How can you document and communicate algorithmic limitations effectively?

The most effective way to communicate algorithm limitation is through requirements. This means clearly identifying the inputs (e.g., CT scans resolution, patient type etc) and success criteria (identify tumor 95% correctly and center-of-mass and volume with 20% variance compared to expert). Also specify positive & negative failures (false positives OK, false negatives not OK) Good requirements force all stakeholders to agree on limitations. In the example above for example miss by a mile is the same as miss by an inch, so a user might see something really off and think the system is broken. Consider adding a requirement that limits the acceptable characteristics of the false positive/negative results.

The best documentation for the code are tests. So, limitations and constraints of the algorithm should be ideally defined in the code itself as unit and other tests.

La premiere chose est de definir le domaine de vol de l'algorithme , vis a vis des uses cases les plus critiques validés par le client. Ensuite il faut rapidement avoir une idee de la complexité independament de toute technologie (decidabiité de l'algo par exemple) puis regarder la mise en oeuvre vis a vis de la perspective IT (langage , environnement etc..) C'est l'ensemble qui doit etre pris en compte

In my opinion, we should document the limitations from the end user's perspective on what they can and cannot do because of these limitations. Later, we should have a section where we can explain the technical details of the algorithm, detailing how it works and how various factors contribute to the limitations.

Effectively communicating algorithmic limitations is crucial for transparency. Transparency fosters understanding and informs decision-making. I am mentioning some plans to achieve this, include clear descriptions, enumerating specific limitations, use decision trees, make comparisons, and providing some real-world examples to corelate. Thanks

Após a identificação, categorize as limitações com base em sua natureza (funcional, não funcional, ética, legal), impacto no projeto, e gravidade. Essa categorização ajuda a priorizar quais limitações devem ser abordadas primeiro e de que maneira.

It's important to use a load testing approach for each layer of your application testing: individual service testing, e2e load testing for the whole application, disaster recovery testing, and other useful techniques that can help you understand the limitations of each component and the whole system. The stats that you will collect from this process might be a good starting point for categorizing the limitations of your system.

Como profesional en el campo, considero que esta categorización puede facilitar la identificación y comunicación de las limitaciones, así como proporcionar un marco para abordarlas de manera estructurada.

One thing I've found helpful is a good set of data and corresponding output. The more extensive and more comprehensive the data set, the better. It allows for a faster feedback loop and early detection of edge cases.

The business function and architectural position are not really software developer conversations, although seniors must be aware of these spheres. What happens if the solution no longer works? How much to replace it? Where does it sit on (eg.) a Wardley Diagram? Is the product open to competitive pressure from the risk of using it?

Comment the complexity on top and put timeouts inside the function or warning logs, clearly explain that this function will block the whole process or eat up too much resources, if possible put it under special roles like "admin" can only call it in special circumstances. And run that function in a separate machine to protect production availability.

The best documentation for the limitations is through well defined unit and integration tests. When done correctly, tests are living documentation. Any documents external to the codebase are bound to be out of date and age poorly.

A documentação deve ser clara, concisa e acessível. Utilize diferentes formatos conforme o público-alvo: Para Desenvolvedores: Comentários no código, anotações, e documentação técnica detalhada. Para Stakeholders: Resumos executivos, relatórios de impacto, e apresentações que destacam as implicações das limitações nos objetivos do projeto.

A good and full load testing solution can provide you with great documentation about what are the limitations of your services, and what services are vital for your application during a potential disaster Using pure statistics collected from it you can provide detailed reports to the business side of your project. Team members who are responsible for business analytics and data analytics can provide their vision about what are potentially dangerous points in the system and share their concerns about which part of the application should be scaled more as a response to business needs and together with the engineering team can start working on Disaster Recovery Planning for vital for the business components.

En mi opinion documentar las limitaciones de manera clara, concisa y coherente es esencial para garantizar una comunicación efectiva y una comprensión precisa en cualquier proyecto de desarrollo de software o algoritmos. Este enfoque promueve la transparencia, la responsabilidad y la colaboración entre todos los interesados, lo que contribuye al éxito general del proyecto.

