While Natural Language Processing already plays a vital role for many organizations, recent instances of NLP bias could mean that take-up slows, and even broader AI programs could be affected. The solution is not trivial, but it is worth working step by step towards solving the problem.
Natural Language Processing (NLP) can be used for many real-world business problems, such as document classification and summarization, named entity extraction, machine translation, fact-checking, and question answering. It can help increase efficiency and effectiveness by reducing search time and improving relevance. It has become a highly effective way of using computers to solve problems traditionally handled by humans.
But instances of bias are occurring in NLP that have the potential to derail the use of these technologies. Implementing AI with modern machine learning (ML) involves two main components: an ML model with a specific architecture and a dataset that models one or more particular tasks.
Both parts can introduce biases.
The black-box nature of ML models can make it difficult to explain the decisions made by the models. Furthermore, models can overfit datasets or become overconfident and do not generalize well to unseen examples. However, in most cases, the dataset used for training and evaluation is the culprit for introducing bias.
A dataset may contain inherently biased information, for example, an unbalanced number of entities occurring.
Datasets that have been manually annotated by human annotators are particularly prone to bias, even if the annotators have been very carefully selected with different backgrounds. Large corpora obtained unsupervised from the World Wide Web still exhibit biases, e.g., due to differences in Internet availability around the world or differences in the frequency of speakers of certain languages.
NLP bias and discrimination
The downside is that populations that are underrepresented in particular data sets are, at best, unable to use an AI system to help them solve the desired task and, at worst, discriminated against because of how the AI predicts outcomes.
Discrimination based on the unfairness of an artificial model becomes a serious problem once AI systems are used to make potentially important decisions automatically with limited human oversight. Moreover, these problems also hinder the progress and acceptance of AI due to the mistrust that is rightly generated.
How to address the NLP Bias
Unfortunately, there is no silver bullet to solve the problem of bias in NLP, ML, or AI in general. Instead, an important component is awareness of the problem and an ongoing commitment to developing AI solutions that improve fairness.
Technically, there are a variety of theories and methods that are being actively researched and developed to improve fairness and explainability. These include but are not limited to measurement and reduction of bias in datasets, principles for balanced training of models, strategies for dealing with inherent uncertainty during inference, and ongoing monitoring of AI decision-making.
Ethics in AI
The recent field of Ethics in AI also plays a role in addressing NLP bias. The challenge is that AI is still a relatively young and fast-moving field of research and application. Although it has existed for many years, it is only recently that the deployment has become more widespread. We have not yet reached the plateau of stability, which is required to formulate and codify behaviors and norms, ensuring a fair playing field.
Our approach to this is threefold:
A) ongoing consciousness-raising internally and with customers and prospects around the issue of bias in AI modelling and AI-supported decision making.
B) calling for and contributing to industry and government working groups establishing the regulatory framework to operate AI responsibly and
C) implementing – not just discussing them – A & B.
NLP is an impactful technology, so useful that the industry cannot afford to let its use be affected by issues of bias. Such technologies work most effectively when they are used to augment human input and intelligence, not replace them. In addition to the above, addressing bias requires focus and industry-wide commitment to mitigate its negative impact.
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