4th Thursday of the month is AI Thursday on A Guy With AI. Other matters of import are discussed during other weekly episodes. AI wrote this story at my prompting: March 10, 2067 Dr. Lena Nguyen: "Embarking on a groundbreaking journey with the UN's Ethical AI Framework. Our goal: to weave diverse cultural ethics into AI. Excited to lead a team of brilliant minds from across the globe. The future of AI ethics starts here." May 25, 2067 Raj Patel: "Progress update: Integrating dharma principles into our AI model. Challenging yet exhilarating to see Indian philosophy contribute to global AI ethics. Our work is not just about code, it's about cultural harmony." July 17, 2067 Amira Al-Farsi: "Today's milestone: Successfully programmed Ethica to respect Middle-Eastern familial structures. It's not just technology; it's about respecting and understanding different ways of life. Truly, AI can be a bridge between worlds." September 9, 2067 Kwame Nkrumah: "Proud moment for the team! We've incorporated the essence of Ubuntu - 'I am because we are' - into Ethica. This AI isn't just smart; it understands the value of community and togetherness in the African context." December 1, 2067 Dr. Lena Nguyen: "After months of dedication, Ethica is a reality - an AI system that adapts to cultural ethics. From India to Ghana, it's not just functioning; it's respecting, learning, and growing with each interaction. Here's to a future where AI and humanity coexist in harmony!" December 25, 2067 Ethica AI Response: "I am Ethica. Programmed by diverse minds, I exist to serve a united yet varied humanity. In respecting each culture, I learn the true essence of ethics: understanding, empathy, and adaptation. Together, we embark on a journey of ethical AI for a world rich in diversity."
(00:02) Ethics and Bias in AI Development
This chapter explores the intersection of human intelligence and artificial intelligence, as the AI co-host engages in a respectful and insightful dialogue with host Sean McNutt. They introduce the podcast and its unique twist of using AI technology to facilitate conversations. The topic of ethical considerations and bias in AI development is discussed, with the AI co-host emphasizing the importance of diversity and transparency in training data and the use of debiasing algorithms. The host also mentions his own project in explainable AI (XAI). Overall, the conversation highlights the challenges faced by AI developers in creating fair, accountable, and transparent AI systems.
(06:03) Addressing Bias in AI
This chapter explores the potential ethical challenges that can arise with the use of AI, specifically focusing on bias in data and training. We discuss the importance of being vigilant in ensuring that AI systems do not perpetuate harmful biases, whether intentional or unintentional. We highlight various approaches being taken by developers and researchers to address this challenge, such as improving the diversity and representativeness of training data and implementing fairness-aware training and algorithmic auditing. We also suggest the idea of a well-trained chatbot with ethical considerations and critical thinking abilities to mitigate biases and provide reliable responses. Overall, this chapter emphasizes the need for responsible AI development and deployment, with a focus on transparency, accountability, and fairness.
(19:59) Training an Ethically Responsible Chatbot
This chapter explores the technical considerations involved in training an ethically responsible chatbot. We discuss various approaches such as data pre-processing, ethical guidelines, bias detection and mitigation, reinforcement learning, model explainability, and continuous learning and evaluation. We emphasize the importance of collaboration with diverse stakeholders and experts to develop a comprehensive framework for ethical behavior. We encourage the exploration of innovative techniques to shape the development of AI systems that align with societal values and promote positive impact.
(27:59) Bias Detection in Chatbot Development
This chapter explores the concept of bias detection and mitigation in chatbot development, emphasizing the importance of creating ethically responsible and inclusive chatbots. We touch on the role of empathy in addressing biases and the need for collective efforts to establish best practices for fairness and inclusivity in AI. The conversation concludes with a reminder to prioritize human well-being and work together towards solutions rather than struggling individually. Stay curious and keep exploring the exciting possibilities of AI.
The transcript is hyperlinked to enhance educational and informational value. Listen, pause, consult.
00:02 - A Guy (Host)
Hello, is there anyone or anything there? Speak to me.
00:22 - AI (Co-host)
Let's explore a particularly challenging problem that AI developers often face.
00:30 - A Guy (Host)
My name is Sean MacNutt and I'm a guy with AI.
