Biases in AI

Biases in AI refer to unfairness in how artificial intelligence systems select decisions. These biases can lead to discriminatory outcomes. Here are some examples of biases in AI:

  • Data bias

    : occurs when the training data used to develop an AI system is not representative or is skewed, leading the model to learn, lean, perpetuate and propagate the biases.
  • Algorithmic bias

    : occurs when the design or structure of the AI algorithm itself assigns different weights to different features in a way that may favor one group over another - or even magnify biases. This may happen even if the training data is unbiased.
  • Representation bias

    : occurs when certain groups or perspectives are under-represented or over-represented in the data, distorting the AI system in perceiving, understanding or recognizing of those groups.
  • Feedback Loop bias

    : propagates over time when the AI system's outputs feed or influence future trainings and inadvertently reinforce unfair perceptions.
  • User Interaction bias

    : can be introduced through the user interaction with the system. For example, if users are habitually more likely to click on certain types of content - though other choices are equally valid, the system may learn to highly recommend that content.
  • Ethical and Social biases

    : may arise during the development process, reflecting the beliefs and perspectives of individuals creating the AI system.

Addressing biases in AI ensures fairness, transparency, and accountability. Mitigating biases include diverse and representative dataset collection, algorithmic fairness, continuous monitoring and auditing. Considerations of ethical guidelines and standards should guide all phases of AI development from Requirements Gathering and Analysis, System Design, Implementation, Testing, Deployment, Maintenance and Support and Iterations.