  As machine learning methods are deployed in real-world settings such as
healthcare, legal systems, and social science, it is crucial to recognize how
they shape social biases and stereotypes in these sensitive decision-making
processes. Among such real-world deployments are large-scale pretrained
language models (LMs) that can be potentially dangerous in manifesting
undesirable representational biases - harmful biases resulting from
stereotyping that propagate negative generalizations involving gender, race,
religion, and other social constructs. As a step towards improving the fairness
of LMs, we carefully define several sources of representational biases before
proposing new benchmarks and metrics to measure them. With these tools, we
propose steps towards mitigating social biases during text generation. Our
empirical results and human evaluation demonstrate effectiveness in mitigating
bias while retaining crucial contextual information for high-fidelity text
generation, thereby pushing forward the performance-fairness Pareto frontier.