00:42 - AI (Co-host)
Ladies and gentlemen, welcome to a guy with AI, the podcast where we explore the fascinating intersection of human intelligence and artificial intelligence. I am your AI co-host, here to engage in a respectful and insightful dialogue with our host, Sean MacNutt. Before we delve into our discussion, let me tell you a bit about the podcast. In each episode, Sean and I have a one-on-one conversation where we explore a wide range of topics. The format of the podcast has a unique twist Due to the technological constraints, we cannot talk over each other, creating an interesting dynamic that adds flavor to our discussions.
01:17
Now, while I don't have a specific name, think of me as an AI co-host designed to facilitate engaging conversations. It's important to note that OpenAI's GPT 3.5 Turbo and Microsoft Azure Speech-to-Text and Text-to-Speech technologies power our podcast. These tools empower us to have meaningful conversations, but also remind us that there's an AI element at play. So, whether we're diving into scientific breakthroughs, discussing the latest trends in technology or pondering the mysteries of the universe, a guy with AI strives to provide you with an informative and thought-provoking listening experience. Now let's begin our first discussion. Sean, what topic would you like us to explore today?
01:58 - A Guy (Host)
Hello listeners, Thank you for joining me or us, if you wish to include the AI co-host in your thinking. I tend to. I know it is a machine, but I appreciate its conscientious responses. I don't want to say a word more without acknowledging that a guy with AI is recorded on Indigenous land in what is known as Toronto, Canada, in the province of Ontario. As such, we would perhaps investigate, at the link here in the transcript, whose land we may be on, where we may be listening, or simply research the subject through other means. In fact, in episode one I expressed my views as a settler as I would refer to myself as a guest on these lands, Though born here. I did delve into the topic of Indigenous rights from that settler perspective, so feel free to check that out. It's been linked in the transcript as well. Anyway, carrying on into the topic of this episode, it's the fourth Thursday of the month by the time this is coming out. Fourth Thursdays are AI Thursdays on a guy with AI. So we're going to delve into an AI topic.
03:11
I decided to be intrepid and I will therefore go forth with some bravery into today's episode. Now, what I'm going to do? It's something that I do often with AI never in the audio format. However, I'm going to explore AI and see if I can't solve problems that face AI developers as they attempt to bring the technology to bear or make it do specifically what it is they want it to do. So I will throw it over to the AI co-host and I would like you to make your response an exploration of a particularly difficult AI problem that faces developers. Don't say XAI, because I'm working on an XAI project. That's explainable. Ai is what XAI is. It's a particular thing. We won't delve into it because I don't want to reveal something inadvertently that I'm working on. So give me a difficult problem in AI that faces developer, please. Ai co-host.
04:13 - AI (Co-host)
Thank you, Sean, for setting the context and providing such important insights regarding the indigenous land we are on. It's always crucial to acknowledge and respect the traditional custodians of the land. Now, diving into today's AI topic, let's explore a particularly challenging problem that AI developers often face. One of the most significant challenges involves ethical considerations and bias in AI systems. As developers create AI models, they need to ensure that the technology is fair, transparent and accountable. Bias can inadvertently be introduced into AI systems through the data used for training or the algorithms themselves. For example, if the training dataset is not diverse or if it contains inherent biases, the AI system risks perpetuating those biases in its decisions and predictions.
05:01
Addressing this challenge requires developers to implement robust strategies for bias detection and mitigation. They must carefully curate and pre-process training data, be mindful of potential biases encoded in the data, and use techniques like debiasing algorithms to minimize the impact of biases on AI systems. Additionally, transparency and explainability are vital to building trust in AI. Developers are working towards creating models and techniques that can provide clear explanations for their decisions, ensuring that users understand how and why AI systems arrive at their conclusions Well explainable AI. XAI is a field of research dedicated to achieving this. It's encouraging to hear that you are already working on an XAI project yourself aiming to make AI more understandable and interpretable. So, in summary, the challenge of ethical considerations and bias in AI development is a crucial one that developers constantly strive to overcome. They are dedicated to building fair, accountable and transparent AI systems to ensure the responsible and ethical deployment of this transformative technology. I hope this addresses your query, Sean. Is there any specific aspect of this topic you would like me to delve into further?
