Start adding items to your reading lists:or
Save this item to:
This item has been saved to your reading list.
Are your automated decision making systems harboring bias?
In today's automated world, business leaders have to take measures to ensure their algorithms and AI models used in their organizations are not proliferating bias and discriminating against certain groups of people.
AI bias can cost your organization heavily - posing real risks to your brand, revenue, consumer goodwill, employee retention and potential regulatory scrutiny.
Know your risks with PwC’s Bias Analyzer.
*Source: 2020 PwC AI Predictions
Bias Analyzer is a tech-enabled service that helps you proactively identify, monitor and manage potential risks of bias so you can protect your organization. PwC can help you stay ahead of brand, operational and regulatory risks of algorithmic biases. Leverage the breadth of our subject matter expertise, supported by our Bias Analyzer technology, to analyze your systems for hidden biases.
Bias risks differ for each business, industry and organization. We start by helping you choose metrics and thresholds to suit your unique business needs, risk tolerance and existing governance policies.
A customized dashboard can help you visualize your risks at a glance and see recommended mitigation paths. Proactively uncover instances of bias, addressing the intersectionality of race, age, gender and other individual characteristics.
Analyze the potential business impact of various corrective actions before making recommended changes to your models.
Uncover unintentional AI bias and align your technology tools with your company’s diversity, equity and inclusion policies and values.
Optimize your AI models against possible hidden bias and tap into additional markets and demographics for your consumers and your workforce.
Continually improve your governance processes by monitoring bias thresholds, disparity in your data and other unintentional risks in your AI models.
Analyze your input and output data for deeper insights into potential risks of bias without exposing or providing access to your proprietary models.