Ethics Unveiled: Navigating the Ai Landscape with a Moral Compass

Welcome, ladies and gentlemen, to the AI training podcast. My name is Mark Latimer, founder of Grateful AI. And if you haven't checked it out already, visit openaictraining.com, where you can get free training on AI. We want to just thank you for being here. I love talking about AI and sharing this information with you. So, just so grateful for this opportunity. A brief recap from our last episode, what is AI? If you haven't had a chance already, check it out. Lot of fun talking about what is AI? And in this episode, I couldn't be more excited to talk to you about the ethical considerations in AI. This is a huge topic and this show is going to be full of the goods. So stick around. We've got lots of great information for you. So let's dive right in understanding the ethical considerations of AI. So first and foremost, let's get some definitions clear. Ethics in the context of artificial intelligence. What does that mean? So maybe you went to school, you had a business program, and there was a class on ethics. Ethics basically boils down to what is the right thing to do, what is right and what is wrong. And oftentimes when you train a computer, when you train an algorithm, when you train AI, your own biases about what is acceptable and what is not acceptable can leak into the training. So that if you believe that a certain topic is off limits, or you have a perspective on something, AI might consider your view, right, because that's how you trained the AI. So it means that discrimination can trickle into AI, which is obviously a bad thing. But how do we avoid these things? I want to discuss the importance of these kinds of ethical considerations in AI development and its deployment. I also want to spend some time exploring the potential impact of AI on privacy, security, and human rights. With so many things happening in the world, from wars to still people not having access to food and water in many parts of the world, how can AI help us solve some of these fundamental problems? Let's step back a bit and look at a bit of the historical perspective around AI and how we got here. So AI has been developed for a long time. It's not something that is new, it's new to the public. There is, I believe, a quote from Thomas Friedman the future is here. It's just not widely distributed. More and more we're seeing that AI is becoming prevalent in all areas of life and business. And maybe for you that's not the case yet, which is fine. But as we slowly move forward, we're learning more and more about how history continues to repeat itself. So what lessons can we learn from things that have happened in the past that can be applied to where we're going in the future? I also want to highlight some of the lessons learned from some past ethical challenges. Some of the big companies like facebook are still plagued with content censorship. And how do you monitor and decide which comments are appropriate and which comments are not? They're using algorithms. They're using tools to help. But it's not easy. Everyone has an opinion. You saw that clearly with COVID Whether you're on the left or the right, everyone had a good reason to make themselves believe. To make themselves. It's easy to fall into the belief that you are right about a certain thing, especially when the feedback you're getting from your community supports that. So the ethical considerations with AI are not without their major challenges. And we've got through a lot of things to get here. And this evolution has been a natural progression to trying to scale technology and doing it in a responsible way. So some of the major current ethical issues in AI are, as we've already touched on, bias and fairness. How does bias, our bias or personal opinion about how we see the world inadvertently affect the AI algorithm? Well, I can tell you it's a huge problem. There are major implications for companies that leverage AI. And as it gets rolled out into different communities, I'm reminded of problems facebook has faced with different markets and having information censored and not censored, and creating wars and different internal battles. Scratch that. So the current ethical issues AI is facing three main ones here bias and fairness, privacy and data protection, and the job displacement. And the job displacement and socioeconomic impact. When it comes to the bias and fairness, it's incredible how bias can inadvertently be introduced into an algorithm. There are major implications for decision making that comes from when you think about a company like Tesla that has AI driving cars. If the car is driving and it has a choice to make, whether it decides, let's say it's approaching a crosswalk and there are people walking, if it has to hit something or someone, what does it choose and how does it make that decision? When it comes to privacy and data protection, there are ethical considerations around collecting and using data. You can't just use people's personal data for just about everything, but most companies seem to. Is it right? I want to also discuss the importance of transparency with user content. Some of these large companies have a way to opt out, but it's usually quite hidden and hard to find. Is AI going to make that a little easier? Big business is often in a position of protecting itself, which totally makes sense when it comes to AI. How do we ensure that we as consumers are protected, and as business owners, we're safe as well? And lastly, from a job displacement and socioeconomic economic impact, from a job displacement and socioeconomic impact, there are ethical implications of AI leading to job loss. 30 years ago, 40 years ago, people were concerned that machines are going to take our jobs, and in some cases in factories. There were certainly systems in place, technology that was introduced that changed the way things work. Maybe you were working at McDonald's and a machine was introduced to do your job, but somebody has to fix the machine. Just like today. Somebody needs to operate AI. So as technology changes, new jobs are created. And in my opinion, more jobs will be created that do not exist yet than will be displaced as a whole.  