Bias and Fairness: The AI Functions We All Need to Understand
In the modern discourse around AI, we spend a lot of time hearing about the next big product or application, often quickly moving from wave to wave talking about capabilities and applications.
Despite the great benefits of many AI tools, there are also potential risks to the blind acceptance or adherence to these systems’ choices.
AI BIASES GENERALLY EMERGE FROM 3 KEY POINTS:
Data Bias
If the data fed to your system is incomplete or not accurate to the time, you can end up with inaccurate outcomes e.g. historical biases against women in certain fields of employment could lead to the historical bias being re-hashed if all the model uses is historically successful resumes.
Algorithm Bias
Assuming your data is solid, AI models can still sometimes output biased or incorrect results when it interprets this information.
Human Bias
When we use these models, we can unintentionally imprint our own biases on the models and skew the results.
KEY STEPS TO STOP AI BIAS TRANSFERRING INTO REAL WORLD HARM.
Human involvement, responsibility and ability to intervene.
Where it is possible, make sure someone (or ideally a diverse group) is overseeing and responsible for the decisions and processes of AI models. Blind trust in algorithmic outputs can lead to outcomes that cause greater harm than good.
Bias Audits
Implement professional audits of your systems to ensure biases against select groups and social cross sections are discovered early.
Transparency and Education
By building understanding of what bias and failure within your (or any) AI models may look like and clear reporting on how your system was trained. The more those directly using these systems understand how they should function, the less likely they are to blindly accept dysfunctional or biased behaviour.
Dataset Diversification
Ensure the training data being input to your models covers the full spectrum of scenarios and demographic groups that would be reflective of the world your business exists in. Reassess these data sets over time to ensure it still represents an accurate depiction of the world/industry around you and where you want to go.
A TOOL FOR GOOD.
Making sure AI is an effective tool, that provides utility and not harm, requires everyone from back end build to front end users to understand the potential points of failure so we don’t take backwards steps when it comes to protecting our people, clients and potential employees.
If you are looking for greater expertise for your business AI functions, get in contact with us to explore what solution best fits your needs.
