Attending to the things that matter is the foremost task of any manager. This means deciding which things matter and how much time to attend to those things. And as we all know the time to decide is shrinking dramatically while the number of to be taken decisions is growing by magnitudes.

We at Trufa are set out to help business managers with these decisions. We are trying to mimic human reasoning with machine learning. For this we are applying following principles in our algorithms:

Attention allocation algorithms must be comprehensible by human beings.
Algorithms people don’t trust in will not be accepted.

Attention allocation algorithms must produce dependable results.
Algorithms never produce 100% precise results. But their margin of error must be negligible.

Attention allocation algorithms must produce reproducible results.
Algorithms must be reliable.

Attention allocation algorithms must be robust against outliers.
Human beings tend to overly turn to outliers though often these outliers are unmanageable.

Attention allocation algorithms must be unbiased.
Among others multi-variate regressions make assumptions about the applicable variates.

Attention allocation algorithms must be diligently applied.
In the 80ties expert systems were the cure for everything. Nowadays machine learning is overrated.

Attention allocation algorithms must work in real-time.
Decisions are to be taken ever faster these days.

Attention allocation algorithms must work on very large data sets.
ERP systems are growing day by day. And the digital transformation is accelerating this.

Attention allocation algorithms must work for various businesses in various industries.
Trustworthiness beats individualism.

Attention allocation algorithms must work across multiple ERP systems.
Non-matching document ids and master data must not throw of the algorithms.

We at Trufa believe that such principles are key to trust in machine learning and artificial intelligence.

What about you?