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How do you manage conflicting Machine Learning goals?

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Machine learning (ML) is a powerful and versatile tool for solving complex problems, but it also poses some challenges for collaboration and teamwork. Sometimes, different ML goals may conflict with each other, such as accuracy, speed, interpretability, fairness, and robustness. How do you manage these trade-offs and align your ML objectives with your stakeholders' expectations and needs? In this article, we will explore some strategies and best practices for dealing with conflicting ML goals.

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1 Define your problem and metrics

Before you start building your ML solution, you need to clearly define your problem and the metrics that will measure your success. What are you trying to achieve with ML? Who are your users and customers? What are their pain points and preferences? How will you evaluate your ML model's performance and value? These questions will help you narrow down your ML goals and prioritize the most important ones. You also need to communicate your problem and metrics to your team members and stakeholders, so that everyone is on the same page and has a common understanding of the ML objectives.

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Balaram Panda

Generative AI
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"Start with a business problem / customer experience and work backwards towards technology " - Steve Jobs Step 1: Understand the As-Is Business Process. Step 2: Ask why the business wants to improve ?? List their pain points. This is your business objective. Step 3: Clearly define the target variable (TV). TV should be aligned with the business objective. For example, in a fraud prevention model, the TV could be historical fraud, while the ML outcome could be real-time fraud identification and blocking. In marketing, TV is customer purchased or not. Step 4: Communicate your understanding ??(1:3) with stakeholders who will benefit from and utilize the ML solution. Obtain their sign-off before proceeding with the technical work.

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Cuneyt Ozturk

Principal Technologist & Eureka Expert | Co-Founder aLL-i@MoveO
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Managing conflicting machine learning goals can be challenging but crucial for achieving effective outcomes. Here are some approaches to handle such conflicts: 1. Clearly define and prioritize goals: Clearly define the desired outcomes and goals of the machine learning system. 2. Consider multi-objective optimization: Instead of focusing on a single objective, consider a multi-objective optimization framework. 3. Weighted objectives: Assign weights to different objectives to reflect their relative importance. 4. Explore ensemble approaches: 5. Introduce constraints: 6.Leverage human oversight: 7. Iterative refinement: 8. Ethical considerations: Take into account ethical considerations when managing conflicting goals.

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2 Balance your trade-offs

Once you have defined your problem and metrics, you need to balance the trade-offs between different ML goals. For example, if you want to increase your model's accuracy, you may need to sacrifice some speed or interpretability. If you want to ensure your model's fairness, you may need to compromise some accuracy or robustness. You need to weigh the pros and cons of each ML goal and decide how much you are willing to trade off for another. You also need to consider the context and constraints of your problem, such as the data availability, quality, and diversity, the computational resources and costs, the ethical and legal implications, and the user feedback and satisfaction.

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Helen Wall

LinkedIn [in]structor | Data Science Consulting
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A common approach is to first focus on accuracy and then focus on speed. However, that's often contingent on the type of model and more importantly how the business users are utilizing its results. If we're running our models at 3 am we probably don't have to worry about speed too much. But if we're running a decision tree algorithm on a website that directly interfaces with the customer, speed matters a lot more. Even a few seconds of latency can make a difference in whether a customer puts the item in their cart or navigates to another website entirely.

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Balaram Panda

Generative AI
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Step 1: Model Explainability vs Accuracy: Ask your stakeholders about their priorities and where they can make compromises. For example, a marketing model might not require high explainability, while a risk assessment model would demand more explanation to ensure decisions aren't biased based on factors like gender or ethnicity. Step 2: Speed (Model Building and Inferencing) vs Accuracy: Develop a modelling (dev and deployment) plan based on: * Expected Frequency of model refresh . * Real-time inference or batch inference in production. If model updates are infrequent and batch inference is expected, prioritize the model that achieves the best accuracy on the testing sample.

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3 Experiment and iterate

Balancing trade-offs is not a one-time decision, but a continuous process of experimentation and iteration. You need to test your ML model on different data sets, scenarios, and environments, and measure its performance on your metrics. You need to compare your results with your baseline and your competitors, and identify the gaps and opportunities for improvement. You need to solicit feedback from your team members, stakeholders, and users, and incorporate their insights and suggestions into your ML model. You need to monitor your ML model's behavior and impact over time, and update it as needed. You need to be flexible and adaptable, and ready to adjust your ML goals and strategies according to the changing conditions and requirements.

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Rohit Raj Jalheria

Data Scientist| NLP| ML| LLMs
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The trade-offs are one of the key aspects to decide how the model will predict. These trade-offs could be decided in a better way once we understand the problem statement, business needs, domain expertise, source of data and it's quality, project contributors and finally the purpose that the model is going to serve. At every stage of the model building and training these trade-offs have to be reviewed and revised based to make the best possible decision. Often the performance is compromised when it comes to selection between accuracy and the time of execution.

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4 Collaborate and communicate

Managing conflicting ML goals is not only a technical challenge, but also a social and organizational one. You need to collaborate and communicate effectively with your team members and stakeholders, and build trust and alignment among them. You need to share your ML goals, assumptions, methods, results, and challenges with them, and explain the trade-offs and rationale behind your decisions. You need to listen to their perspectives, expectations, and concerns, and address them in a respectful and constructive way. You need to acknowledge the limitations and uncertainties of your ML model, and be transparent and honest about its strengths and weaknesses. You need to leverage the diverse skills, knowledge, and experiences of your team members and stakeholders, and foster a culture of learning and innovation.

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Cuneyt Ozturk

Principal Technologist & Eureka Expert | Co-Founder aLL-i@MoveO
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Leverage human oversight: Involve human experts and stakeholders in the decision-making process. Their knowledge and understanding of the problem domain can help navigate conflicts and make informed choices. 7. Iterative refinement: Machine learning models are not fixed and can be refined over time. Continuously evaluate and analyze the performance of the model with respect to different goals. Refine the model and its objectives as necessary to achieve a better balance. 8. Ethical considerations: Take into account ethical considerations when managing conflicting goals. Ensure that the objectives align with ethical standards and promote fairness, transparency, and accountability.

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5 Learn from others

Managing conflicting ML goals is not a unique or isolated problem, but a common and shared one in the ML community. You can learn from others who have faced similar or related problems, and benefit from their insights and solutions. You can read research papers, blogs, books, and podcasts that discuss the trade-offs and best practices of different ML goals. You can join online forums, groups, events, and courses that offer opportunities for networking, discussion, and education on ML topics. You can seek advice, feedback, and support from your mentors, peers, and experts in the ML field. You can also contribute to the ML community by sharing your own experiences, challenges, and lessons learned, and helping others who are struggling with conflicting ML goals.

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6 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 do you manage conflicting Machine Learning goals?

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6

1 Define your problem and metrics

2 Balance your trade-offs

3 Experiment and iterate

4 Collaborate and communicate

5 Learn from others

6 Here’s what else to consider

Machine Learning

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How do you manage conflicting Machine Learning goals?

1

2

3

4

5

6

1 Define your problem and metrics

2 Balance your trade-offs

3 Experiment and iterate

4 Collaborate and communicate

5 Learn from others

6 Here’s what else to consider

Machine Learning

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