Every enterprise has blind spots that impact their business because of a lack of real-time visibility into people, assets, and workflows. The majority of the tasks still go unmonitored and thus improving productivity is a high priority for all enterprises. AI and computer vision have enabled many enterprises to gain actionable insights through a mechanism called visual process automation (VPA). Enterprises are looking to embedded computer vision to provide the visual intelligence required for their IoT applications. The challenge with most VPA solutions is that they are inherently biased.

Recent analyses of public domain ML models have revealed that many of them are inherently biased. This bias originates from the image and video datasets that were used to originally train the model. The lack of access to volumes of quality data is the reason why many enterprises are seeing biased results from their computer vision models. Enterprises are looking for solutions to enable their ML solutions to be more accurate, robust, and unbiased.

In this talk, Dori.AI introduces a structured methodology to address the issue of data and model bias that may be inherent in the ML models you are building, providing insights into techniques that help to answer the following questions:

• How do you prepare an unbiased dataset?
• What metrics are used to analyze data bias for CV datasets?
• How do you rebalance datasets?
• Can introducing data bias remove model bias?
• How can synthetic data or data augmentation be used to enhance existing datasets?
• What explainability metrics are used to analyze model bias for CV models?
• How do you properly benchmark to reveal data and model bias?

Speaker: Dr. Nitin Gupta, Founder of Dori AI