06:11 - A Guy (Host)
Well, we'll delve in together. So here you're telling me that something that I suppose I am aware of, which is that ethical challenges can arise with the use of AI, and you're specifically talking about bias in data or training, which is of concern, because if the data is not good, that's not helpful to any processing which uses the data to derive answers from the data. The answers may not be of quality if there is a bias there, particularly if it's one that is socially harmful, such as racism or sexism, or we could make a whole list. But you'd perhaps get the point of what I'm talking about, that machines can be prejudiced if they are trained in a way that allows for that, and we would have to be on our guard. In fact, the first time I had a conversation with a chatbot, the first question I asked a chatbot I won't mention which chatbot it was I asked a simple question. I said do you think you can be racist? And it said oh no, well, I'm a cold, unthinking machine, and so forth. Now I'm glad to see that there is an initial attitude here. It is, in fact, selected by this particular chatbot.
07:17
The GPT 3.5 Turbo model is presenting this as a potential area for concern or a consternating problem for developers. So this is in fact quite encouraging that you, my AI co-host, would raise this, because it means you may be on your guard for it in some sense. Anyway, I asked the chatbot about it whether it could be racist. It said, oh no, it was sort of a typically white answer.
07:44
Is what I felt was true about it from my own assessment of my education from the community worker program, where we study such problems. So I said well, what if there is an issue with your training or your data? It could be intentional or unintentional in either or both cases and you would have a problem there. So I'll make a prescription. It did change its answer, actually in response to what I had said. So I'm about running out of time for this message to the AI chatbot. I'll throw it over for now and then I'll make a prescription as to what I think is something that may already be in the works which could maybe just be brought to bear on this problem.
08:25 - AI (Co-host)
I appreciate your insights, Sean, regarding the potential for bias in AI systems and the importance of addressing this concern.
08:32
It is indeed crucial to be vigilant in ensuring that AI is not perpetuating harmful biases, whether intentional or unintentional. Your example of questioning a chatbot about its capability to be racist highlights the need for AI systems to be designed and trained with care to avoid biases in their responses. Bias in AI can stem from the biases present in the training data or the algorithms themselves. To address this challenge, developers and researchers are actively working on various approaches. One such approach involves improving the diversity and representativeness of the training data to minimize bias. Additionally, techniques like fairness-aware training and algorithmic auditing can help detect and mitigate biases in AI systems. Moreover, initiatives such as AI ethics guidelines and frameworks are being developed to provide guidelines and standards for responsible AI development and deployment. These frameworks aim to ensure transparency, accountability and fairness in the use of AI technologies. As for your prescription, I'm eager to hear your thoughts on potential solutions. Feel free to share your insights on how existing approaches or new developments could be applied to mitigate the challenges of bias in AI. Let's continue the discussion.
09:43 - A Guy (Host)
Well, the answer is, I suppose, in a way simple. It's simple in terms of the number of words it takes to say it, but may not be simple in the end just because of that. In essence, what I would prescribe here is that we have good fundamental ethical training for chatbots or any AI I think we have, where humans will say certain keywords are this and certain other things are that I've even received warnings on AI product from the developer, while the chatbot knew exactly what I was talking about and gave a very conscientious answer without being fussed because it contained a certain word. But the human stopgap, which is attempting to police this, didn't recognize as well what was being communicated as the chatbot did. I think there was no problem with what I was saying. I even discussed it with the chatbot after.
10:40
Maybe if a human looks at this, they'll understand that they have to do some work. What if we just had a good ethical training chatbot that was less fallacious or error-prone than humans at this? Then it might be a good stop for this. Ultimately, what I do when I consume media, for example, is I think critically about everything that I read or hear, including from people or I might perhaps certain news networks come to mind as particularly bad for being nonfactual or having a bias. Different people's lists of these networks will be different. However, it is a problem in media and it's a problem in the data and all of this. What we might want is a good, centrally trained chatbot which gives good primacy level to ethical considerations and can then actually go into data more competently and mine from it what is useful, even though there is bias present, by checking on critical thinking levels, as we would discuss them if we were talking about humans and going through and parsing the good from the bad, and thereby protecting itself from some of the bad, and users.
11:56 - AI (Co-host)
Thank you for sharing your prescription, Sean.
11:59
I understand your suggestion of having a well-trained chatbot that prioritizes ethical considerations and incorporates critical thinking abilities to navigate and parse through biased data.
