How can you ensure AI models are transparent and explainable to stakeholders?

AI models are powerful tools for generating insights, predictions, and recommendations from data. But how can you ensure that they are transparent and explainable to stakeholders, such as customers, regulators, and decision-makers? In this article, you will learn some best practices for data governance and artificial intelligence, and how to apply them to your AI projects.

1 Define your objectives and expectations

Before you start building or deploying any AI model, you need to define your objectives and expectations clearly. What problem are you trying to solve? What value are you trying to create? What are the ethical, legal, and social implications of your model? How will you measure and evaluate its performance and impact? By answering these questions, you will be able to align your model with your business goals, stakeholder needs, and regulatory requirements.

Add your perspective

2 Choose appropriate data and methods

The quality and relevance of your data and methods will affect the transparency and explainability of your AI model. You need to ensure that your data is accurate, complete, consistent, and representative of your target population. You also need to choose methods that are suitable for your problem domain, data characteristics, and desired outcomes. For example, if you need to provide clear and intuitive explanations for your model's decisions, you may opt for simpler or interpretable methods, such as decision trees or linear models, over more complex or black-box methods, such as deep neural networks or ensemble models.

Add your perspective

Daniel Saksenberg

Co-founder & Chief AI Officer - Emerge
Report contribution
While complete and accurate data is always first prize, almost no organization has perfect data. One of the major advantages of machine learning is that is can still produce valuable models from "dirty" data, provided the data is consistently dirty. One need not wait for the "perfect" dataset to get started. Most ML methods at present are black boxes in terms of what they actually learn. But one can communicate how the model learns to stakeholders (ideally with simple examples) to provide confidence that the algorithm's learnings are well-founded.

Like

3 Document and communicate your process and results

One of the key aspects of data governance and artificial intelligence is documentation and communication. You need to document and communicate your process and results throughout the entire AI lifecycle, from data collection and preparation, to model development and testing, to model deployment and monitoring. You need to provide evidence and justification for your data sources, methods, assumptions, limitations, and outcomes. You also need to communicate your process and results in a clear, concise, and understandable way to your stakeholders, using appropriate formats, channels, and languages.

Add your perspective

Daniel Saksenberg

Co-founder & Chief AI Officer - Emerge
Report contribution
Just like human experts, ML algorithms need to earn trust, especially given how "black-box-y" they are. Showing, with real-world, live data, that they are consistently making good decisions (and, hopefully, better decisions than the best human experts) goes a long way to earning this trust. Model performance monitoring builds this "trust" data over time. It also helps to know when a model needs retraining, or when there are fundamental shifts in the environment in which the model has been trained. Most stakeholders are not ML experts or statisticians. It is important to communicate in terms that are meaningful to these users in real-world terms. Nobody cares about RMSE or MAE, but they do care about sales, correct diagnoses, profit etc

Like

4 Implement feedback and accountability mechanisms

Another important aspect of data governance and artificial intelligence is feedback and accountability. You need to implement feedback and accountability mechanisms to ensure that your AI model is transparent and explainable to stakeholders, and that you can address any issues or concerns that may arise. For example, you may provide users with options to request more information, report errors, or challenge results. You may also establish audit trails, logs, and reviews to track and verify your model's actions and outcomes. You may also assign roles and responsibilities to ensure that your model is compliant, ethical, and fair.

Add your perspective

5 Keep learning and improving

Finally, you need to keep learning and improving your AI model and your data governance practices. You need to monitor and evaluate your model's performance and impact on a regular basis, using relevant metrics and indicators. You need to collect and analyze feedback from your stakeholders, and use it to improve your model's transparency and explainability. You also need to keep up with the latest developments and best practices in data governance and artificial intelligence, and apply them to your AI projects.

Add your perspective

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?

Add your perspective

Kevin Posen

Investment professional
Report contribution
In my view, this is the wrong benchmark. The comparison is to real-world humans -- not to some textbook ideal. Humans are not so good at explainability and transparency. In my experience, more often than not client questions are answered with post-hoc narratives that are reminiscent of "Just So Stories". More often than not (again, in my experience) the transparency of humans was an illusion, presumably rooted in our tribal instinct to trust "nice" and familiar people. Why did Steve Jobs or Henry Ford or Howard Hughes or Elon Musk do XYZ? Mostly by intuition, in my view, which they themselves would not be able to explain. Measured against this benchmark, machines already do ok.

Like

How can you ensure AI models are transparent and explainable to stakeholders?

1

2

3

4

5

6

1 Define your objectives and expectations

2 Choose appropriate data and methods

3 Document and communicate your process and results

4 Implement feedback and accountability mechanisms

5 Keep learning and improving

6 Here’s what else to consider

Data Governance

Rate this article

Thanks for your feedback

More articles on Data Governance

More relevant reading

How can you ensure AI models are transparent and explainable to stakeholders?

1

2

3

4

5

6

1 Define your objectives and expectations

2 Choose appropriate data and methods

3 Document and communicate your process and results

4 Implement feedback and accountability mechanisms

5 Keep learning and improving

6 Here’s what else to consider

Data Governance

Rate this article

Thanks for your feedback

Explore Other Skills