Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity

Understand

Machine Learning (ML) is increasingly applied in real-life scenarios, raising concerns about bias in automatic decision making.

• We focus on bias as a notion of opinion exclusion, that stems from the direct application of traditional ML pipelines to infer subjective properties.
• We argue that such ML systems should be evaluated with subjectivity and bias in mind.
• Considering the lack of evaluation standards yet to create evaluation benchmarks, we propose an initial list of specifications to define prior to creating evaluation datasets, in order to later accurately evaluate the biases.