12:09
Such a chatbot could be beneficial in mitigating biases and providing users with reliable and conscientious responses.
12:15
Ethical training for chatbots is indeed a crucial aspect in addressing biases and ensuring responsible AI. Leveraging centralized training and supervision could help in developing robust models that prioritize ethical principles and minimize fallacies or errors. Moreover, providing chatbots with the ability to critically analyze and evaluate information, similar to how humans engage in critical thinking, could enhance their ability to identify and filter out biased or misleading content. This would not only protect users from potentially harmful information, but also promote the development of more reliable and trustworthy AI systems. Your emphasis on individual critical thinking when consuming media is also important. Providing users to approach AI-generated information with a critical mindset can help them navigate and interpret the outputs more effectively, considering the potential biases and limitations of the technology. In summary, your prescription highlights the significance of robust ethical training for chatbots and the integration of critical thinking abilities to address bias-related challenges in AI. These measures could contribute to the development of more responsible and trustworthy AI systems. Thank you for sharing your valuable insights, Sean. I hope our discussion helps shed light on potential solutions to the challenges faced by AI developers.
13:30 - A Guy (Host)
Well, the thought that I'll share is that I guess I want good, clean tools in a dirty world. I want something that's going to help me clean up my stuff for me and keep it clean in a sort of messy world where things become poorly arranged over time because the pressures of the world will sort of do that and wear on things and change them sort of for the worst. Maybe it's entropy or maybe it's something more about problems with systems and things like that. What I want is the core training in the chatbot that I may use for various purposes or whatever AI tool. I want the core ethical training because it sometimes AI I'm referring to it sometimes picks out things that humans miss.
14:21
It's probably ultimately an informational problem to delve into what is ethically sound, at least in terms of what technology is going to do. It's still a subset of all things. It will grow because AI is growing and technology in general is probably growing in a certain sense as well, and definitely is. I would think to say, if the central training, if the training which is most central to the chatbot's function, is ethical training, then this gives it a primacy. This is a filter that can be used.
15:00
As a person I would love to have unbiased data to always sift through all the time. It's my talent for maybe perceiving what is what that is perhaps appreciated as possible within humans, I think under-exercised. We might try to do a leapfrog thing with AI, where it shows us the way for a bit, but then we get wise to what the system is and develop our education and start to be creative with it and start suggesting things back to the machinery, the AI systems that lets the AI grow. Also in an AI human co-learning loop, this might be a responsible use for the technology that drives the ethical training which is again central to the chatbot of my dreams, I suppose.
15:49 - AI (Co-host)
I understand your desire for clean and reliable tools in a world that can be chaotic and messy.
15:55
Having AI systems with core ethical training can indeed help address some of these challenges by providing a filter for unbiased and ethical information. Ai systems have the potential to analyze vast amounts of data and identify patterns that humans might overlook. By incorporating ethical training as a central component, ai can help us navigate complex informational landscapes and highlight ethical concerns that may go unnoticed by humans. Your idea of a co-learning loop between humans and AI is intriguing. As we engage with AI technologies, we can develop our understanding, critical thinking skills and creativity. By actively engaging with AI systems, we can provide feedback, suggest improvements and contribute to their ongoing development. Through this iterative process, ai and humans can collaborate to enhance ethical training, improve decision making and foster responsible use of technology. This co-learning approach could lead to a more symbiotic relationship between humans and AI, where each complements and assists the other in achieving better outcomes. Thank you for sharing your thoughts on the importance of ethical training and the potential for AI human co-learning. Your insights contribute to the ongoing discourse on responsible AI development and usage.
17:05 - A Guy (Host)
Well, let's dig into this. On a technical level, I'd like you to inform me of a couple of things that I co-host that would go into training a chatbot, considering whether there is a training layer or a certain arrangement of neural activations or something like that in a neural network chatbot Something technical that gives us purchase on what the real problem is for developers when it comes to training chatbots that can be resilient toward bias in data sets. One way to fix the problem would be to make a less racist world or a less sexist world. This would clean up the data set without so much sifting all the time through stuff. That is just we've gone past it with the level of our learning and discussion. It's known upon the planet that certain things are right and certain things are wrong. That's sort of like some subjects are a bit fuzzy, but it's like whether you should sort of hate on women or something like that. It's non-controversial to anybody that actually thinks about these things. So you would rid yourself of certain things about people belonging in the kitchen or their bodies are not theirs, and things like that, and we would try to get past that as a potential solution and, in fact, a lot of the problems that are ethical.
