The Problems of Outdated and Incomplete Data
Building Unbiased Machines
The work of creating unbiased systems differs from traditional software quality practices. Those practices focused on looking for errors in software code and then fixing the bugs. With AI, a company’s quality measurements must shift from examining lines of software code to examining the quality of the data and the algorithms they use to make meaning of that data.
Successful implementations must include three quality-control steps:
1. Ensuring that data sets are complete and well understood. This requires a company to set up a mature data management capability, review the data inputs for the AI system, and vet the sources of that data for accuracy and completeness.
2. Employing experts to validate the use of data by AI algorithms. Skilled people trained in specific domains need to ensure that systems using AI (or machine learning or natural language processing) are producing high-quality outputs. For example, that can mean having a customer experience expert review the results of a system that produces automated responses to customer requests.
3. Adding new data. To improve, a successful AI system requires fresh data on an ongoing basis. Updating the data continuously makes for more accurate outputs. Additionally, more data sources give the AI algorithm more evidence from which to draw insights, improving the quality of its work. Therefore, it is important to take advantage of new technologies—such as visually enabled systems that can interpret text and images, as well as augmented and virtual reality systems that can replicate physical environments—that can supply new data to AI systems.
When combined, these efforts will increase the probability that an automated system will provide accurate results. They will become self-improving, which guards against the risk of biased outcomes based on incorrect or incomplete data.
How Unbiased Machines Reduce Risk
This article looks at two areas where we have worked to create and sustain unbiased and self-improving machines: systems that automate operations monitoring and systems that automate labor-intensive, regulated processes. In both areas, machines can detect anomalies in patterns of data more quickly and accurately than humans.
Now, AI and machine-learning applied to monitoring systems makes the work easier at both government agencies and private-sector enterprises.