Artificial intelligence bias for dummies
What is artificial intelligence bias?
A bias in AI algorithms results from faulty assumptions made during the algorithm development process or in the training data. In artificial intelligence, a bias manifests in a variety of ways, including ethnicity prejudice, gender bias, and age discrimination. Human prejudice – conscious or unconscious – lurks throughout the development of AI systems. Data scientists, too, can make mistakes from excluding valuable entries to inaccurate labeling to under- and-oversampling. Under sampling, for example, can produce skews in class distribution and cause AI models to completely disregard minority classes.
What are some examples of artificial intelligence bias?
Imagine an algorithm that selects only white female nurses for a multispecialty practice or a breakthrough skin cancer diagnostic that does not work on African Americans. Sounds discriminating? These examples serve to illustrate how uncontrolled artificial intelligence propagates bias. Several examples of AI bias – also known as algorithmic bias – have been found where a machine-learning model gave systematically incorrect results. Data algorithm bias is a reflection of how data are blended, how models are built, and how results are implemented and understood by algorithm developers.
What types of bias are present in artificial intelligence?
AI biases can be classified as algorithmic, data-based, or human, depending on the source of prejudice. AI practitioners and researchers warn against overlooking the human element because it underpins all the other two and often exceeds them. Algorithms typically exhibit the following types of AI bias:
Bias in reporting
These AI biases occur when the training dataset’s frequency of events does not precisely reflect reality. Take the example of a consumer fraud detection tool that consistently underperformed in a remote geographic location, giving an erroneously high fraud score to all customers within the region. Owing to the remoteness of the territory, investigators wanted to ensure that every new claim was fraudulent before traveling to the area.
Bias in selection
This sort of AI bias happens when training data is either unrepresentative or not randomly selected. Take the example of three commercial image recognition products. The technologies were designed to categorize photos of members from Asian and African countries. According to the study, not a single product managed to identify more than one in every three women of color due to a lack of diversity in training data.
Bias in group attribution
A group attribution bias occurs when data teams extrapolate data, which is true for individuals, to entire groups that an individual may or may not be a part of. Recruiting and admissions systems can exhibit this type of bias, which favors individuals who graduated from particular colleges while discriminating against those who did not.
Bias in implicit
This sort of AI bias happens when AI assumptions are made based on human experience that may or may not be generalized. For example, if data scientists have picked up on cultural cues about women being housekeepers, they may struggle to connect women to significant corporate roles despite their explicit commitment to gender equality.
How to fix artificial intelligence bias?
Define and narrow the business problem you're attempting to solve
When you try to solve far too many cases, you typically end up with an unmanageable number of labels spread across an unmanageable number of classes. Narrowly describing an issue will assist you in ensuring that your model is operating properly for the specific reason you designed it.
Structured data collection that allows for differing viewpoints
There are frequently numerous correct opinions or labels for a single data point. Collecting those viewpoints and accounting for valid – often subjective – conflicts will make your model more adaptable.
Recognize your training data
Both academic and commercial datasets may have classes and labels that introduce bias into your algorithms. The better you understand and own your data, the less likely you are to be blindsided by unfavorable labeling. Additionally, make certain that your data appropriately reflects the diversity of your end-users. Is the acquired information adequate to cover all of your potential use cases? If not, you may need to explore different sources.
Assemble a diverse ML team that asks a variety of queries
We all bring different experiences and perspectives to the workplace. People from diverse backgrounds will ask different questions and interact with your model in different ways. This may help you detect problems before your model goes into production.
Consider all your customers
Understand that your end-users will not be the same as you or your staff. Be sympathetic. Recognize your end consumers’ various backgrounds, experiences, and demographics. Avoid AI bias by learning to anticipate how individuals who aren’t like you will engage with your technology and what issues may occur.
Annotate with variety
The more diversified your opinions, the larger the pool of human annotators. This can be quite beneficial in reducing bias both at the first launch and as you continue to retrain your models. One alternative is to use a global population of annotators, who can not only contribute a variety of perspectives but also support a wide range of languages, dialects, and regionally-particular information.
With feedback in mind, test and deploy
Models are rarely static throughout their lives. A common but serious error is releasing your model without allowing end-users to provide input on how the model is performing in the real world. Opening a discussion and feedback forum will help ensure your model is well-received.
Make A Plan To Update Your Model Based On The Feedback
You should assess your model regularly, not just based on client input, but also by having independent persons audit it for changes, edge cases, and instances of bias that you may have overlooked. Make sure you receive feedback from your model and provide feedback of your own to improve its performance, iterating toward greater accuracy.
Conclusion
An AI governance framework should contain bias-free AI techniques and regulations. Completing the framework outlined above will help to promote a better commitment to diversity and, consequently, result in less prejudiced end products.
