The most popular and comprehensive Open Source ECM platform
Let us know more
Auditing Algorithms: Protecting Privacy and Minimizing Biases
As organizations move forward with digital transformation and increasingly automate business processes, computer algorithms are taking on greater importance.
Algorithms are precise in what they do, but they are simply follow the logic that was used to build them, and when that logic is flawed, and when the algorithm is applied at scale hundreds or thousands of times, decisions and actions can be taken which are skewed towards unintended biases or inappropriate actions.
Algorithms have been accused of violating privacy, unfairly treating customers in applications and decisions, and the targeting of customers with unwanted solicitations. Algorithms based on artificial intelligence (AI), deep learning (DL), and machine learning (ML) are particularly being targeted.
As a result, organizations are turning to algorithm audits to try to uncover biases and unfair practices that are built into algorithms. Regulations are being proposed to require algorithm auditing.
A recent example in the news was the software used by the company HireVue when helping big companies like Walmart and Goldman Sachs interview candidates. Hirevue’s algorithms that analyzed candidate interviews were criticized as being biased.
Tech columnist James Kobielus wrote that “CIOs need to get ahead of this trend by establishing internal practices focused on algorithm auditing, accounting, and transparency. Organizations in every industry should be prepared to respond to growing demands that they audit the complete set of business rules and AI/DL/ML models that their developers have encoded into any processes that impact customers, employees, and other stakeholders.”
