登录查看更多内容

What challenges do ANNs present to achieving interpretability?

由人工智能和领英社区提供技术支持

Artificial neural networks (ANNs) are powerful and versatile machine learning models that can learn complex patterns from data. However, they are also notoriously difficult to understand and explain, especially when they have many layers and nodes. This poses a challenge for achieving interpretability, which is the ability to provide meaningful and intuitive explanations for how and why a model makes a decision or prediction. In this article, you will learn about some of the main challenges that ANNs present to achieving interpretability, and some of the possible solutions and trade-offs.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

1 Black-box problem

One of the most fundamental challenges of ANNs is that they are often considered as black boxes, meaning that their internal workings are opaque and hidden from the users. This makes it hard to trace the logic and reasoning behind their outputs, and to identify the relevant features and factors that influence them. Moreover, it makes it difficult to verify, validate, and debug the models, and to ensure that they are fair, ethical, and trustworthy. The black-box problem is exacerbated by the increasing complexity and scale of ANNs, which often involve millions of parameters and nonlinear transformations.

添加您的观点

Paul Eder, PhD

I Lead You to New Insights | Strategy Consulting, Artificial Intelligence, & Data Innovation | Author of FIRESTARTERS
举报内容
As an analyst, I want a tool that I can tweak and understand how results change. If I design a system so complicated that I don't understand it - then when it breaks, I won't be able to fix it. Always build schematics that allow your tool to be decided. Otherwise, you're creating a tool that will become obsolete or lose real predictive power over time.

已翻译

赞
Sanjay Kumar MBA,MS,PhD
举报内容
A significant challenge in Artificial Neural Networks (ANNs) is their "black-box" nature, where their internal operations are concealed, making it challenging to understand the rationale behind their outputs and the factors that affect them. This opacity hinders the ability to verify, validate, and debug the models, as well as ensure fairness, ethics, and trustworthiness. The problem is compounded by the growing complexity and size of ANNs, often involving millions of parameters and nonlinear transformations.

已翻译

赞

2 Lack of standards

Another challenge of ANNs is that there is no widely accepted and consistent definition of what interpretability means, and how to measure and evaluate it. Different users, domains, and applications may have different expectations and requirements for interpretability, depending on the context, purpose, and audience of the model. For example, a medical practitioner may need a detailed and causal explanation for a diagnosis, while a marketing analyst may only need a summary and correlation for a customer segment. Moreover, there is no clear and common framework or methodology for designing, implementing, and testing interpretable ANNs, which leads to a diversity and disparity of approaches and techniques.

添加您的观点

3 Trade-off with performance

A third challenge of ANNs is that there is often a trade-off between interpretability and performance, meaning that increasing one may decrease the other. This is because interpretability often requires simplifying or restricting the model architecture, parameters, or features, which may reduce its accuracy, robustness, or generalization. For example, a shallow or sparse ANN may be easier to interpret than a deep or dense one, but it may also have lower predictive power or flexibility. Therefore, finding the optimal balance between interpretability and performance is a difficult and context-dependent task, which may involve compromising or sacrificing some aspects of the model.

添加您的观点

Sanjay Kumar MBA,MS,PhD
举报内容
A key challenge with Artificial Neural Networks (ANNs) is the trade-off between interpretability and performance. This trade-off means that enhancing one aspect may come at the cost of the other. Achieving interpretability often involves simplifying or constraining the model, which can lead to reduced accuracy, robustness, or generalization. For instance, a shallow or sparse ANN might be more interpretable but less powerful than a deep or dense one. Balancing interpretability and performance is a complex and context-specific task, sometimes requiring compromises or sacrifices in certain model aspects to strike the right balance.

已翻译

赞

4 Data and domain issues

A fourth challenge of ANNs is that they are not only influenced by their own structure and function, but also by the data and domain that they are trained and applied on. This means that interpretability may depend on the quality, quantity, and diversity of the data, as well as the complexity, variability, and specificity of the domain. For example, noisy, incomplete, or biased data may lead to misleading or inaccurate interpretations, while complex, dynamic, or heterogeneous domains may require more sophisticated or adaptive interpretations. Therefore, interpretability requires not only understanding the model, but also the data and domain.

添加您的观点

5 Human factors

A fifth challenge of ANNs is that they are ultimately intended to interact with and serve human users, who have their own cognitive, emotional, and social factors that affect how they perceive and process interpretations. This means that interpretability may vary depending on the user's background, expertise, preference, and goal, as well as the mode, format, and timing of the interpretation. For example, a technical user may prefer a mathematical or graphical interpretation, while a non-technical user may prefer a verbal or visual interpretation. Moreover, interpretability may influence the user's trust, satisfaction, and engagement with the model, which may have positive or negative consequences.

添加您的观点

Sanjay Kumar MBA,MS,PhD
举报内容
One of the challenges with Artificial Neural Networks (ANNs) is their interaction with human users. Humans bring their cognitive, emotional, and social factors that can influence how they perceive and process interpretations provided by ANNs. Interpretability can vary depending on the user's background, expertise, preferences, and goals, as well as the mode, format, and timing of the interpretation. For instance, technical users may prefer mathematical or graphical interpretations, while non-technical users may favor verbal or visual explanations. Additionally, interpretability can impact a user's trust, satisfaction, and engagement with the model, leading to various outcomes, both positive and negative.

已翻译

赞

6 Future directions

The challenges of ANNs for achieving interpretability are not insurmountable, and there are many ongoing research and development efforts to address them. Some of the possible solutions include using hybrid or modular architectures that combine ANNs with other interpretable models, applying post-hoc or intrinsic methods that extract or enhance interpretations from ANNs, developing standardized and comprehensive metrics and benchmarks for assessing interpretability, incorporating data and domain knowledge and constraints into ANNs, and designing user-centric and interactive interpretations that suit the user's needs and expectations. These solutions may not only improve the interpretability of ANNs, but also their performance, reliability, and usability.

添加您的观点

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?

添加您的观点

Machine Learning

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What challenges do ANNs present to achieving interpretability?

1

2

3

4

5

6

7

1 Black-box problem

2 Lack of standards

3 Trade-off with performance

4 Data and domain issues

5 Human factors

6 Future directions

7 Here’s what else to consider

Machine Learning

给文章评分

感谢您的反馈

更多Machine Learning相关文章

更多相关阅读内容

What challenges do ANNs present to achieving interpretability?

1

2

3

4

5

6

7

1 Black-box problem

2 Lack of standards

3 Trade-off with performance

4 Data and domain issues

5 Human factors

6 Future directions

7 Here’s what else to consider

Machine Learning

给文章评分

感谢您的反馈

查看其他技能