3 Ways to Reduce Bias in AI with Better Context Clio

Of all the concerns marketers have when introducing AI into decision-making, there’s one we don’t talk about enough: Are we too quick to assume that AI knows what’s going on in our heads when we build models?

This comes from a growing concern about introducing bias when creating prompts and formatting queries. Bias can come from not providing context and nuance, the knowledge that lives in our heads, which we call upon when making decisions for ourselves but forget to consider when working with AI.

Why is context essential?

I could just assume that you know what context is and why we need to provide it while building our queries. But then you might not understand the reasons why I think it’s so important. My points will not have the same impact and your understanding may be colored or distorted.

The same thing can happen if we trust too much in artificial intelligence’s ability to think.

Context is what we give to our AI model to help it sort, analyze, and report results and insights accurately. It’s like adding conditions when you create an automated email workflow.

This goes beyond the basic questions of which model to use and what to use it for. We must remember that we have an incredibly powerful tool, but it is not infallible. We need to think about how we use it and what information we need to provide to gain accurate and useful insights and analysis.

I understand. We engage AI and assume that it knows everything or that our context doesn’t matter. But this misses my key point. The AI ​​knows a lot, but only you know the context in which you are asking questions.

In short, AI can’t read our minds. Too often we create queries that assume this is the case. This colors the answers that AI gives us.

3 ways to protect yourself from bias when using AI

Here are three practices to follow to get more valuable results from your AI queries.

1. Provide context and nuance

I spoke with executives at one company who were dealing with a situation where a senior executive took over the AI ​​model, improperly uploaded sensitive company operational information in raw form, and asked the model to interpret it.

In addition to failing to ensure that the data would not be shared outside the company, this executive failed in two other key ways:

• By providing only raw data, it gave the AI ​​model no context to consider when analyzing the information and formulating responses.
• He wrote the suggestions to imply that he wanted a negative result or to confirm his prejudice.

Training the AI ​​model made it notice that implicit negativity. Without context, the AI ​​model couldn’t think beyond the negativity embedded in the stimuli.

The resulting recommendations: surprise! – were negative and inaccurate. If the company had made decisions based on distorted results, it would have gone down a disastrously wrong path.

We assume that the machine will pick up on nuances in word choice or vocal tone as a human would. Or we expect him to use reasoning based on previous experiences that are not part of his data memory.

I see marketers making this mistake as they explore using AI in their marketing programs. They treat AI as a tactic rather than part of a strategy.

As with everything in marketing (and in life, if you think about it), strategy must come before tactics. You first develop the strategy (the approach) and then the strategy will guide your tactical decisions. Artificial intelligence is, above all, a tactic: a tool that helps you implement your strategy to achieve your goal.

As part of the development of such a strategy, we now need to define how to avoid biases and how to recognize them in the development and results. We also need to know the context we need to provide to build a reliable model.

This has to be a first. You can’t do it on the fly. Missing this step means that all information you enter will be incomplete and your analysis will be flawed.

2. Provide enough information to help your AI model make the best decisions

How to avoid incorrect results? One way is to do what I did when training one of my AI models on a company. I uploaded about 47 different files, contracts, PowerPoints, articles, and a myriad of other sources of information, which gave the model complete context for the topic I was researching.

Then I did something that AI experts don’t discuss much.

I asked the model, “What do you need to know? What information are you missing?” This helps the model bridge the gap and avoid making decisions without crucial information, such as context.

We hear every day about companies that are replacing employees with artificial intelligence. The latest is Block, the company behind Square, Cash App and Afterpay. CEO Jack Dorsey said the smaller workforce “will move faster with smaller, highly talented teams using AI to automate more work.”

Great. But human employees provide the context that AI models need to deliver better results. An AI model only has the context we give it. We must recognize that biases will harm our companies if we do not take them seriously in this step.

Here’s another example. Doing analytics is an excellent use for AI. It can accelerate insights that you can highlight to examine growth, losses, or opportunities that you might not discover any other way.

If I upload my email sending data and ask my AI model to analyze it and suggest alternative schedules for sending email campaigns, I have to explain that we send emails on Wednesdays and Fridays because that’s when we have updated inventory numbers.

We believe our subscribers open our emails more on Saturday mornings. If you don’t add that context, you’re shortening the analysis.

You need to add this step to your AI analytics strategy. It’s where you say, “Here’s what I know and what fuels my decisions.”

This step is what I call commemoration. You catalog everything you know about how you make decisions in your job, so that when you leave it, the next person who sits in your chair will have a well-rounded base of information.

You may be hesitant to do so because it means giving up your secret sauce: the context and value you bring to your work.

But you have to give it up. Your AI model needs all this information to make a decision in line with what you know.

But that’s not all. You have to constantly look for holes in the interpretation. Don’t gloss over a questionable comment or finding. Don’t assume that your model knows what you know. Don’t assume you can fix the problem later.

There’s a science to this. Our leaders need to make sure we are addressing this problem.

3. Use incremental innovation to uncover biases and add context

Big steps forward capture attention and hamper speeches at corporate conferences, but they rarely lead to sustainable, manageable change.

Artificial intelligence fuels the desire for immediate improvement. AI technology vendors are selling executives the dream of monumental, company-changing advancements. Level C thinks it’s great. Shareholders will love it. The board of directors will be thrilled.

But can the director, senior director, manager, vice president or senior vice president make it work?

Incremental innovation is a more viable alternative. It takes small steps to build something big. You make a change, study the effect, then build on what you learn to make the next one. Each step is a proof point that may reveal a gap or weakness. In AI terms, this means revealing where a biased or non-contextual query could lead you astray.

Yes, it may take longer to achieve total change. Nowadays, we often don’t have the time we need to make conscious and sustainable changes. But it can produce better results in the long run.

You learn all the nuances of the context. You can put two people on the same project, work from the same information base and see if the result is the same.

That doesn’t mean big moves aren’t worth it. But at this stage you need to ask yourself some tough questions:

• Are these changes realistic?
• Have we set up guardrails?
• Have we learned about guardrails?
• How can we make sure we don’t get into trouble?

A marketer told me recently, “When AI starts running ads and emails, some companies are going to make mistakes. They’re going to be very public, and they’re going to be very loud, and they’re going to be very egregious. Because someone somewhere is going to trust the machine to make all the decisions, and that will be the wrong move.

Such decisions will not be well-informed because they lack context and are biased. Because it’s difficult to demonstrate it on a large scale.”

AI outputs are only as good as your inputs

Artificial intelligence is a powerful tool. Technology is moving faster every day, and we can’t slow it down long enough to establish guardrails and rules.

But as responsible marketers, we have to do it. No one wants to be the person who pushes a button and sends out a fundamentally flawed campaign because we haven’t considered bias or context.

That doesn’t mean we should stop using AI (big no). Every marketer should use AI in a way that best suits their agenda. But we must be careful and responsible in how we use and manage our approaches.

Just remember this: AI can’t crawl into your brain and find out how long you’ve been at that company, the conversations you have with colleagues, your preferences and company rules. Take the time to ensure you account for biases and context as you develop your strategy.

Key points

• AI outputs are only as reliable as the context and assumptions built into the prompt.
• Missing context introduces bias by forcing the AI ​​to interpret incomplete or misleading input.
• Marketers need to view AI as a tool within a defined strategy, not as a decision maker.
• Providing detailed input, including business rules and constraints, improves accuracy and relevance.
• Incremental testing helps identify biases early and refine how context is applied over time.