Natural Language Processing (NLP) is the foundation that supports the technology around us today: from search engines to automated customer service. As these systems gain an increasing influence on social and economic outcomes, however, the question of bias in NLP has become hugely important. In this paper, we provide a comprehensive review of bias in NLP, from its sources, and societal impacts to the current approaches to mitigating it. We look at recent studies of data and algorithmic biases that persist and have a disproportionate impact on marginalized communities. Our results stress the necessity of interdisciplinary approaches to these challenges by merging the insights of computer science, linguistics, and ethical and social sciences. To this end, we develop a framework for building fairer, more inclusive NLP systems that leverage diverse data in combination with state-of-the-art debiasing methods and ethical AI guidelines. This work adds to an old debate about making ethical AI while also suggesting where to direct future efforts on the creation of fairer NLP technologies.