  Forced labour is the most common type of modern slavery, and it is
increasingly gaining the attention of the research and social community. Recent
studies suggest that artificial intelligence (AI) holds immense potential for
augmenting anti-slavery action. However, AI tools need to be developed
transparently in cooperation with different stakeholders. Such tools are
contingent on the availability and access to domain-specific data, which are
scarce due to the near-invisible nature of forced labour. To the best of our
knowledge, this paper presents the first openly accessible English corpus
annotated for multi-class and multi-label forced labour detection. The corpus
consists of 989 news articles retrieved from specialised data sources and
annotated according to risk indicators defined by the International Labour
Organization (ILO). Each news article was annotated for two aspects: (1)
indicators of forced labour as classification labels and (2) snippets of the
text that justify labelling decisions. We hope that our data set can help
promote research on explainability for multi-class and multi-label text
classification. In this work, we explain our process for collecting the data
underpinning the proposed corpus, describe our annotation guidelines and
present some statistical analysis of its content. Finally, we summarise the
results of baseline experiments based on different variants of the
Bidirectional Encoder Representation from Transformer (BERT) model.
