How can you effectively communicate algorithm changes to non-technical stakeholders?

It is important that you use plain English or the dominant language of your region to communicate algorithm changes. Using Pseudocode and aligning your changes to your organisation key messages, strategic intent, governance goals etc should satisfy stakeholders. But it is important to also justify why the changes were made and to make those changes in collaboration with stakeholders.

To explain algorithm changes to non-technical stakeholders, first simplify the concepts by comparing them to something familiar, like upgrading a recipe for better results. For example, if an algorithm now works faster, say it's like moving from a bike to a car for quicker travel. Show how the change will benefit them, such as speeding up reports or saving money. Use charts to make the improvements clear and relate the changes to their goals, like better customer satisfaction. Keep the conversation open for questions and updates.

What's in it for me? This is the question that you need to be asking on behalf of your audience before giving your message. The exact problems with the algorithm are probably of no interest to your business stake-holders. It's the outcomes, specifically, the business outcomes which are of interest to your stakeholders. Also, these people are going to want to know how you intend to fix it, how long it will take, how much money and so forth. Try to speak the language of your audience to get better traction. If you are unsure, then do some investigation beforehand, and try to talk to people who know your stakeholder. Also, explain the issue to an LLM and ask it to translate into lay terms, and tell it the audience as well.

Its important to share the usage of algorithms in business terms. The biggest challenge people face is the ability to speak according to what the other want to hear. Non technical persons would understand non technical details only.

Using analogy similar to or related to things target audience/person is already aware of and examples is the best way to explain algorithmic changes to non technical people. On top of it some algorithmic changes can be directly mapped to real life scenarios, which can be used to effectively communicate the changes.

Mostly analogies used to explain algorithms are vague but I believe that any form of analogy used must be something the stakeholders can relate to. Always think of your stakeholders as kids and explain things to them in a way that a kid can understand.

Using analogies has been one of the most effective tool in my experience. For instance if you are trying to explain the difference between IP (internet protocol) and ICN (Information Centric Networking), two different networking technologies, you could just say - In an office if you are trying to find a stapler, in the IP world, you look up the directory to find out where the supply cabinet is and you go there. In ICN, you ask the person next to you if they have a stapler, and they ask the person next to them, and so on until the stapler is found and makes its way back to you. The analogy only works at the high level. However, once the audience has wrapped their head around the high level concept, they are less intimidated by the details.

From my experience, combining analogies with visual aids and simple language creates the most effective communication. When using analogies, I ensure they are simple and relatable to everyone, avoiding those that require extensive explanation, as this can defeat the purpose. Understanding the background of the target audience beforehand is highly beneficial, as it allows us to tailor examples accordingly, making it easier to capture their attention and reduce anxiety or boredom. Additionally, using analogies not only to explain the algorithmic changes but also to illustrate the impact of these changes is equally important, as this is often what stakeholders are most interested in.

Visual analogies work great. I still remember seeing a photo of "do not back up" tire spikes used as an analogy for an operation that is easy to perform, but very hard to reverse/invert (e.g. multiplication vs factoring).

Some questions to help you better define your communication: What is the main objective of the algorithm change? Who is the target audience for your communication (management, technical team, customers)? What is the technical knowledge level of your audience? What are the key concerns the audience might have?

Stakeholders are mostly concerned about the impacts that the algorithm will bring in. To start with, I recommend going with the benefits first, including the complexity and risks if any involved. With this we bring the stakeholders on their toe and they do concentrate more as the change is bringing in more value and benefit to them. Explain about the user experience, performance increase and other aspect that is of interest of the stakeholders. Later on go with explaining the algorithm and it's boundaries.

The impact on changes of an algorithm on business results, articulating different strategy outcomes against competitors, changes with respect to competitors, their strategies, economic conditions and other factors… For example Michael Jordan scored more than 40 points, the Bulls win the game with 0.95 percent probability. As a business if you have the price high, medium or low what happens to the market share of our company. It might depend whether you are the leader or follower. In these cases you might use different theories, changing the algorithm, hence changing the impact and the interpretation of the result of the algorithm. With more data, the impact on the changes of the algorithm is minimized.

Explique o benefício ou resultado que a mudan?a do algoritmo vai causar, em vez de explicar como você vai chegar nesse resultado ou benefício. A pessoa n?o quer saber o como, ela quer saber o que vai mudar para ela.

