Augmenting Our Thinking. Insights on Ethical AI Collaboration from The Neil Wilkins Podcast

As Artificial Intelligence (AI) continues to evolve, so does the importance of data literacy in understanding and making decisions about the insights generated by AI. With generative AI, the technology can create new, never-before-seen data that can be difficult to interpret without a strong foundation in data literacy. On this episode of the Neil Wilkins Podcast, Kevin Hanegan discusses the importance of data literacy in navigating generative AI, providing examples of how data literacy can be applied to make better decisions in various industries. He discusses the fundamentals of data literacy and examines how data literacy can be applied to help manage the risks associated with AI and ensure ethical decision-making.

Key Insights

Ethical Concerns and Collaboration with AI

• 🌍 The infiltration of AI into various sectors has sparked a big debate about bias, as it raises concerns about how biases may be perpetuated or amplified through AI technologies.
• 🤔 Training bias occurs when we only feed AI data from a certain segmentation of customers, potentially leading to biased outcomes.
• 🤖 AI can be biased and unethical if the training data it was given is not right or missing certain populations.
• 🤔 AI can provide answers that sound realistic and plausible, but it's important to question and verify the data sources to avoid blindly accepting potentially wrong information.
• 🧠 The use of AI in the loop of activity, from setting goals to making decisions, has the potential to be highly beneficial.
• 🔄 The iterative process of humans critiquing AI-generated options and feeding the feedback back into the AI can enhance the decision-making process.
• 🌐 The collaboration between AI and humans is meant to augment each other, not take over, according to the fascinating research studies discussed.
• 🌐 AI can augment our thinking and help us question techniques, providing a framework for leveraging AI in a way that enhances our decision-making process.

Importance of Data Literacy and Education

• 🧠 Educating people about the importance of data is crucial, as it forms the foundation of knowledge and understanding in various fields, regardless of one's academic background.
• 🤔 Data literacy is critical thinking in the 21st century, where we need to analyze information in relation to our objectives and goals to make better decisions.
• 🔄 Wisdom is not a one-time achievement, but a continuous iterative process that requires constant fine-tuning and adaptation to changing trends and data.
• 🛒 More software tools will emerge that aim to automate the entire data cycle, from integration to analytics to storytelling, potentially revolutionizing how we interact with and derive insights from data.
• 🌍 Embracing evidence-based decision-making over hearsay and media biases will lead to better outcomes for individuals and society as a whole.

