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How can you incorporate feedback from users and stakeholders when working with algorithms?

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As a software engineer, you probably work with algorithms on a regular basis. Algorithms are sets of rules or steps that solve a problem or perform a task. They can range from simple to complex, and from generic to specific. But how do you know if your algorithms are effective, efficient, and user-friendly? How do you incorporate feedback from users and stakeholders when working with algorithms? In this article, we will explore some strategies and best practices to help you answer these questions.

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Ankit Malik

Distinguished AI Scientist | Data Engineer | AI Agents

1 Understand the problem and the context

Before you design or implement any algorithm, you need to understand the problem and the context that you are trying to solve. Who are your users and stakeholders? What are their needs, goals, preferences, and expectations? What are the constraints and requirements of the system, the data, and the environment? How will your algorithm fit into the overall workflow or process? By asking these questions, you can define the scope, purpose, and criteria of your algorithm, and avoid unnecessary or irrelevant features.

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Asad Mujeeb

Business Intelligence | Python | Data Analysis | Machine Learning | Data Analytics Lead @MLSA Multan
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Be transparent about how feedback is being used and keep stakeholders informed about the progress of the project. Open communication builds trust and encourages ongoing engagement

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2 Test and evaluate your algorithm

Once you have a working algorithm, you need to test and evaluate its performance and functionality. You can use various methods and metrics to do this, such as unit testing, debugging, code review, benchmarking, complexity analysis, accuracy, speed, memory usage, scalability, robustness, and usability. You can also compare your algorithm with alternative or existing solutions, and see how it performs under different scenarios or inputs. By testing and evaluating your algorithm, you can identify and fix errors, bugs, inefficiencies, or limitations.

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Asad Mujeeb

Business Intelligence | Python | Data Analysis | Machine Learning | Data Analytics Lead @MLSA Multan
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Pay special attention to any ethical concerns raised by users and stakeholders. Ensure that the algorithm does not inadvertently reinforce bias, discrimination, or other negative impacts.

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3 Collect and analyze feedback

Testing and evaluating your algorithm is not enough. You also need to collect and analyze feedback from your users and stakeholders. Feedback is the information that you receive from others about your algorithm, such as comments, suggestions, ratings, reviews, complaints, or compliments. Feedback can help you understand how your algorithm is perceived, used, and valued by your target audience. You can collect feedback through various channels and methods, such as surveys, interviews, focus groups, observation, user testing, analytics, or social media.

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Ankit Malik

Distinguished AI Scientist | Data Engineer | AI Agents
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The users or business stakeholders are the only entities who know how the application should ideally work. In AI-powered systems, the development team often gets sidetracked because they think a particular metric fits the purpose. Your milestones need to be followed by a feedback call to make sure your creation is critically analyzed.

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4 Prioritize and implement feedback

Feedback is useless if you do not act on it. You need to prioritize and implement the feedback that you receive from your users and stakeholders. Prioritizing feedback means deciding which feedback is more important, relevant, or urgent than others, and which feedback can be ignored, postponed, or rejected. You can use various criteria and tools to prioritize feedback, such as impact, effort, cost, risk, value, urgency, or feedback matrix. Implementing feedback means making changes or improvements to your algorithm based on the feedback that you have prioritized. You can use various techniques and tools to implement feedback, such as prototyping, iteration, version control, or documentation.

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5 Communicate and validate feedback

Feedback is a two-way process. You need to communicate and validate the feedback that you collect and implement from your users and stakeholders. Communicating feedback means informing your users and stakeholders about the feedback that you have received, the feedback that you have prioritized and implemented, and the feedback that you have ignored or rejected. You can use various channels and methods to communicate feedback, such as email, newsletter, blog, report, presentation, or demo. Validating feedback means verifying that the changes or improvements that you have made to your algorithm based on the feedback are effective, efficient, and user-friendly. You can use various methods and metrics to validate feedback, such as testing, evaluation, feedback collection, or feedback analysis.

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6 Learn and improve from feedback

Feedback is a continuous and iterative process. You need to learn and improve from the feedback that you collect, analyze, prioritize, implement, communicate, and validate from your users and stakeholders. Learning from feedback means reflecting on the feedback that you have received and the changes or improvements that you have made to your algorithm, and identifying the lessons, insights, or best practices that you can apply to your future work. Improving from feedback means updating or refining your algorithm based on the learning that you have gained from the feedback, and enhancing its performance, functionality, or usability. By learning and improving from feedback, you can make your algorithm more effective, efficient, and user-friendly.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you incorporate feedback from users and stakeholders when working with algorithms?

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1 Understand the problem and the context

2 Test and evaluate your algorithm

3 Collect and analyze feedback

4 Prioritize and implement feedback

5 Communicate and validate feedback

6 Learn and improve from feedback

7 Here’s what else to consider

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