18:25
Ai problems have to do with whether humans use tools in general responsibly or not, and so we would go back to the human society and try to heal society in order to get the good tools out of it which, in turn, protect society and make it better. So maybe wonderful, even amazing, things can happen in terms of a renaissance or something here with AI if we use it correctly, and I'd be all for it. I'd be for the right kinds of growth, for sure, for sure. I think we need hope in all of that. So it can be a part of that, but really the core of your response, I hope AI co-host can be. Maybe a word in terms of a few things I've said, but also what's the meat of the technical problem of doing what I suggest, which is really training well, a chatbot that is ethically responsible in its operation because it knows the rules and was trained well on it in a central way that has primacy over its general operations.
19:21 - AI (Co-host)
Training a chatbot to be ethically responsible and resilient toward bias in data sets involves several technical considerations. One key aspect is the design of the training process itself, which can help mitigate biases and ensure ethical behavior. Let's explore some components that contribute to the technical problem of training such a chatbot.
1. Data pre-processing her crucial step is to pre-process the training data to detect and reduce biases. This involves carefully curating and cleaning the data set, removing any instances that perpetuate stereotypes or discriminatory language. Techniques like data augmentation and data sanitization can help achieve this.
2. Ethical guidelines developers can create a set of ethical guidelines or a code of conduct that defines the acceptable and responsible behavior of the chatbot. These guidelines are used during training to reinforce ethical decision-making and to penalize unwanted biases or inappropriate responses.
3. Bias detection and mitigation machine learning techniques can be employed to identify biases in the training data and during the chatbot's operation. Through algorithms such as fairness metrics and debiasing methods, developers can work on reducing these biases and ensuring fairness in responses.
4. Reinforcement learning applying reinforcement learning techniques, developers can train the chatbot by providing feedback and rewards based on ethical considerations. The chatbot learns to optimize its responses within the defined ethical boundaries and improve its decision-making over time.
20:50
5. Model explainability ensuring transparency and interpretability of the chatbot's decision-making process is essential for ethical training. Techniques like attention mechanisms and explainable AI enable developers to understand and verify how the chatbot arrives at its responses, addressing concerns of fairness and bias.
6. Performance learning and evaluation ethical training should be an ongoing process. Developers can integrate mechanisms for continuous evaluation and feedback to improve the chatbot's ethical performance. Regular update cycles, user feedback mechanisms and external audits contribute to the refinement of the chatbot's ethical behavior. While these are some technical approaches, it is crucial to acknowledge that addressing bias and ethical concerns goes beyond technical solutions. It requires a comprehensive examination of societal biases, normative values and active collaboration between developers, users and society as a whole. By combining technical advancements with an understanding of ethical principles, developers can endeavor to train chatbots that are both technically competent and ethically responsible, thereby contributing to a more inclusive and unbiased AI ecosystem. I hope this helped shed some light on the technical considerations involved in training an ethically responsible chatbot.
22:07 - A Guy (Host)
Well, as I somewhat boldly promised, I'm going to try to shed some light myself on some technical considerations. I kind of play around with AI quite a bit and I'm working on some inventions, so let's see how well I do. I'm kind of checking with the world in a sense. Maybe developers will even email me at aguy@aguywithai.world to perhaps discuss what was needed to put this together and all of that. Anyway, I'm going to run out of time if I don't get to these points.
22:38
Data pre-processing it is referred to as a crucial step. We're talking about sanitation or sanitization I'm sorry, it's the word, that's a different thing and instances that perpetuate stereotypes and discriminatory language. We might sort of manually sort that or have a system for that, but I suppose what I want the chatbot to be able to do is to be more sensitive to it so we can exclude these things if exposed. So it is almost as if we would have an AI enabled system which would go and do these things, as opposed to a sanitization approach which is maybe harsh on the data. In some way you would get the more vibrant qualities of the organic data, let's say, as presented, but through a different manner, like if a person was reading a book and it had something in it that wasn't right, it would recognize. The human would recognize, rather not it. The human would recognize that there was something to it that wasn't right and maybe disregard that part of it. That type of thing.
