INBOX INSIGHTS, May 1, 2024: AI Ethics, Model Tuning

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Helpful, Honest, Harmless

Are you using Generative AI?

That sounds like the start of a sales pitch, doesn’t it?

It’s not. I promise.

I genuinely want to know if you’re using Generative AI. Not only do I want to know, but I want to know if you understand how the system you’re using decides what responses to give you.

I’m not going to get into technical details, that’s not why you’re here. There are academic papers and other articles that get into the weeds. For context, at a high level, the guiding principles for how companies, like OpenAI and Anthropic, are training Large Language Models (LLM) are HHH. HHH stands for Helpful, Honest, and Harmless.

Sounds good, right? Who wouldn’t want to use a system that is helpful, honest, and harmless?

Well, let’s not get ahead of ourselves.

There isn’t one singular definition of helpful.

There isn’t one singular definition of honest.

There isn’t one singular definition of harmless.

You see where I’m going with this.

Generative AI is a great tool to integrate into your workflow. There are a lot of reasons why marketers would want to optimize their efficiencies. But this is where I am going to encourage you to read the fine print. The thing that we all say we do, but don’t.

Ok, I’m actually going to ask you to do more than just that. Before you sign up for a generative AI tool to integrate into your workflow, I want you to go through a simple exercise. The goal is to determine what you will and won’t accept from a Large Language Model.

I’d like you to start by outlining your company values. When we think about helpful, honest, and harmless you should be able to tie those into what your company stands for.

As an example, here are the values that we outlined for Trust Insights:

  • We reject deception and secrecy. We are transparent and honest.
  • We reject laziness and stupidity. We are committed and smart.
  • We reject obfuscation and bullshit. We are clear and direct.
  • We reject discrimination and bias. We are fair and just.
  • We reject ego and selfishness. We are humble and generous.
  • We reject pigheadedness and willful ignorance. We are cooperative and aware.
  • We reject gloomy and dramatic. We are cheerful and agreeable.
  • We reject thoughtless acceptance of the status quo. We do better.

When I go through the exercise of selecting a piece of software, like generative AI, I want to have those values front and center. Why? Because the way that the model is trained may not align with your values. For instance, what I think is fair and just, may not resonate with you.

No, this is not normally a step you need to take when assessing software vendors. You want to take this extra step because of how companies are training the models. Unless you’re getting into the code (which they won’t share with you) you don’t know what the companies consider helpful, honest, or harmless. You have to do your due diligence and make those judgments for yourself.

Once you have a shared understanding of your value, go ahead and read the fine print, also know as the terms and conditions. Make sure you know what you’re signing up for and that you’re comfortable with the software. Generative AI is rapidly evolving. So quickly that most of us feel like we can’t keep up, let alone know exactly what it entails.

This is an important time in our industry to be skeptical and questioning. Someone who isn’t you is deciding what is helpful. Someone who isn’t you is deciding what is honest. Someone who isn’t you is deciding what is harmless. You can’t control that. But you can control whether or not to use their software.

Our friends over at the Marketing AI Institute are doing a lot of work trying to understand and educate on this topic. Be sure to follow them to stay up to date as well as following our content.

Are you clear on your values? Reply to this email to tell me or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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Binge Watch and Listen

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the complex and critical topic of AI ethics. You will explore the fundamental principles of ethics and how they apply to the development and use of artificial intelligence. Discover the importance of transparency, accountability, and minimizing harm when working with AI models. Finally, learn how to navigate the challenges of bias and censorship in AI, and how to choose the right tools and frameworks that align with your company’s values and ethical principles.

Watch/listen to this episode of In-Ear Insights here »

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s discuss model tuning. Many AI services from big tech companies like Google’s AI Studio, OpenAI’s Platform, Anthropic’s Console, IBM WatsonX Studio, etc. all offer the ability to create tuned models. But what does this mean, and why would you do it?

Large language models work based on the prompts we give them. In general, the more specific, relevant text we provide in a prompt, the more likely it is we’re going to get a satisfactory output for most common tasks. The key phrase there is common tasks – the major use cases like summarization, extraction, classification, rewriting, question answering, and generation all have thousands or millions of examples around the web that models have trained on.

Sometimes, however, you want a model to perform a very specific task, a very specific way – and because language is naturally ambiguous, language models may not always do things the same way even when instructed to do so, much in the same way a toddler may not do things the same way even with firm instructions.

Generally speaking, you get better performance out of models by providing a few examples. You might have a specific style of summarization, so in your prompt, you’d specify a few examples of the right and wrong way a model should summarize your input text.

But sometimes, you need a model to conform exactly to a format, and even a few examples may not be enough to guarantee that output. That’s when you switch from prompting to model tuning. How it works is relatively straightforward: you provide a LOT of specific examples of the way you want a model to do a task, and then with the help of AI infrastructure (like that provided by the big AI tech companies), you essentially change how the model works by teaching it those examples.

For example, suppose you were building a system to do something like sentiment analysis. If you’ve ever done sentiment analysis with a large language model, you can tell it to provide only a numerical score and most of the time it will – but some of the time it wants to wax rhapsodic about your input text. That’s fine if you’re using a language model in a consumer interface like ChatGPT. That’s not fine if you’ve incorporated the language model into other software, like your CRM.

In that case, you’d want to build at least a thousand examples of exactly how you want the model to respond, in key-value pairs that look like this toy example:

  • Input: Score the sentiment of this text: “I really hate when my food is delivered cold.”
  • Output: -5

You’d have many, many specific examples of this in what’s essentially a spreadsheet, and you’d give that to the training software to tune the model to become really, really good at this task and delivering exactly the output you want.

As you migrate and evolve from end-user, consumer use of generative AI to organizational and enterprise use cases, these predictable, reliable responses become more and more important. When integrated into other software, there’s no opportunity to go back and ask the model to do it again, so tuning the model for a specific use case is essential.

The key takeawy to remember is that tuning language models makes them very good at one specific task. If you have a mission-critical task you need the model to do right all the time, tuning the model is the way to go.

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