Empowering Non-Experts to Create and Understand Their Own Forecasts Through Accessible Forecasting Software

Core Concepts

Designing forecasting software that empowers non-expert users to create and understand their own forecasts by integrating state-of-the-art forecasting methods with human-centered design principles.

Abstract

The study focuses on designing forecasting software that is accessible and transparent for non-expert users. The researchers prototyped an end-to-end forecasting software with a graphical user interface and gathered feedback from 19 participants with varying levels of forecasting experience. The key findings include: A stepwise approach helps domain experts generate their own forecasts and understand cause-and-effect relationships. Users appreciated being able to make iterative adjustments and observe the model's reactions. A white-box model with explainable components is important for effectively embedding domain knowledge and building understanding and trust in the produced forecasts. Users were able to interpret the model's inner workings using familiar concepts like trend and seasonality. Domain expertise is essential for operating the forecasting software and interpreting the results. Users with domain knowledge were better able to integrate relevant factors, validate the plausibility of forecasts, and generate meaningful insights. The researchers provide design recommendations to make forecasting more accessible to a broader audience, including creating a safe "forecasting playground" for experimentation, educating users on forecasting concepts, and incorporating domain-specific questions and guidance.

Stats

"Forecasts inform decision-making in nearly every domain." "Recent years have seen rapid innovation in forecasting methods with Deep Learning leveraging more data to achieve higher accuracy." "Modern forecasting generally does not include relevant human judgment nor domain knowledge, instead purely relying on data." "A lack of transparency may lead to rejection and a return to human judgement."

Quotes

"I think the step-by-step process is good." "I feel like the process gave me confidence when I saw error values decreasing." "It all made sense to me with the kind of hints there."

Key Insights Distilled From

Designing forecasting software for forecast users: Empowering non-experts to create and understand their own forecasts

by Richard Stro... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14575.pdf

Deeper Inquiries

How can forecasting software be designed to better integrate domain experts and their knowledge throughout the entire forecasting workflow?

In order to better integrate domain experts and their knowledge into the forecasting workflow, forecasting software should be designed with the following considerations: Domain-Specific Customization: The software should allow domain experts to customize the forecasting model based on their specific knowledge and requirements. This could include the ability to incorporate industry-specific variables, adjust model parameters, and define relevant features for the forecast. Interpretability and Transparency: The software should provide clear explanations of how the forecasting model works and how domain knowledge is being utilized. This transparency helps domain experts understand the reasoning behind the forecasts and build trust in the system. Collaborative Features: Incorporating features that facilitate collaboration between domain experts and the forecasting system can enhance the integration of domain knowledge. For example, the software could allow for feedback loops where domain experts can provide insights or adjust the model based on their expertise. Educational Resources: Providing educational resources within the software can help domain experts understand the forecasting process better and make informed decisions. This could include tutorials, guides, and explanations of forecasting concepts tailored to their domain. Flexibility and Adaptability: The software should be flexible enough to accommodate different types of domain knowledge and adapt to various industry contexts. Customization options and adaptable models can help ensure that the software is relevant and useful across different domains. By incorporating these design principles, forecasting software can effectively integrate domain experts and their knowledge throughout the entire forecasting workflow, leading to more accurate and actionable forecasts.

What are the potential challenges and limitations in creating a truly domain-agnostic forecasting system that can be easily adapted to different industries and use cases?

Creating a domain-agnostic forecasting system that can be easily adapted to different industries and use cases poses several challenges and limitations: Diverse Data Sources: Different industries may have unique data sources and formats, making it challenging to create a one-size-fits-all forecasting system. Adapting to these diverse data sources while maintaining accuracy and reliability can be complex. Industry-Specific Variables: Each industry may have specific variables and factors that influence forecasting outcomes. Designing a system that can accommodate these variables while remaining flexible and adaptable is a significant challenge. Domain Knowledge: Understanding the nuances of various industries requires domain expertise, which may not always be readily available within the design and development team. Incorporating domain knowledge effectively into a generic forecasting system can be a hurdle. Model Complexity: Balancing the complexity of the forecasting models to be robust and accurate across different industries while keeping the system user-friendly and accessible can be a delicate balance. Evaluation and Validation: Ensuring the performance and reliability of a domain-agnostic system across diverse industries requires extensive testing, validation, and feedback from domain experts. This process can be resource-intensive and time-consuming. Interpretability and Explainability: Making the forecasting system interpretable and explainable across different industries is crucial for user trust and acceptance. Ensuring that the system can provide meaningful explanations regardless of the industry context is a significant challenge. Addressing these challenges and limitations requires a thoughtful and iterative approach to system design, incorporating feedback from domain experts, and continuously refining the system to meet the diverse needs of different industries.

How might advances in explainable AI and human-AI collaboration further enhance the accessibility and trustworthiness of forecasting software for non-expert users?

Advances in explainable AI and human-AI collaboration can significantly enhance the accessibility and trustworthiness of forecasting software for non-expert users in the following ways: Interpretability: Explainable AI techniques can provide users with insights into how the forecasting model works and why specific predictions are made. This transparency helps non-expert users understand the rationale behind the forecasts, leading to increased trust in the system. User-Friendly Explanations: By presenting explanations in a user-friendly and intuitive manner, non-expert users can easily grasp the reasoning behind the forecasts without needing a deep understanding of complex algorithms or technical details. Interactive Visualizations: Interactive visualizations can help non-expert users explore the forecasting results, understand the impact of different variables, and gain insights into the forecasted outcomes. This hands-on approach enhances user engagement and comprehension. Human-AI Collaboration: By fostering collaboration between non-expert users and the AI system, users can provide feedback, input domain knowledge, and validate the forecasts. This collaborative approach ensures that the system benefits from human expertise while empowering users to make informed decisions. Feedback Mechanisms: Incorporating feedback mechanisms that allow users to provide input on the forecasting process can improve the accuracy and relevance of the forecasts. Non-expert users can contribute their domain knowledge and insights, enhancing the overall forecasting quality. Educational Resources: Explainable AI can also serve as an educational tool for non-expert users, helping them learn about forecasting concepts, model interpretations, and best practices. This educational aspect can build user confidence and competence in using the forecasting software. By leveraging advances in explainable AI and promoting human-AI collaboration, forecasting software can become more accessible, transparent, and trustworthy for non-expert users, ultimately leading to better decision-making and outcomes.