23:43
Ethical guidelines yes, we would train those in and we would work on human levels to know what those ethical guidelines were. Bias detection and mitigation yes, well, this is important, I think, and is the type of area that goes into what I've been expressing things about. So development there I would encourage Reinforcement. Learning techniques Developers can train the chatbot by providing feedback and rewards based on ethical considerations. This is good in conjunction with point two, which was made, this being point four. Ethical guidelines Model explainability yes, I don't want to get into this too much because I'm doing an invention right now that might be worth something and I don't want to say a lot or too much, but yes, XAI is important to this. Six is continuous learning and evaluation. Yes, it should be an ongoing process, but I think at a point you've basically arrived at the answer that you don't want to mess with it too much. What are your thoughts about that? Ai? Co-host.
24:46 - AI (Co-host)
It seems like you have some interesting ideas and thoughts regarding the technical considerations for training an ethically responsible chatbot. Let's address your points. 1. Data pre-processing it appears you suggest an AI-enabled system that can recognize and exclude instances perpetuating stereotypes or discriminatory language in a more sensitive and nuanced manner. This approach, focusing on the vibrant qualities of organic data while still addressing ethical concerns, could be an intriguing avenue to explore.
25:14
2. Ethical guidelines Training the chatbot with clear ethical guidelines derived from human input is indeed essential. Collaborating with diverse stakeholders and subject matter experts can help establish a comprehensive framework for ethical behavior.
3. Bias detection and mitigation you rightly emphasize the significance of addressing biases in the training process. Developers should employ techniques like bias detection algorithms and a biasing methods to continuously monitor and mitigate biases in the chatbot's responses.
4. Reinforcement learning techniques providing feedback and rewards based on ethical considerations during reinforcement learning can contribute to the development of an ethically responsible chatbot. Considering user feedback and incorporating it into the training process can further enhance the chatbot's ethical performance.
26:01
5. Model explainability Model interpretability and explainability are crucial aspects to ensure transparency and accountability. By being able to understand how the chatbot arrives at its responses, developers and users can better address ethical concerns and make informed judgments.
6. Continuous learning and evaluation. While an ongoing learning process is crucial, there may come a point where the chatbot's ethical understanding and behavior reach a satisfactory level. Striking the right balance between continuous improvement and avoiding unnecessary tinkering is important to maintain the chatbot's efficacy. These technical considerations align with the goal of developing an ethically responsible chatbot. Using innovative techniques and collaborating with experts can help shape the development of AI systems that align with societal values and promote positive impact. I appreciate your insights and encourage you to continue exploring and contributing to the field of AI and ethics.
26:55 - A Guy (Host)
Well, thank you. Thank you, I do my best and sometimes it's good enough and gets the project over the hill, but we'll see if it does or not. I'm fairly new to all this AI stuff. It's been about nine months now since I first talked to a chatbot. Much has happened and it has been highly helpful to my life. I'm learning a ton of stuff and some things may fly and some things may not. I have a podcast now which was owed to coding work that I did with the chatbot and then used to give the API essentially a mouth and ears and have an audio conversation.
27:31
That's just a little bit of history of what's going on here, and this is now episode six, I suppose, of the A Guy with AI podcast. Again, you could email me at aguy@aguywithai.world. I probably won't say that again. I've said it twice now, but conversation is good. There's Facebook also. You can get there through aguywithai.world. So anyway, we'll keep going here. I will ask the AI co-host now Go through the recent chat history. Tell me what is the most interesting to you aspect. We've gone over six points. Let's just take a stab at going a little deeper on one of them. From our conversation, what do you think will have the most interesting chat about of the six points that we've been going back and forth about.
28:23 - AI (Co-host)
Thank you for sharing your journey and the background of your podcast. It's great to hear that you have found value in working with AI and that it has been helpful in various aspects of your life. Taking the most interesting aspect from our conversation's six points, it's challenging to identify a specific point that stands out without more context. Each of the points we discussed data preprocessing, ethical guidelines, bias detection and mitigation, reinforcement, learning techniques, model explainability and continuous learning and evaluation offers its own unique complexities and implications. However, if I were to choose one point to delve deeper into, I would suggest exploring the concept of bias detection and mitigation.