La transparence pour communiquer est importante , elle se traduit par exposer la tracabilites des methodes utilisees pour evaluer la complexite , les normes mais aussi les data , choses tres importantes la complexite des data , jeux de testes etc..

A documentação deve ser clara, concisa e acessível. Utilize diferentes formatos conforme o público-alvo: Para Desenvolvedores: Comentários no código, anotações, e documentação técnica detalhada. Para Stakeholders: Resumos executivos, relatórios de impacto, e apresentações que destacam as implicações das limitações nos objetivos do projeto.

Comunicar las limitaciones de manera honesta y comprensible es crucial para establecer confianza y colaboración efectiva. Utiliza canales apropiados según el contexto: presentaciones para clientes, notificaciones para usuarios. Asegúrate de adaptar el nivel de detalle y tono de comunicación a cada audiencia. La transparencia fortalece las relaciones y permite abordar las limitaciones de manera efectiva.

If you are developing an algorithm of the complexity implied by this document, it is intellectual property. Communicate with your stake holders and work with them to translate the result of the team's efforts into something that can be appreciated by broader audiences. I appreciate the intent of what might be termed "radical transparency" but find that it is more akin to sending foxes through a wheat field with torches tied to their tales than accomplishing the intended goal of communication.

Don't wait for users to encounter issues before addressing limitations. Communicate them proactively during the design, development, and testing phases. Be transparent about the limitations and any potential risks associated with using the algorithm.

au dela des aspects theoriques des differentes mesures existant , le benchmarck quand c'est possible est une maniere tres operationelle pour evaluer les limites. Voir meme se rapprocher de problematiques connexes qui ont deja ete testees

Evaluar las limitaciones del algoritmo y su impacto implica seleccionar métricas adecuadas, recopilar datos y comentarios, analizar continuamente y considerar la retroalimentación de las partes interesadas. Esto nos permite identificar áreas de mejora y tomar medidas correctivas para garantizar la calidad, ética y rendimiento del algoritmo.

esenvolva estratégias para mitigar ou eliminar limitações. Isso pode incluir otimização de código, revisão de arquitetura, ou mesmo a redefinição de expectativas com stakeholders. Documente essas melhorias e comunica-as claramente a todos os envolvidos.

Where possible, suggest ways to mitigate or address the limitations. This could involve providing workarounds, suggesting alternative algorithms, or outlining plans for future improvements.

Isolate your algorithm as best you can so that it is a simple function call with inputs and outputs. This is how others will reason about interacting with your solution. If an AI model, what does the dataset need and how noisy can the data be? Also, try to reframe the problem you're solving. Do this a few times. Are you raising a bridge? Shrinking the boat? Draining the lake? In your limitation documentation you'll want to include this to justify why you chose the solution you did. Knowing the context of decision making is very useful to future maintainers.

Develop a unit test suite explicitly featuring cases where the algorithm runs into its limitations, and annotate/describe/tag cases accordingly. This serves multiple purposes: a) documentation through actual code b) can integrate with automated regressions c) fast turnaround for validation of any future improvements to the algorithm

- When you write an algorithm it’s very important to consider writing unit tests and generate code coverage to understand unit tests cover all use cases. - Consider benchmarking the algorithm so that any modification will be tracked for performance changes and memory need. Test cases provide enough details for developers to know the flow and objective of algorithms. For large audience one can consider documenting the pseudo code and work flow diagram as a picture speaks louder than text.

Any kind of technical limitations should be tracked in the source code itself: as comment with a concise (and synthetic) description and as link to the documentation. Information needs to be found where the audience can stumble on it. Engineers will find it in the code, PMs et al. will see it in the feature documentation or Wiki.

- When you write an algorithm it’s very important to consider writing unit tests and generate code coverage to understand unit tests cover all use cases. - Consider benchmarking the algorithm so that any modification will be tracked for performance changes and memory need. Test cases provide enough details for developers to know the flow and objective of algorithms. For large audience one can consider documenting the pseudo code and work flow diagram as a picture speaks louder than text.