Artificial Intelligence has transformed modern technology and influenced many areas of human life including healthcare, education, business, transportation, and communication. AI systems are now capable of making decisions, analyzing behavior, recognizing patterns, and automating complex tasks. Although AI provides many benefits, it also raises serious ethical and social concerns. Intelligent systems can influence employment, privacy, fairness, and human decision-making. If AI systems are designed or used irresponsibly, they may produce harmful or unfair outcomes. Ethical issues in AI mainly focus on ensuring that intelligent technologies are:
fair
transparent
secure
accountable
beneficial for society
As AI adoption increases globally, responsible development has become extremely important for researchers, governments, and technology companies. This chapter discusses some major ethical challenges associated with Artificial Intelligence and explains the importance of developing trustworthy AI systems.
12.1 Bias and Fairness in AI
Bias and fairness are among the most important ethical concerns in Artificial Intelligence. AI systems learn patterns from historical data, and if the training data contains unfair or biased information, the system may also produce biased decisions. Machine Learning models do not automatically understand fairness. They only learn from the examples provided during training. As a result, existing human biases present in datasets may unintentionally become part of AI systems. For example:
hiring systems may favor certain groups
facial recognition systems may perform unevenly across populations
loan approval systems may create unfair decisions
Such problems can affect individuals and society significantly. Understanding AI Bias
AI bias occurs when a system produces unfair or inaccurate outcomes for certain groups of people. Bias may enter AI systems through:
incomplete datasets
unbalanced training data
incorrect assumptions
human errors during development
For example, if a facial recognition model is trained mostly on limited types of faces, it may perform poorly for other groups. This demonstrates how data quality directly affects fairness in AI systems. Fairness in AI Systems Fairness means that AI systems should treat individuals and groups equally without discrimination. A fair AI system should:
provide balanced decisions
avoid harmful discrimination
work reliably for diverse users
Achieving fairness in AI is challenging because human societies themselves contain social and historical inequalities. Researchers therefore work continuously on methods to reduce bias and improve fairness in machine learning models.
DCMI Metadata Terms. Dublin Core has no element that separates the original file from the text extracted out of it, and none for LOM's educational characterisation. Both survive here as provenance statements and in the record itself, not in the projection.
the standard ↗
The groups below are this library's, for reading. DCMI Terms itself has no categories; each term keeps its standard name.
Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.
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Text extracted from pdf to Markdown by pdf-inspector; the original is retained unchanged beside it.
Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.
IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other.
the standard ↗
Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.
3 Meta-metadata
4/4
Identifier3.1
this engine
resource_id
URI: tag:aim-pro.eu,2026:oer/2237c62a2a9c/record
The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
Contribute3.2
this engine
source
AIM-PRO WP3 OER harvester (Zenodo) — creator
Who generated this record and when — a statement about the record, not about the resource.
Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
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The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.
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Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.
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Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.
Taxon path9.2
not collected — this library does not fill it
Where the competency framework goes. Empty in the record for the reason above.
Description9.3
not collected — this library does not fill it
Keyword9.4
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What could not be established5
Where the source's metadata could not be carried
over as it was — missing, contradictory, with no matching term in the
standard, restructured, or taken from the repository — and what was done
instead. Without these notes, an empty element would look like something
the harvester missed.
Status
Field
Why
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Not available
rights_holder
no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it
Not available
publisher
the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim
Not available
educational
the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them