Gender bias within AI
Artificial intelligence is today’s big thing. Everyone’s talking about and everyone’s eager to see all the possibilities. But, despite all the positives AI can bring to a business, when it comes to gender biases within AI, one needs to recognize that these biases stem from peoples’ inherent biases.
Whatever model or system we create is a reflection of our biases.
Let’s give you an example. Gender bias can be seen with word-embeddings. Word-embeddings is when words are converted to numerical representations, which are used as inputs in natural language processing models (NLP). These word-embeddings represent words as a sequence of numbers. Essentially, if two words have similar meanings, the associated embeddings will be closer together (in a mathematical sense), which means that AI can “objectively” finish sentences.
For example, assigning queen to female or king to male. But, the issues come in when AI fills sentences such as boys to engineers and female to nurses. These inherent gender biases reflect an outdated perception and are not an accurate representation of our reality.
This is just one example. Research has also shown gender bias in speech with respect to emotion, which comes from when people misinterpret the emotions of one demographic category more often than another. Machines observe these biases and mislabel information related to emotions, causing bias within AI.
We know that there is a greater level of bias error for specific demographic categories and that there are variables that need to be taken into account when developing or training machine-learning models.
But, what other factors cause these AI biases?
- A skewed dataset: If you have missing data from the training data that goes into the machine-learning models, there will be errors when using the AI tool. The models you create will fail to scale or acknowledge those who weren’t part of the initial dataset. For instance, if your dataset only includes 15% of female speakers then there will be issues when females use your AI tool.
- Training labels: Most likely, the training data is labeled by people to teach the model how to behave. But, because people have conscious and unconscious biases, these can unintentionally be encoded to the machine-learning models. When the AI tools estimate these labels, which they do, this will lead to misclassification and unfairness, leading to bias.
- Features: Text-to-speech technology, also known as field speech synthesis, for instance, has been known to perform poorly for female speakers because speech used to be analyzed and modeled for taller and longer vocal cords, which are typically males but females have a higher-pitched voice, and so the tools don’t accurately decipher the speech for females.
We’ve given you a lot of information on gender bias within AI but haven’t told you how to avoid these gender biases. In our next data blog post, we will cover the best practices for machine-learning/AI teams and how they can avoid gender bias.