Ethics and Bias in AI starts with you

Ethics and Bias in Artificial Intelligence starts with you

Ethics and bias in data collection, marketing, and general business are hot topics right now. Why? Artificial Intelligence is changing the way we make decisions and doing the work for us.
You can catch the discussions here: 
What does ethics mean to me? Ethics are the moral principles that govern a person’s behavior or the conducting of an activity. I live by a simple moral and ethical code: be kind to others, be honest, help where you can, collect as much information as possible, and make decisions that don’t keep you up at night
Rather than rehash the same conversation about the challenges and issues in the business space, I want to take a step back and focus on some potential solutions. Those solutions start with you. 

Yes, you. 

You might be thinking, “I don’t even collect data” or “I don’t use artificial intelligence so what does this have to do with me?” The short answer is everything. 
Artificial Intelligence is a man-made technology. This means that the people who are creating the technology are introducing their own biases, ethics, and beliefs. For some tech that’s a non-issue, but by nature, Artificial Intelligence takes over the decision making for humans and makes our lives easier.  
Ethics and bias are large topics unto themselves. People spend their whole lives studying what this looks like around the world, within different cultures and within different segments of the population. There is no one way to approach how people decide what is ethical for them and what decisions they can live with. Humans are complex and the way that decisions come about is equally, if not more, complex. 
When putting together Artificial Intelligence for your organization, the goal is generally time savings and decision making. Even if you’re purchasing something “off the shelf” there is still some set up that has to happen for your specific use case
Who is deciding what these decisions and outcomes should be? Not the machines, not to start. We are – and if you’ve purchased software, the people who made that software. So how do we lead this initiative with our best foot forward? Here are some places to start if you want to check in with yourself before venturing down the road of Artificial Intelligence

Start with you. 

How comfortable are you with being honest, even if it’s difficult? What happens to us, as humans, is that we bend the truth to make other people feel ok about the information delivered. When we do that, we are not always 100% honest about the information. When we introduce that to a machine, the machine cannot decipher an action taken to spare someone’s feelings versus a direct statement
Do you feel like you jump to conclusions before getting all of the information? Listen, we all do it. We make assumptions, gather more data, and then backtrack to change what we originally thought. Machines don’t have that capability (yet). Decisions are black and white, there is no grey area to ponder instincts or body language. Decisions change as more data is available, but that is dependent on a person adding that new information into the algorithm. A machine can only make a decision on the data that it has. If you are introducing your conscious or unconscious bias to the technology, the machine will follow suit
The gist is that Amazon introduced a hiring algorithm using its own historical hiring data. The issue that presented is that the data set was biased toward hiring mostly men, so the algorithm followed suit. There was a lack of governance around the training data set to understand if it was biased or not. Amazon made quick work to correct the issue, working toward upholding their ethics and values. You cannot prevent mistakes from happening. How you react is oftentimes more important. 

Look around you. 

Who do you surround yourself with? Are they people that you trust? Do you question their decisions? Do you feel like you can be honest with them? 
Are you surround by people who think exactly like you, or are you (respectfully) challenged to think differently? One of the issues we’ve observed is when a team is too homogenous and they set out to build new tech. Having shared values and beliefs is fine, but, the blind spot is a lack of outside perspective and opinions. This is where we introduce bias into the systems.
If you’re starting to introduce Artificial Intelligence into your organization, who is in charge? Is it one singular person, or a team of people who come from different walks of life to bring different perspectives? Something to consider before introducing AI into your team is putting together a committee. Your committee should include different role levels, different departments, and different backgrounds. This will help keep everyone accountable for the decisions going in and balance out the use cases for the tech
Does your company have a code of ethics and values – and do they adhere to it? An easy place to start is with what your company believes in. A lot of companies say one thing and do another. If the shared values are not public, that’s the first question to ask. The second question is, do you agree with those values? 
For example, here are the values for Trust Insights:
We make every effort to uphold these values with every decision that we make. We challenge each other to make sure that we make each decision with care and thoughtfulness. 

Not sure where to start? Ask.

Slack and social media groups are great places to start if you have questions about ethics and AI. Sure – you can do solo research but if you want other opinions you need to ask around. If you are in marketing and are curious about the challenges that others have run up against when introducing AI it is as simple as posting a question. You’ll get a variety of responses from people at all different experience levels and backgrounds to help shape the conversation in a balanced way
Want to join our community?
Ethics and bias in Artificial Intelligence is an initiative that is in its infancy. There is a lot of room to grow, and a lot of room for learning. What are your thoughts – let me know in the comments.

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