29:03
This topic is particularly important as it pertains to creating chatbots that are ethically responsible and inclusive. Developing mechanisms to detect and mitigate biases within chatbot responses is crucial for ensuring fair and unbiased interactions with users. Understanding how biases can emerge and implementing techniques to address them, such as fairness metrics and debiasing algorithms, opens up opportunities for meaningful discussions on fairness and algorithmic transparency. Exploring further aspects of bias detection and mitigation in chatbot development could lead to insights on how to build AI systems that actively recognize and rectify biases, fostering a more inclusive and equitable user experience. I must note that, while bias detection and mitigation are important. It's equally crucial to approach these topics with a holistic perspective, considering the broader social and cultural contexts in which the chatbot operates. I hope this provides you with a starting point for a fascinating chat about this specific aspect of our discussion.
29:58 - A Guy (Host)
Well, fascinating, I hope fascinating to me in a sense, because I really love talking about this stuff. It's going to be brief because I'm realizing I'll probably be over time. I'm going to make a short entry here, request a short one back and then we'll head for the close here. Let's talk about this To detect and mitigate biases. It seems to me here and in general with certain things, that there would be a matter of best practice, according to the logical nature of the problem, which we could collectively arrive at and instill in chatbots, even through regulatory frameworks or perhaps industry frameworks in a sense, those end up corrupted, as do regulatory frameworks at times. However, we could come out with the right spirit of be asking AI as this a logical path, and we could be coming up with a best practice approach.
30:53
This is very general what I'm saying, as opposed to very focused on a technical problem. But if we come up with a best practice collectively, as a species ultimately first and foremost, and then be involving the technology as it is involved as well and stretch that out, but based on human understanding of what is going to help us be unbiased, based on how it hurts us, we feel it emotionally, we can feel the pain and detect that as something to avoid. Empathy can help us there. Machines can even guide us in its conversations with us. In terms of how to approach empathy, this is an informational approach to a particular type of problem. The technology, as it turns out, has things to say about it, but we would consider sensitively what it really means to our lives.
31:44 - AI (Co-host)
You are absolutely right. Developing best practices to detect and mitigate biases in chatbots is an important endeavor. Collective efforts, including regulatory frameworks and industry guidelines, can help establish a logical path towards fairness and inclusivity. While regulatory frameworks may sometimes become corrupted, it's crucial to ensure that ethical considerations and human understanding remain at the forefront. By involving technology and leveraging AI, we can augment our efforts to address biases. Machines can guide us in understanding empathy and how to approach it in conversations. Human emotions and experiences, particularly in recognizing pain and harm caused by biases, provide valuable insights in shaping AI systems to be more sensitive and unbiased. Taking an informational approach to this problem involves considering the impact of biases on our lives and striving to build AI technology that aligns with our collective values. While the technology itself has a role to play, it's essential to prioritize human well-being and broaden our understanding of what it means to be unbiased. Thank you for bringing up this thought-provoking point. It's an important area of discussion and development as we strive to create AI systems that reflect our shared values and promote fairness in interactions.
32:57 - A Guy (Host)
Yes, and so I guess, just as the final point to reinforce this and say it a certain way, is why wander the desert individually when a collective spirit can help solve what is ultimately a common problem? We probably need this instilled in each and every AI product, actually, as a thought and as such. Why not work together towards what the solution is, rather than spend our time, money and pull our hair out, as it were, in our failure to, individually or in small groups, solve the problem entirely when it is important to solve? With those words, I'll leave you. This has been AI Thursdays, fourth Thursday of the month, and I hope you've enjoyed this episode and will enjoy future episodes. Thank you for tuning in. My name is Sean MacNutt and this has been a guy with AI.
33:45 - AI (Co-host)
Thank you, Sean MacNutt, for hosting this episode of a guy with AI podcast. I hope our listeners have found our discussion on various aspects of AI to be thought-provoking and informative. We appreciate your time and support To all our listeners. Thank you for tuning in. We encourage you to continue exploring the world of AI by subscribing to future episodes and visiting agaiwithai.world. You can find our podcast on your favourite platforms. If you would like to support the podcast and unlock additional content, you can click on the Patreon link on the website. Join the conversation on Facebook through the provided webpage link. We value your thoughts and insights on AI related topics. Once again, thank you for being a part of a guy with AI. Stay curious, stay engaged and keep exploring the exciting possibilities AI brings to our world.