Ethical AI in Talent Development has a name: BAST.AI. Founder and CEO Beth Rudden and I worked on deploying ethical AI in people analytics, augmenting decisions, based on transparency, explainability and German data privacy regulations, in close collaboration with key stakeholders including the works councils.   

Ethical AI supports decision making based on evidence and with that, can contribute measurably to the productivity and sustainability of the workforce. Examples include: 

• Future driven development of roles and skills (soft / hard skills)
• Anticipating and preventively addressing pain points
• Recognizing and making targeted use of skill strengths
• Focused enablement investments, ROI of learning
• Transparent career opportunities based on talent

With that, AI has the potential to strengthen the stratgic contribution of HR and Talent Development functions business success: HR related insights generated with the support of AI can become critical for business decisions and success.

Step 1:

We take your data and extract the entities and relationships that describe your organization's language. We generate a formal knowledge graph called an ontology. 

Step 2: 

We use the ontology to understand your data in relation to the language of your organization. Listen to Beth on the importance of language.

Step 3:

We can use the ontology to infer that you may have evidence for skills or competencies.

An Ontology represents a network of information with logical relations. The picture above shows a knowledge graph of an ontological network of skills related to one specific skill that we searched (blue circle top left). All other circles are representing skills related to the target skill. Colour code, position and relation to each other is based on explainable relations. Ontologies come with 4 main characteristics: 

1. Concept search: Concepts directly related to a particular skill are used, as well as any known language variation (beyond key word searching)
2. Continously learning: Ontology development is an iterative process. It is continously improving and validating (machine and human)
3. Explainable: Ontologies function like a "brain". They work and reason with concepts and relationships in ways that are close to the way humans perceive interlinked concepts. 
4. Scalable: Easy to extend and add to existing ontologies. As a result, the model evolves with the augmenting data structure without impacting related systems.