I can intuit that you hated me the moment you saw me at the interview. Because I've observed how hatred works, and I have a decent Theory of Mind model of the human condition.
I can't tell if you hate me because I'm Arab, if it's because I'm male, if it's because I cut you off in traffic yesterday, if it's because my mustache reminds you of a sexual assault you suffered last May, if it's because my breath stinks of garlic today, if it's because I'm wearing Crocs, if it's because you didn't like my greeting, if it's because you already decided to hire your friend's nephew and despise the waste of time you have to spend on the interview process, if it's because you had an employee five years ago with my last name and you had a bad experience with them, if it's because I do most of my work in a programming language that you have dogmatic disagreements with, if it's because I got started in a coding bootcamp and you consider those inferior, if one of my references decided to talk shit about me, or if I'm just grossly underqualified based on my resume and you can't believe I had the balls to apply.
Some of those rationales have Strong Legal Implications.
When asked to explain rationales, these LLMs are observed to lie frequently.
The default for machine intelligence is to incorporate all information available and search for correlations that raise the performance against a goal metric, including information that humans are legally forbidden to consider like protected class status. LLM agent models have also been observed to seek out this additional information, use it, and then lie about it (see: EXIF tags).
Another problem is that machine intelligence works best when provided with trillions of similar training inputs with non-noisy goal metrics. Hiring is a very poorly generalizable problem, and the struggles of hiring a shift manager at Taco Bell are just Different from the struggles of hiring a plumber to build an irrigation trunkline or the struggles of hiring a personal assistant to follow you around or the struggles of hiring the VP reporting to the CTO. Before LLMs they were so different as to be laughable; After LLMs they are still different, but the LLM can convincingly lie to you that it has expertise in each one.
A really good paper I read last year from 1996 helped me grasp some of what is going only: Brave.Net.World [1]. In short, when the Internet first started to grow, the information that was presented on it was controlled by an elitist group with either the financial support or genuine interest in hosting the material. As the Internet became more widespread that information became "democratized", or more differing opinions were able to get supported with the Internet.
As we move on to LLMs becoming the primary source of information, we're currently experiencing a similar behavior. People are critical about what kind of information is getting supported, but only those with the money or knowledge of methods (coders building more tech-oriented agents) are supporting LLM growth. It won't become democratized until someone produces a consumer-grade model that fits our own world views.
And that last part is giving a lot of people a significant number of headaches, but its the truth. LLMs' conversational method is what I prefer to the ad-driven / recommendation engine hellscape of modern Internet. But the counterpoint to that is people won't use LLMs if they can't use it how they want (similar to Right to Repair pushes).
Will the LLM lie to you? Sure, but Pepsi commercials promise a happy, peaceful life. Doesn't that make an advertisement a lie too? If you mean lie on a grander world view scale, I get the concerns but remember my initial claim - "people won't use LLMs if the can't use it how they want". Those are prebaked opinions they already have about the world and the majority of LLM use cases aren't meant to challenge them but support them.
> When asked to explain rationales, these LLMs are observed to lie frequently.
It's not that they "lie" they can't know. LLM lives in the movie Dark City, some frozen mind formed from other peoples (written) memories. :P The LLM doesn't know itself, it's never even seen itself.
At best it can do is cook up retroactive justifications like you might cook up for the actions of a third party. It can be fun to demonstrate, edit the LLMs own chat output to make it say something dumb and ask why it did and watch it gaslight you. My favorite is when it says it was making a joke to tell if I was paying attention. It certainly won't say "because you edited my output".
Because of the internal complexity, I can't say that what an LLM does and its justifications are entirely uncorrelated. But they're not far from uncorrelated.
The cool thing you can do with an LLM is probe them with counterfactuals. You can't rerun the exact same interview without the garlic breath. That's kind cool, also probably a huge liability since it may well be for any close comparison there is a series of innocuous changes that flip it, even ones suggesting exclusion over protected reasons.
Seems like litigation bait to me, even if we assume the LLM worked extremely fairly and accurately.
I can't tell if you hate me because I'm Arab, if it's because I'm male, if it's because I cut you off in traffic yesterday, if it's because my mustache reminds you of a sexual assault you suffered last May, if it's because my breath stinks of garlic today, if it's because I'm wearing Crocs, if it's because you didn't like my greeting, if it's because you already decided to hire your friend's nephew and despise the waste of time you have to spend on the interview process, if it's because you had an employee five years ago with my last name and you had a bad experience with them, if it's because I do most of my work in a programming language that you have dogmatic disagreements with, if it's because I got started in a coding bootcamp and you consider those inferior, if one of my references decided to talk shit about me, or if I'm just grossly underqualified based on my resume and you can't believe I had the balls to apply.
Some of those rationales have Strong Legal Implications.
When asked to explain rationales, these LLMs are observed to lie frequently.
The default for machine intelligence is to incorporate all information available and search for correlations that raise the performance against a goal metric, including information that humans are legally forbidden to consider like protected class status. LLM agent models have also been observed to seek out this additional information, use it, and then lie about it (see: EXIF tags).
Another problem is that machine intelligence works best when provided with trillions of similar training inputs with non-noisy goal metrics. Hiring is a very poorly generalizable problem, and the struggles of hiring a shift manager at Taco Bell are just Different from the struggles of hiring a plumber to build an irrigation trunkline or the struggles of hiring a personal assistant to follow you around or the struggles of hiring the VP reporting to the CTO. Before LLMs they were so different as to be laughable; After LLMs they are still different, but the LLM can convincingly lie to you that it has expertise in each one.