Artificial intelligence is a highly innovative technology that helps businesses expedite processes and increase efficiency. In turn, companies are predicted to invest $500 billion annually in AI by 2024. However, AI introduces unique concerns into business models. Most concerning as of late is the ways in which AI amplifies pre-existing biases on a large scale. Though trust aware processes that integrate visualization, discovery, analysis, human-centric examination, and monitoring help to mitigate risks associated with AI bias, enterprises must understand what AI fairness is to create a comprehensive plan to institute it.
In his latest article in Techopedia, Andrew Pery outlines the five challenges associated with applying fairness to AI systems:
- The concept of “fair” is interpretable based upon cultural, social, economic, and legal boundaries.
- Fairness and bias are not always the same idea.
- To provide group fairness or individual fairness, businesses must approach their AI strategies differently.
- To ensure fair outcomes, statistical parity must be balanced.
- Fairness is defined by those in power, which can perpetuate pre-existing power hierarchies.