Corresponding author: Ronit Purian (

Academic editor:

In this paper we aim to provide an implementation of the FAIR Data Points (FDP) spec, that will apply our bias detection algorithm and automatically calculate a FAIRness score (FNS). FAIR metrics would be themselves represented as FDOs, and could be presented via a visual dashboard, and be machine accessible (

First we may discuss the context of this topic with respect to Deep Learning (DL) problems. Why are Bayesian Networks (BN, explained below) beneficial for such issues?

The motivation for using Bayesian Networks (BN) is to learn the dependencies within a set of random variables. The networks themselves are directed acyclic graphs (

In this paper we present a way of using the DL engine tabular data, with the python package

We begin by finding our optimal DAG.

DAG = bn.structure_learning.fit(dataframe)

We now have a DAG. It has a set of nodes and an adjacency matrix that can be found as follow:

print(DAG[

The outcome has this form Fig.

Where rows are sources (namely the direction of the arc is from the left column to the elements in the row) and columns are targets (i.e., the header of the column receives the arcs). When we begin drawing the obtained DAG, we get for one set of variables the following image: Fig.

We can see that the target node in the rectangle is a source for many nodes. We can see that it still points arrows itself to two nodes. We will discuss this in the discussion (i.e.,

So, we know how to construct a DAG. Now we need to train its parameters. Code-wise we perform this as follows:

model_mle = bn.parameter_learning.fit(DAG, dataframe, methodtype=

We can change ‘

In this paper we have presented some of the theoretical concepts of Bayesian Networks and the usage they provide in constructing an approximated DAG for a set of variables. In addition, we presented a real-world example of end to end DAG learning: Constructing it using BN, training its parameters using maximum likelihood estimation (MLE

FAIR metrics, represented as FDOs, can also be visualised and monitored, taking care of data FAIRness.

Ronit Purian

First International Conference on FAIR Digital Objects, poster

Constructing DAG. Credit: Authors

The outcome has this form

Target node

The obtained graph

A set of factorized conditional distributions that reflect the DAG’s structure: The training outcome for a given variable

DAG presentation code. Credit: Authors