Explore KSACs by Pathway
In late 2025, the Partnership pulled together members of industry from across tech to share how the in-demand skillsets for entry-level roles in their fields have changed in the last two years. The Knowledge, Skills & Abilities and Credentials (KSACs) below reflect their feedback on entry-level expectations, especially in a tech workplace increasingly shaped by AI.
5. Data Ethics
| Label | KSAC Description | Bloom's Taxonomy Level | |
|---|---|---|---|
| a | Identify the established ethical and legal issues in data management facing organizations. | knowledge | 2 |
| b | Distinguish between technical feasibility and appropriate use of machine learning in applied contexts | knowledge | 2 |
| c | Identify the established ethical and legal issues in data management facing organizations. | knowledge | 2 |
| d | Apply ethical and compliance considerations when making or supporting data-driven decisions | ability | 2 |
| e | Explain the important issues around data governance. | knowledge | 2 |
| f | Present real world examples of data bias and the unintended consequences of using analytics, machine learning, and AI in making decisions. | knowledge | 2 |
| g | Explain the importance of provenance, transparency, and explainability in building trust in machine learning systems. | knowledge | 2 |
| h | Explain the importance of personal privacy issues related to the collection and usage of data. | knowledge | 2 |
| i | Explain the limitations and potential unintended effects of machine learning when such algorithms encounter new scenarios. | knowledge | 2 |
| j | Explain how scale, automation, and data volume can amplify errors, bias, and unintended consequences in machine learning systems | knowledge | 2 |
| k | Explain individual and data bias and the implications each has in data analysis. | knowledge | 2 |