The key is being aware that bias exists, taking active steps to minimize it, and avoiding overconfidence that an AI system is completely "unbiased" or "objective."

Related
how can we ensure AI systems are as unbiased as possible
what are the main challenges in reducing bias in AI
are there any successful examples of unbiased AI applications

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