Feedback and calibration tools

- Okay, so let's talk about how tools and technology can inadvertently introduce bias into the evaluation process. In a recent study, the University of Southern California's Information Sciences Institute looked at two AI databases to see if their data was fair. They found that almost 39% of the facts used by AI were bias. This included positive and negative biases. As organizations increasingly rely on tech and AI, it will be important to identify where bias may occur to ensure fair and equitable performance evaluation process. Some common sources of bias in these systems include the design of evaluation criteria, data collection methods, and algorithms. During the design of evaluation criteria, this is where diverse perspectives are a big help. Seek perspectives from different work groups and stakeholders to build comprehensive success measures. Through diverse voices, you'll have a higher chance of finding bias. This same principle applies to data collection methods. Ensure your…