Introduction
Autonomy
Transparency
Non Maleficence
Justice
Beneficence
Takeaways
Intro

Autonomy

We respect the autonomy of every single human being in our digital offerings.

We recognize that everyone has the right to live according to their ideas and make autonomous decisions as long as this does not harm the interests of others.

All this also applies in the digital context. People should be able to make autonomous decisions when transmitting their data or allowing an algorithm to analyze these data. The following three principles help us keep this in mind when developing digital services:

We explain our algorithmic systems.

Our conviction is that algorithmic systems should be explainable. Anyone using our digital services or considering using them should know whether they are directly or indirectly affected by an automated decision. If this is the case, we want to help them understand how algorithm-based processes achieve their results. And if there are algorithmic systems we cannot explain, we should carefully evaluate their added value.

Explainability

We safeguard the privacy of those who provide data.

We respect and protect the privacy of individuals – not only when collecting data but also when interacting with our digital offerings. That's why we ensure that individuals can make independent decisions about the use of their data.

Privacy

We foster digital literacy.

We make a conscious effort to promote the digital literacy of those who develop, use, operate, and make decisions about the use of algorithmic systems. How do we do this? By providing them with up-to-date knowledge about handling data and algorithmic systems. In addition, we encourage our employees to ask questions about digital ethics and examine our algorithmic systems' impact.

Literacy

Meet Ron

Ron is a Product Owner for Data Analytics in the HR department.

This department wants to do something about the company's high turnover rate – employees tend to leave the organization after only two to three years.

An HR Business Owner asks Ron to develop a machine learning tool to identify talented employees willing to stay.

This tool should help HR and managers optimize investments in training programs for individual employees and strengthen relationships with top internal talent.

What do you think?

A tool that helps determine who gets additional training and who does not has...

no impact on employees
A
little impact on employees
B
an immense impact on employees
C

Please answer the question above to move on.

Ron realizes the tool will have an immense impact on employees. After all, it will influence what training opportunities are offered to them. This, in turn, will affect their long-term career prospects.

Ron is aware of the responsibility this places on him. He is determined to think the matter through and find a sensible solution.

Ron gets to work right away. First, he will need to train a machine learning algorithm with data sets of employees' past performance to predict future trends.

Ron knows that when collecting training data, it is essential to ask whether the data respects individual privacy and data protection regulations.

Ron also realizes that no data set paints a complete picture of a person and that the tool thus has its limitations.

He will address these limitations and their implications early on so that the HR department can account for them in their workflows.

What do you think?

(multiple answers possible)

If the limitations and implications of the tool were not clear to everyone

people would not use the tool
people might unknowingly rely too much on the tool
employees might not get training opportunities, despite being a good fit
this wouldn't matter
Submit

Please complete the quiz above to move on. Select the correct answers and then click on submit.

Ron discusses these points with the HR Business Owner. He wants to ensure the tool meets the needs of HR. It should, however, also respect the privacy of individuals and be understood by all. With the HR Business Owner, he develops a tool that meets our principles of explainability, privacy, and literacy.

Well done, Ron!

Next lesson
Transparency