It's important to view yourself as more than a part of a story. In today's episode, we're talking about the self-sabotage that comes with algorithms we identify for ourselves and how we can learn from our past when planning for the future.
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Transcript (Generated by OpenAI Whisper)
How can we observe the same event? Two humans watch the same event, even if it's a small, very simple event, and come away with completely different conclusions. That's what we're talking about in today's episode. My name is Jonathan Cutrell and you're listening to Developer Tea and my goal in the show is to stop Driven Developers Connect to their career purpose and do better work so they can have a positive influence on the people around them. A show is not about making your own career work. Finding your career purpose is not intended to be a selfish endeavor. That's why the whole goal of the show is to improve the lives of the people around you simultaneously as you improve your own life. It's important to understand that as you embark on a journey of understanding yourself, understanding what you want, and throughout your career, building on that purpose, that you can't view yourself solely as the part of your story that matters. I want you to hear me correctly, I'm not saying that you don't matter. This isn't going to be a message of self-deprecation or anything along those lines. You do matter very much so. But sometimes you are wrong. Sometimes you are very wrong and statistically, perhaps most of the time, you're at least a little bit wrong. It's very difficult to be right and it's very difficult to be right completely. And with that in mind, you can quickly recognize that it's somewhat of a self-sabotaging idea to push your own agenda endlessly. It's kind of a backwards reasoning. And in fact, that's exactly what we're talking about in today's episode, backwards reasoning. This is prompted by quite a few stories, not any one specific story, but quite a few stories that have hit media recently that talk about bias in algorithms. The very simple version of this storyline goes like this. We find out that an algorithm starts to, for example, select a particular group of people. Because these algorithms are very important, like selecting a group of people that may be qualified for a particular loan or on the flip side, selecting a group of people that is likely to relapse after being incarcerated for a crime. Other examples include employability or earning potential. And there's a lot of these. And this isn't a new problem. If we look at research previous to artificial intelligence, we can see similar algorithmic decisions or categorizations. Now, that in itself is not necessarily a problem. It's a problem when we understand this categorization improperly. This happens a lot with artificial intelligence more, perhaps, than before because people tend to trust these algorithms more than they would trust a human. And because it's a little bit difficult to explain how those algorithms are created in the first place. And so we end up with these algorithms that, for one reason or another, have some sort of bias. Very simple example might be selecting a particular race or a particular gender or a particular age or a combination of these factors for things like relapsing into crime or the ability to pay back a loan or earning potential. And this is where things get interesting. And perhaps they will hit home for each of us. You see, we can look at these algorithms and come to conclusions about how they became the way that they are. As developers, we might understand that these algorithms are based on perhaps some kind of machine learning, where we take some historical data and plug it in and use various, you know, categorization in order to build a future forecasting model. But when we describe these algorithms and use them, when we use this information in a way that has built-in assumptions, what can be damaging? I'll give an example. Let's say we had an algorithm that determined the earning potential of a given person. And we can see very clearly that the earning potential of a female is lower than the earning potential of a male. Now describing exactly why this algorithm predicts this unfortunate reality, that's where our backwards reasoning can come into play. And where two people can look at the same algorithm, the same output and have totally different opinions on what exactly is happening. For the informed person, you know that this is a long historical issue, that the bias that is built into society hasn't necessarily been fixed, and certainly not long enough to affect the algorithm. And so you'd have to create an algorithm that correctly accounted for those cultural biases. However, many people look at the algorithm or they look at this data set over time, and they ignore the possibility that there was bias that caused the data to be the way that it is. And here's the point. We don't want to stay on algorithms for the sake of the episode because that's really not the only problem that we face when we're dealing with our own opinions. When we believe the stories that we assume, and when we create reasoning, when we're observing something, sometimes that reasoning doesn't have all of the context it needs to be accurate. Perhaps, never, perhaps we never have all of the context that we need to be totally accurate. And so what should we do? How can we observe the world around us, observe what is happening, and form valid useful opinions? Perhaps this is close to impossible, but it's important to recognize as we've said on many episodes before that when you combine multiple people's opinions, you're not necessarily going to eliminate bias, but you may find where those biases exist. You're more likely to find where those biases exist. And remember at the beginning of this episode, we said that this isn't about creating your own story for your own sake. This is about you recognizing that your success is not only just about you, but it can't even happen with just you. You must rely on the people around you because the truth is, these stories that we tell ourselves, we've talked about this before, quite a few times on the show, in fact, we had an episode called Stories We Tell Ourselves, the stories that we tell ourselves and the things that we observe in our perception, these are all very convincing things. It's very easy to believe what we perceive. It's very difficult to question what we perceive and to try to gain insight from the perceptions of others. But this is critical to success, not only as a developer, but really as a human being. And so for you to be successful and to find truly uncover that personal career purpose in your life, you have to start by perhaps humbling yourself, recognizing your propensity for error, and then taking steps to avoid or at least support that reality. Not avoiding the reality itself, but avoiding the pitfalls of that reality, supporting where you will fall short by relying on others in your path, relying on the people that you collaborate with, and reminding yourself always that whatever belief that you do hold it loosely. Remember that you have some belief that you're holding now, that when somebody presents to you some kind of evidence, some kind of information, some reason to change that belief, that if you continue to hold on to it, you'll likely be hurting yourself. You'll be avoiding becoming better, improving. Be willing to change the belief about why, for example, those algorithms, they ended up that way. Be willing to change your belief about the right way to write code, the correct framework to use, the correct approach to use. Be willing to change your beliefs about virtually everything that you encounter in your career. This isn't a plea to tell you to change your beliefs constantly. This isn't me telling you that everything that you believe is wrong, or that you will always be wrong. This is me telling you to band together with others, and to adopt a mindset of growth, adopt a mindset of change. Give yourself the freedom to reject things that you once accepted. Give yourself the opportunity to explore new ways of thinking and acting. I can tell you this open growth-oriented mindset, as perhaps the most important thing that you can adopt. Very likely, if you're listening to the show, you're already open to this idea, but it takes a true mindset shift. It takes being wrong and confronting that wrongness many times before you really understand the value of that humility. So I encourage you to seek situations where you may be wrong. seek situations where there are other people with differing opinions from you. Find places to test those beliefs and to test those methods. And if you feel like you can't test those beliefs and test those methods, that fear is pointing you to something. It may be pointing you to a new job or a new career, more than likely it's pointing you to a conversation. The discussion about why change is so threatening. And perhaps that discussion is one that you just have with yourself. Perhaps you need to investigate why you feel threatened by the prospect of changing your beliefs, changing your methods, changing anything. Thank you so much for listening to today's episode of Developer Tea. I hope that this has been both encouraging and convicting and also inspiring. I really believe that the people who are listening to the show that your mind is open to this kind of thinking. And I encourage you to explore new ways of practicing your craft as a developer, finding the places where your beliefs need to be kind of tested. Thank you so much for listening. If you enjoyed today's episode, I encourage you to subscribe and whatever podcasting app you are using right now. That's the best way to make sure you don't miss out on future episodes just like this one. We are approaching 600 episodes of this show. So of course, we are delivering a lot of content on Developer Tea and it's going to continue. So again, if you don't want to miss out on future episodes, I encourage you to subscribe and whatever podcasting app you're using right now. Thank you so much for listening and until next time, enjoy your tea.