Why Explainability Will Be a Cornerstone of AI Adoption

Greater control and better interpretation of AI have become necessary to convince the most reluctant to adopt it.

It is no longer to be demonstrated, artificial intelligence has indeed interfered in our lives. So much so that we are not even aware of it anymore: voice assistants digital, facial recognition, social networks, connected cars… this omnipresence can curb a certain distrust of AI.

Until now, the process of calculating many AI models could be seen as a black box: the data scientists and engineers behind the creation of the models were not always able to explain the cogs inside that box – nor the results achieved. This lack of interpretation has made companies think twice before leaning towards AI, and more particularly in critical areas such as healthcare or finance. This is how the concept of Explainable AI emerged, which can be defined as a set of processes that helps humans understand how an algorithm arrives at a specific result.

However, Explainable AI is not just about disclosing the criteria the algorithm used to reach a decision, but also about revealing why it chose that particular option over the others. A company can rely on explainable AI to understand the strengths and weaknesses of its programs, the errors it is prone to and know the necessary corrections.

Why is interpretation necessary?

Explainable AI will be able to characterize the accuracy and results of models, and will also enable organizations to take a responsible approach when developingalgorithms . AI makes processes more transparent and, therefore, fairer. It becomes easier to comply with the laws. Better interpretation will also boost customer confidence: a better understanding of AI decisions instils a higher level of confidence in the organization. Finally, explainable AI can bring more tangible results by offering valuable insights into key business metrics.

How to develop this approach?

In sum, developers need to incorporate explainable AI techniques into their respective workflows. Easier said than done… this approach certainly cannot be driven by a single person or through the creation of a role – such as Ethics Manager or AI Manager. Just as a fair and just social order requires everyone’s involvement. The responsibility for applying explainable AI techniques rests with every employee in the organization. But how to achieve it?

1. Maintain data quality

The success of AI models depends on the quality of the data used to build them. Unreliable, inaccurate or biased data can lead to bias in the algorithm. Data quality checks, which rigorously assess each situation, verify the impact of each decision, and examine which part of the data led to a particular result, can mitigate unintended results.

2. Give users visibility into decision paths

Organizations need to provide their users with transparency over their data pipelines and data-driven processes in order to build trust. While AI algorithms are typically protected by intellectual property and not disclosed, when organizations share their code of conduct and guidelines for their data-driven programs, there is no denying that end-user trust will increase. sees strengthened.

3. Marry Explainable AI Practices to an MLOps Environment

MLOps should be integrated with Explainable AI quality checks. At each stage of the software development life cycle, in addition to testing for coding errors, it is possible to verify the desired results. Any deviation from the expected results can then be corrected before moving on to final production.

4. Human presence is necessary

Humans will continue to play a vital role, ensuring that AI models are “fair” from a human perspective and based on the right trends/insights.

5. Empower AI scientists

AI experts and data scientists must embrace their responsibilities by ensuring they perform the right training on the machines they work with. Organizations must also establish the code for ethical decision-making and disseminate information about the impact of their actions on the end user.

Explanations of AI models can make our AI systems more reliable, compliant, efficient, fair and robust. Optimal conditions to drive and accelerate the adoption of AI and its business value. The companies that will rely on such an approach will, for sure, be the ones that will do well in the long term.

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Why Explainability Will Be a Cornerstone of AI Adoption

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