Welcome to exploring today's AI concerns. In this video, you will learn about some of today's hot topics in AI. First, you will hear about why trustworthy AI is the hot topic in AI. Then, you will hear about how AI is used in facial recognition technologies, in hiring in marketing on social media and in healthcare. People frequently ask me what our current hot topics in AI and I will tell you that whatever I answer today is likely to be different next week or even by tomorrow. The world of AI is extremely dynamic, which is a good thing. It's an emerging technology with an amazing amount of possibilities and the potential to solve so many problems, so much faster than what we thought was before possible. Now, as we've seen in some cases it can have harmful consequences. And so, I would say that the hot topic in AI is how do we do this responsibly? And IBM has come up with five pillars to address this issue, kind of summarizing the idea of responsible AI. That is explain ability, transparency, robustness, privacy and fairness. And we can go into those topics in more depth, but I want to emphasize two things here and one is that this is not a one and done sport. If you're going to use AI, if we're going to put it into use in society, this is not something you just address at the beginning or at the end. This is something you have to address throughout the entire lifecycle of AI. These are questions you have to ask whether you're at the drawing board, whether you're designing the AI, you're training the AI or you have put it into use or you are the end user who's interacting with the AI. And so, those five pillars or things you want to constantly think about throughout the entire lifecycle of AI. And then second and I think even more importantly is this is a team sport, we all need to be aware of both the potential good and the potential harm that comes from AI. And encourage everybody to ask questions. Make room for people to be curious about how AI works and what it's doing. And with that I think we can really use it to address good problems and have some great results and mitigate any of the potential harm. So stay curious. In designing solutions around Artificial Intelligence, call it AI, facial recognition has become a permanent use case. There are really three typical examples of categories of models and algorithms that are being designed. Facial detection that is simply detecting whether it is a human face versus a or a dog or cat. This type of facial recognition happens without uniquely identifying who that face might belong to. In facial authentication, you might use this type of facial recognition to open up your iPhone or your android device. In this case, we provide a one-on-one authentication by comparing the features of a face image. What they previously stored, single up image, meaning that you are really only comparing the images with the distinct image of the owner of the iPhone or android device. Facial matching, in this case, we compare the image with a database of other images or photos. Just as different from the previous in that, the model is trying to determine a facial match of an individual against the database for images below it or photos belonging to other humans. There are many different examples of facial recognition. Many of them you have no doubt experienced in your day to day activity. Some have proven to be helpful while others have shown to be not so helpful and then there are others that have proven to be direct criminal in nature. Where certain demographics of people have been harmed because of the use of these facial recognition systems. We've seen facial recognitions and solutions in AI systems provide significant value in scenarios like navigating through an airport or going through security or security align. Or even using previous previous examples like the one we talked about earlier where facial recognition to unlock your iPhone or possibly to unlock your home or down lock the door in your automobile. These are all helpful uses of facial recognition technologies but there are also some clear examples and use cases that must be off-limits. These might include identifying a person and the crowd without the sole permission of that person or doing mass surveillance on a single or group of people. These types of uses of technology raises important privacy, civil rights, and human rights concerns. When used the wrong way by the wrong people in facial recognition technologies no doubt can be used to suppress, dissent, or infringe upon the rights of minorities or to simply just erase your basic expectations of having privacy. AI is being increasingly introduced into each stage of workforce progression, hiring, onboarding, career progression, including promotions and awards handling, attrition etc. Stock board hiring: Consider an organization that receives thousands of job applications. People applying for all kinds of jobs, front office, back office, seasonal, permanent. Instead of having large teams of people sit and sift through all these applications, AI helps you rank and prioritize applicants against targeted job openings, presenting a list of the top candidates to the hiring managers. AI solutions can process text in resumes and combine that with other structured data to help in decision making. Now, we need to be careful to have guardrails in place. We need to ensure the use of AI in hiring is not biased across sensitive attributes like age, general ethnicity and the like. Even when those attributes are not directly used by the AI but maybe creeping in, coming in from proxy attributes like zip code or type of job previously held. One of the hot topics in AI today is its application and marketing on social media. It has completely transformed how brands interact with their audiences on social media platforms like TikTok, LinkedIn, Twitter, Instagram, Facebook. AI today can create ads for you, it can create social media posts for you. It can help you target those ads appropriately. It can use sentiment analysis to identify new audiences for you. All of this drives incredible results for a marketeer. It improves the effectiveness of the marketing campaigns while dramatically reducing the cost of running those campaigns. Now, the same techniques and capabilities that AI produces for doing marketing on social media platforms also raises some ethical questions. The marketing is successful because of all of the data and social media platforms collect from their users. Ostensibly, this data is collected to deliver more personalized experiences for end users. It's not always explicit what data is being collected and if you are providing your consent for them to use as data. Now, the same techniques that are so effective for marketing campaigns for brands can also be applied for generating misinformation, conspiracy theories. Whether it's political or scientific misinformation and this has horrendous implications for our communities at large. This is why it is absolutely critical that all enterprises adhere to some clear principles around transparency, explain ability, trust, privacy in terms of how they use AI or build AI into their solutions into their platforms. The use of AI is increasing across all healthcare segments, healthcare providers, pairs, life sciences etc. Pair organizations are using AI and machine learning solutions that tap into claims data, often combining it with other data sets like social determinants of health. A top use case is disease prediction for coordinating care. For example, predicting who in the member population is likely to have an adverse condition, maybe like an ER visit in the next three months and then providing the right forms of intervention and prevention. Equitable care becomes very important in this context. We need to make sure the AI is not biased across sensitive attributes like age, gender, ethnicity, etc. Across all of these of course, conversational AI where virtual agents as well as systems that help humans better service the member population. That has become table stakes. Across all of these use cases of AI in health care, we see a few common things. Being able to unlock insights from the rich sets of data the organization owns, improving the member or patient experience, and having guardrails in place to ensure AI is trustworthy. [MUSIC]
