How can data visualization help non-technical stakeholders understand AI decisions?

Data visualization serves as a powerful bridge between technical AI outputs and non-technical stakeholders. By presenting complex AI decisions through intuitive visual representations, such as charts or graphs, it enables stakeholders to comprehend intricate patterns and insights, fostering better decision-making and transparency.

The power of AI is that we can one day ask our data to visualize itself. One of my new favorite programs is Looker Studio from Google. The new GA4 can be less informative out of the box than you would prefer, and the other Universal had too many tabs. Looker Studio is a great example of what the future of data visualization may look like. Right now it's manual to build custom reports, but I can see a future where you ask Bard to build a report by asking "What regions have the highest traffic acquisition from social media? I prefer maps and pie charts. Blue is good, red is bad."

Although AI still looks like a black box, and its inside operations are somehow not clear, there is so much to see and explain to other non-technical peers. Data visualization is an awesome tool to simplify difficult processes and make them more understandable. We can use visualizations to describe models, their mechanisms, and their outputs and insights.

Data visualization plays a crucial role in bridging the gap between AI decision-making and non-technical stakeholders' understanding. By converting complex AI outputs into intuitive, visual formats, it makes the abstract and often intricate patterns of AI algorithms accessible and comprehensible. Graphs, heat maps, and other visual tools can illustrate how an AI system is interpreting data, the relationships between variables, and the rationale behind specific decisions or predictions. This visual approach enables stakeholders to grasp the AI's processes and outcomes more easily, fostering trust and facilitating informed decision-making, without requiring deep technical knowledge of AI workings.

Visualization, and being able to understand the trend within the data, is a key to predict the potential pitfalls of your approach in advance. A key differentiator of a good AI developer and a not so good one is time to working demo, and time to efficient live. The above speed is achieved as a result intuition in selection of approach and data visualization plays a key role in achieving this intuition to allow you to develop efficient solutions.

Data visualization is essential for communicating AI decisions to nontechnical stakeholders. Translating complex algorithms into visual formats like charts, graphs, and intuitive representations enables easy comprehension of critical insights. Serving as a universal language, visualizations facilitate effective communication between technical and nontechnical team members, supporting informed decision making. Visualizations contribute to transparency, building trust in AI system reliability and fairness. Their user-friendly nature aligns insights with stakeholders' domain knowledge, making the information more actionable and encouraging efficient collaboration.

Visualization turns a "lecture" into a "game". It's easier to glance at a visual and leverage pattern recognition opposed to read a mountain of technical insights to arrive at a mental picture/understanding. Data viz is a shortcut to understanding the data.

Data scientists and I believe this applies to tech professionals in general, who often tend to overly concentrate on the technical aspects and the outcomes of our models. Nevertheless, terms like random forests, neural networks, correlations, and cointegrations might not resonate with everyone. Ultimately, for a project to thrive, it is crucial to engage with all stakeholders. Therefore, one must weave a narrative that seamlessly integrates data and key performance indicators (KPIs). Here comes the data viz, you can show pieces of evidence and progress one's protagonist into the final scene (where the KPI goes up), without ever using a technical word, without needing a Bachelor's in math to understand everything.

Data visualization is essential for making AI's complex processes easier to understand. In my work with AI, it's clear that showing how AI gets from point A to point B in a visual format helps everyone understand what's happening under the hood. Simple charts or diagrams can show what data goes into an AI system, what it does with that data, and what kind of decisions it makes as a result. This helps non-technical people see where AI might be getting things right, where it might go wrong, and how certain it is about its decisions. Making AI transparent in this way is key to building trust and making sure AI is used wisely.

Data visualization is important because we want to tell a story about the data. Decision makers and stakeholders who might not have a technical background need to access information in a simple way so that they can be able to make informed decisions.

To use data visualization for AI effectively, it's important to start with a clear purpose and know who the audience is. The right type of visualization can vary greatly depending on the AI system in question. The goal is to make the AI's decisions understandable, showing the journey from data input to output. Good data visualization tells a story with the data, allowing the audience to interact with it and ask questions. It should be laid out logically, emphasize the main points, and provide context to explain AI decisions. It's also valuable to encourage the audience to engage with the visualization, giving them space to provide feedback or raise queries.

To effectively use data visualization in AI, start by pinpointing your audience and purpose. Choose visuals that suitably convey your AI's data and decisions. Create a narrative that illustrates the AI process, encouraging audience interaction and feedback. Structure your visualizations logically to emphasize critical points and guide understanding. Support AI decisions with additional data or evidence, and invite audience engagement for a richer, more collaborative insight into AI operations. This method demystifies AI, enhancing understanding and trust.

The ultimate goal is to create a dialogue - a visualization that not only presents data but also invites questions, interaction, and feedback. It’s like opening a door for the audience to step into the AI’s world and explore it. This not only aids in understanding but also builds trust and transparency around AI decisions. To effectively achieve this, the narrative should be logically sequenced, highlighting key decisions, turning points in data patterns, and the consequential outcomes. The visuals should serve as guideposts, leading the audience through the AI’s decision-making journey. In this era where AI’s impact is profound and far-reaching, it’s crucial that the story told by data visualization is not just understood but also felt.

Microsoft PowerBI Copilot provides the capability to create visualizations from a natural language prompt. You can even create a narrative about a report page- summarizing the report or sections of the page. You can even customize the tone of the narrative.

I can say even Excel could be very helpful, using tools that other people used to use, could make it more easier for them to understand