Exactly this is very critical, by explaining what prior and after changes that impact customer is very critical by visual representation, cost, additional efforts...such detailing

To communicate algorithm changes to non-technical people, visual aids are incredibly useful. Flowcharts can show the algorithm's steps without diving into code, while infographics combine images and text to simplify explanations. Comparison tables and bar or line graphs are also effective for highlighting performance improvements. If the change impacts the interface, mockups can visually demonstrate the new look. For a more dynamic approach, consider an explanatory video or an interactive dashboard that allows real-time exploration of changes. The goal is to clearly and simply convey how these changes benefit both the business and its users.

Data visualization is a powerful tool not only for presenting final results but also for supporting the data science team throughout all stages, from exploration to result evaluation. Depending on the objective and the available data, it’s essential to choose the most effective visualization. One type of chart I particularly like to use when applicable is the violin plot, as it allows for visualizing the distribution of previous performance on one side and the improvement on the other.

Visual aids play a crucial role in bridging the gap between complex algorithmic concepts and non-technical stakeholders. By utilizing flowcharts and diagrams, we can demystify the intricacies of algorithms, making their functionalities and impacts more relatable. This approach not only enhances comprehension but also fosters informed decision-making, as stakeholders can visualize the implications of changes in real-time. In an era where emerging technologies like artificial intelligence are rapidly evolving, clear visual communication becomes essential for effective leadership and strategic alignment across diverse teams.

Use clear and concise language: Short sentences: Avoid long and complex phrases. Active verbs: Use active verbs to make the message more dynamic (e.g., "The algorithm will identify" instead of "It is identified by the algorithm"). Avoid slang: Maintain a professional and formal tone.

A before-and-after graph, for example, can clearly show improvements, helping bridge the gap between technical details and business outcomes. Keep it clear and impactful.

Summarize Key Points: Provide a concise summary of the most important aspects of the change. Avoid technical jargon and focus on the high-level implications and benefits. Provide Real-World Examples: Use case studies or examples to show how similar changes have had positive impacts in other situations. This can help stakeholders relate to the benefits in a practical context.

Always connect algorithm changes to what matters most to your stakeholders. Identify their key drivers—be it cost savings, user engagement, or gaining a competitive edge—and explain how the algorithm supports these objectives. For instance, if an algorithm enhances efficiency, highlight how it leads to cost reductions. By aligning technical adjustments with business goals, you create a strong, persuasive case for the importance of these changes.

Non-technical stakeholders will be more invested in the changes if they understand how it helps the company’s objectives. Always relate the algorithm change to something they care about. For example, say, “This change will help us reach our goal of improving customer satisfaction by giving more personalized service,” or “It aligns with our goal to reduce operational costs by 5%.” Tying the explanation to business goals makes the change seem less abstract and more valuable.

Tie the algorithm changes back to the stakeholders' business goals, such as cost savings or user engagement. Find the right metric to compare it with.

Narrative Approach: Frame the changes within a story. For example, “Imagine you’re shopping online, and the website now recommends products you actually want. That’s what this algorithm change will do.” Case Studies: Share examples or case studies where similar changes have led to positive outcomes.

One thing I maintain is continual communication with stakeholders. An algorithm change would impact performance, and from a totally data-oriented standpoint, it is understandable that stakeholders would have ambiguity in this regard. In this situation, keeping a clear communication is key. Be straight what are the root-causes, and rather importantly, your initiative towards finding a fix in this regard would reassure the stakeholders with your proactive approach to shorcomings.

Establish an ongoing conversation with stakeholders. Encourage questions and provide regular updates throughout the implementation process.

Maintain an ongoing conversation rather than a one-time presentation. Invite questions, address concerns, and provide regular updates as the algorithm changes are implemented. This two-way communication builds trust and ensures stakeholders remain informed and engaged throughout the process. By fostering an open dialogue, you create a collaborative environment where non-technical stakeholders feel valued, heard, and aligned with the technical team, leading to smoother transitions and better outcomes.

Keep the associated stakeholders informed with regular updates and encourage questions to maintain a collaborative understanding.

Interactive Sessions: Hold Q&A sessions or workshops where stakeholders can ask questions and provide feedback. Regular Updates: Keep stakeholders informed with regular updates on the progress and impact of the changes.