The jobs will change, the way jobs are performed will change, but the net effect will be an increase in employment. When it comes to the potential strategies when it comes to the potential strategies to mitigate these negative socioeconomic impacts, I'm reminded of the way that as society, we're resilient. We're always trying to do the right thing, whether it appears that way or not. I believe humans are good. We're full of love and joy, and there is so much potential to create good if that's what we're looking to do. From an industry perspective, I want touch on a few different industries here healthcare. So when it comes to diagnosing patients and patient care and research, there are a whole bunch of ethical implications on how to do it successfully and how to do it well, especially when it comes down to patient privacy. What is the right amount of data for AI to have access to? How personal should we be getting in finance? AI can help in risk assessment, but who's responsible when it's wrong? Fraud detection is good. You're seeing more and more use of facial recognition you're seeing more and more use of facial recognition in tools as simple as your phone, especially your iPhone. But what happens when facial recognition turns into big brother, monitoring all of your movement? When it comes to practices for fairness and transparency, it's important for us to ask ourselves the tough questions so that we can make better decisions. There are a few points I want touch on. As mentioned, facial recognition. There are concerns around bias, privacy, as well as public responses. The technology companies are going to keep pushing the limits of what is socially acceptable. If you think about the way people are even hired, there's algorithms, especially on LinkedIn, that filter out people using biases, not intentional. Some are, but where is the fairness? Where is the line? And what kind of adjustments to these algorithms should these companies be making? In order to be fairer, there are these ideas of predictive policying or predictive policing, where I remember my minority report, which is a great movie with Tom cruise, where they could anticipate crimes, but the crimes hadn't happened yet. Do you as a listener, do you believe that's where we're going? Do you believe AI is going to predict a crime and you be punished before it actually happens? It's 2023. It feels like the future. We do have flying cars, as I mentioned earlier. It's just not widely distributed. Let's talk a bit about healthcare again. Patient privacy, informed consent. What kind of ethical guidelines should be involved? I believe there is an opportunity for gratitude in design. If we embrace a design philosophy that acknowledges the contributions of diverse perspectives and the collective intelligence will be able to better shape AI systems. There should be a level of transparency and accountability and fostering that transparency in AI development. To express gratitude to users for their trust needs to be more prevalent. Needs to be more prevalent. Establishing accountability mechanisms to honor the responsibility of AI creators and operators needs to be at the forefront of these conversations with equitable access developing AI systems that prioritize these types of benefits and express a genuine gratitude for the potential positive impact on diverse communities. We need to be empowering users through designing interfaces that help people have the knowledge and the control and acknowledge their role as active participants in their respective AI ecosystems. Through continuous learning and improvement, I believe we will have an opportunity to genuinely express gratitude for these learning opportunities and create a better, more ethical AI. The commitment to continuous learning and improvement is something that we need to address and know that, much like ourselves, AI will never be perfect because it's only human to be imperfect. Of course, there is a cultural sensitivity around recognizing and appreciating the diversity of cultures, values and unique perspectives in AI applications. And avoiding these reinforcing stereotypes is hard because it's human to categorize people, places and things. As our minds are categorizing machines. We are trying to organize information and take shortcuts. This is where stereotypes come from, which are the basis of all kinds of humor and jokes. Comedians will often argue, can you make a joke without offending someone? Many would say it's very challenging. When it comes to collaborative governance, how do we cultivate collaboration among industry, academia, policymakers, and the public at large? Acknowledging the collective wisdom necessary for ethical AI stewardship and governance? There are AI systems that really need to incorporate a level of gratitude for the resources they consume and strive for sustainable practices in AI development and deployment. This type of human centered or human centric approach should prioritize the well being and interests of the individuals expressing gratitude for the humanity AI serves. For those of you listening, I want to encourage you to share your thoughts on ethical considerations in AI. I'd love to hear your examples of situations where you believe ethical guidelines should be implemented in AI development. As a call to action, I'd like to invite you to participate on social media. Check us out on Instagram at OpenAI training or visit us on the website openaitraining.com. And let's continue the conversation because this is a conversation that's important to have. I want to thank you for joining me on this episode where we've been exploring the ethical considerations of AI. I'd love for you to if you're feeling called to it. Subscribe leave reviews and share this podcast with friends and colleagues interested in AI. And now it's time to tease out the juice for the next episodes coming down the pipe. And I just love this stuff. I love talking about AI. And in the next episode, we're going to be looking at AI. In popular culture. How it shows up, where it shows up, and what it means for us as people, individual and society. I can't wait to have you there. Can't wait to go into detail and dig deep. I appreciate you so much for being here and I want to thank you for your time. Why be good when you can be grateful? See you in the next episode. I love you. Bye.