Our belief is that transparency, consistency, and reliability are required to earn trust. And so, we choose to build these qualities into our products alongside qualities core to machine learning like robustness and accuracy.
Humans Have Established Ways of Building Trust
Why these qualities? Because these are the same qualities we desire in people. We naturally build trust in someone over time; when they do what they say they will do, they do it well, and they do it consistently.
When we meet someone for the first time as a complete stranger, we have no basis to know how they will behave. For example, if we join a new football team and a teammate offers to give us a ride to the game, we will probably instinctively create a backup plan in case the new teammate doesn’t show up. After the first ride is successful, we become less cautious. Of course, if this same teammate drives erratically or gets lost, we will lose confidence in them. Lastly, the teammate has to continue to do what they have promised, or eventually, they will lose our trust.
Trusting Augmented Systems
Just like in our inter-personal relationships, earning consumer trust is important for SAP systems, especially when it comes to machine learning technology.
To build trusted features, we have to embed the qualities that people use to build trust with each other into our systems. At SAP, this means we are investing in a number of priorities to build the trust between our end users and our systems.
Priority 1: Be Transparent
When machine learning technology provides you with a particular result we need to be transparent in how the technology arrived at this answer. We do this through various techniques of Explainable AI including reason codes, key influencers, quality and precision KPIs.
For example, if you predict customer lifetime value using SAP Analytics Cloud the system automatically provides a list of key indicators that contribute to the resulting prediction. To further explain the results, you’ll also see a list of outliers that were so unusual that they were excluded from the underlying machine learning model.
Priority 2: Be Consistent
For machine learning technology to be trusted, the results must be consistent. At SAP, this means being able to provide a consistent experience across a wide range of use cases.
For example, you’ll see the same quality indicators when you forecast in different workflows throughout SAP Analytics Cloud. The indicators will be consistent whether you are working inside a chart, building a planning forecast, or building a predictive model. With the same question and data, you should receive the same answer, regardless of the workflow.
Priority 3: Be Reliable
Lastly, machine learning technology must be reliable. We want you to use SAP Analytics Cloud with confidence in our respect for model accuracy and robustness.
For this, we turn to one of our core SAP Analytics Cloud values: tell it like it is. Sometimes a machine learning algorithm can’t solve your business problem for myriad reasons. Maybe there isn’t enough input data, there aren’t any strong patterns in the data, or the data isn’t in the right shape. These scenarios are highlighted by low-quality indicators or, if the input data is very unreliable, we will give you no results at all. That way when you do get results, you know you can trust them.
IDC trends show that data will explode ten-fold by 2025, meaning human intelligence simply isn’t enough. Without being able to trust automated systems, you can’t harness the value of the vast data inside.
- Watch our webinar: Leveraging Machine Learning in SAP Analytics Cloud
- Dive deep into the augmented analytics capabilities in SAP Analytics Cloud
This article originally appeared on the SAP Analytics Blog and has been republished with permission.