Summary

• 00:00 Data literacy is crucial in understanding and utilizing data effectively, as it involves not just numbers but also qualitative information, and it requires critical thinking to transform raw data into useful knowledge and wisdom; when analyzing data, it is important to start with specific questions and goals to ensure actionable insights.
• Kevin Hanegan, author of "Turning Data Into Wisdom" and data and AI specialist, discusses the value of data and the challenges people face in understanding and utilizing it effectively.
• Data literacy is the understanding that data is not just numbers and facts, but can also be qualitative information like reviews, and it is important to educate people on this concept.
• Customer feedback is data that can be aggregated into information and then applied to solve a problem, requiring domain understanding.
• Data literacy is the ability to critically think and apply information to make better decisions, transforming raw data into useful knowledge and ultimately wisdom.
• Start with asking questions before looking for answers when analyzing data, as it is more effective to have a clear objective and use the tools to uncover the answers rather than getting lost in irrelevant data.
• To ensure data is actionable, start with specific questions and goals, as adding specificity upfront allows for easier data access and understanding, similar to how clear instructions in an English class lead to better essays, and generic questions to AI yield uncertain results.
• 07:03 Starting with the right question and aligning it with organizational goals is crucial in turning data into wisdom, as it allows for innovation, validation, and avoiding biases and incorrect assumptions.
• A Michelin Chef's restaurant failed because he didn't communicate the goal of speed to his staff, highlighting the importance of starting with the right question.
• You need evidence to support your decisions and strategic plans, especially if you are a decision-maker or budget allocator.
• Starting with a goal and asking relevant questions can lead to innovation and opportunities, but it is important to align the questions with the organizational goal to avoid missing out on potential insights.
• Setting goals is crucial in data analysis as it ensures that important aspects are not missed and allows for the use of data to validate and support those goals.
• The speaker emphasizes the importance of not skipping the critical phase of interpretation in the process of turning data into wisdom, as it is where biases and incorrect assumptions can occur.
• Before defining goals, it is important to conduct a strategic analysis using data and information, which can then be used to shape and critique potential goals for the future.
• 12:38 Continuously analyzing data is crucial for gaining wisdom, improving processes, and making conscious decisions to adapt in a changing world.
• Data analysis is a circular process where wisdom is gained, fine-tuned, and iterated upon, and it is crucial to continuously cycle through this process to stay competitive.
• Capture data and evidence during the conclusion of a project to encourage future iterations and prevent the misconception that finishing a project is enough.
• Data serves as a common bond between previous and future projects, allowing for learning and improvement through questioning both outcomes and processes.
• Analyzing data is not just about the outcome, but also about the process, as a good outcome could be due to chance while a flawed process could lead to an undesirable outcome, and it is important to critically challenge the process and make modifications based on the analysis.
• Understanding how our brains use data to make decisions is crucial, as our brains rely on past experiences and unconscious processing to make quick and efficient decisions.
• In the past, things remained the same and there was no need for upskilling, but now everything has changed and we must make conscious decisions to adapt.
• 20:04 Our past experiences shape our decision-making, but it's important to challenge biases, recognize bias in AI, and provide holistic data to avoid incorrect perceptions and judgments.
• Previous experiences can create biases and stereotypes, but personal experiences can challenge and change those biases.
• Our past experiences shape our decision-making, but it is important to consider different perspectives and not let irrelevant or assumption-based information cloud our judgment.
• Bias is prevalent in our society due to our past experiences, and it is important to recognize and understand our own biases.
• AI's ability to manage bias in data decision-making is hindered by a lack of awareness and understanding among a significant portion of the population.
• AI is good at number crunching and finding clusters of customers, but it can be biased if it is only trained on data from a certain segment of customers.
• Bias in data is a significant concern in AI, as it can lead to incorrect perceptions and judgments, and it is crucial to provide holistic data to avoid such biases.
• 25:24 AI can be biased and unethical if given incorrect or incomplete data, but it can assist humans in decision-making by crunching numbers and presenting options, highlighting the need for critical thinking and creativity to work alongside AI.
• AI can be biased and unethical if the training data it was given is incorrect or missing certain populations, and people's belief that computers don't lie can exacerbate this issue.
• AI can provide wrong answers that sound plausible, so it's important to question and verify its data, but AI will continue to be pervasive despite fears of job loss.
• AI can assist in certain parts of the process from setting goals to making decisions, but there are also parts where it should not be used.
• AI can assist humans in the decision-making process by crunching numbers and presenting options, but humans still play a critical role in critiquing and providing their experience and knowledge before handing it back to AI for the next step.
• Automation will take over tasks that don't require critical thinking or emotions, but humans still need to possess skills like critical thinking, systems thinking, creativity, and curiosity to work alongside AI.
• Partnering AI with human chess players allows them to draw against grand master champions, demonstrating that AI can handle number crunching while humans provide strategy and the human element, making businesses more powerful, although it may be scary for humans who have spent years learning, they still need to be critical thinkers and creative in asking AI for different techniques, saving their brain from tiredness.
• 30:59 Data enables collaboration, informed decision-making, and meaningful conversations, but it should be used as a tool to support decisions rather than blindly relied upon, and challenging our own opinions is important to uncover biases in data analysis.
• The speaker discusses the importance of collaboration and augmentation in research studies, using the example of the McKinsey podcast on AI as a clear and easy-to-understand way of consuming information.
• Data, when used wisely, enables collaboration, informed decision-making, and meaningful conversations, and reframing it as information literacy or critical thinking in the digital world can make it more accessible and inspiring.
• Data should be used as a tool to support our decisions and strategies, rather than blindly relying on it, as confirmation bias can lead to incorrect conclusions.
• Challenge your own opinions and be reflective in order to uncover subtle discrepancies and biases in data analysis.
• 34:29 In the future, businesses will be more data-driven, using streamlined tools and AI to drive innovation, while addressing climate change requires understanding trends and outliers, and schools should prioritize critical thinking and creativity.
• In the future, businesses and startups will be in a very different place, using different tools and approaches due to being more data-driven.
• The future will bring tools that streamline the entire data process, including integration, collection, analytics, storytelling, and automation, with the help of AI, leading to exponential innovation and advancements in fields like healthcare and space exploration.
• Addressing the climate challenge requires humans to understand and believe the data, focusing on trends rather than single data points, while also considering outliers as potential early warning signs of change.
• Schools need to prioritize teaching critical thinking, systems thinking, and creativity, as these skills are essential for navigating the world, even though they are often overlooked in traditional education.
• 38:23 Soft skills are crucial in the age of AI, and embracing evidence-based decision-making helps us collaborate effectively; reframing negative situations and approaching others with understanding leads to better outcomes and wisdom; visit turningdataintowisdom.com for resources on AI, data literacy, and decision-making.
• Soft skills will be more important than ever in the age of AI, and embracing evidence-based decision-making will help us make better choices and collaborate authentically with AI.
• There is a lot of control and opportunity for positive outcomes in turning data into wisdom, but it is important to have the right soft skills to effectively communicate decisions and frame them in a way that resonates with others.
• Reframing negative situations and approaching others with understanding and collaboration can lead to better outcomes and wisdom in various aspects of life.
• Visit turningdataintowisdom.com for articles, white papers, videos, training, tutorials, and books on AI, data literacy, decision-making, and techniques to augment thinking.
• Thank you for your time and for clarifying the importance of taking data seriously and framing it properly in our businesses.