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As part of Solutions Review’s Expert Insights Series\u2014a collection of contributed articles written by industry experts in enterprise software categories\u2014Bernd Ruecker, the co-founder and chief technologist at Camunda<\/a>, explains how companies can begin to root out the bias in their automated processes.<\/strong><\/em><\/p>\n Done well, process automation is a valuable tool<\/a>. But what if the automated processes perpetuate existing biases that surfaced only occasionally in manual processes? How can you avoid bias sneaking into your algorithms behind process automation?<\/span>\u00a0<\/span><\/p>\n Let’s disentangle this important question using the example of issuing credit cards. This is a process that can be visualized using the standardized Business Process Model and Notation (BPMN):<\/span>\u00a0<\/span><\/p>\n Source: Camunda<\/em><\/p><\/div>\n There are two great things about BPMN. First, this model can be directly executed by process orchestration tools. Those tools will then coordinate all credit card requests and integrate the right systems at the right time.\u00a0<\/span>\u00a0<\/span><\/p>\n Second, the visual model helps to understand the process. As this is the executed model, the visual is not wishful thinking but IT reality. And to eliminate bias, transparency and visibility for various stakeholders are essential. In this example, we can assume the process will <\/span>not<\/span><\/i> contain bias. At the same time, we can suspect that specific decisions within single tasks\u2014like the customer rating or the decision about the credit line\u2014can contain a level of bias.<\/span>\u00a0<\/span><\/p>\n This is a common pattern: Bias is typically found in the decision logic but not so much in the process control flow. <\/span>While this sounds obvious, it offers clues about how bias can creep into such decisions. A typical customer journey looks like this: In early maturity stages around automation, decisions are made manually. A human looks at all the customer data and makes a decision about the credit limit based on specific rules enriched with their gut feeling (called experience). This gut feeling might introduce bias in the first place. Humans possess the capacity to exhibit<\/span> 180 different types of bias<\/span><\/a>.\u00a0<\/span>\u00a0<\/span><\/p>\n When automating a decision like this, many companies are looking into machine learning (ML) and artificial intelligence (AI). In doing so, they will use historical decision data to train the machine learning model. While this is a complicated task, it’s easy to understand how things will turn out: If you teach something with biased data, it will learn that bias.<\/span>\u00a0<\/span><\/p>\n It can create <\/span>business <\/span><\/a>r<\/span><\/a>ule biases<\/span><\/a> that can perpetuate themselves in business practices. If a bank trains systems to accept one set of documents and reject similar documents, it can discriminate against people applying for loans. If a recruiter creates rules with tight qualification criteria, a firm could favor one group of applicants over another.\u00a0\u00a0\u00a0 <\/span>\u00a0<\/span><\/p>\n![\"\"](\"https:\/\/solutionsreview.com\/business-process-management\/files\/2023\/01\/Expert-Insights-Badge.png\"){kind=icon}
Process automation is gaining traction across industries because enterprises see the many benefits it can provide in an increasingly competitive business environment. It speeds up innovation, improves productivity, and frees employees to take on more complex strategic jobs. One in three IT professionals says their process automation investments at least<\/span> paid for themselves within a year<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n![\"\"](\"https:\/\/solutionsreview.com\/business-process-management\/files\/2023\/01\/Camunda-1.png\")
Bias is typically found in decision logic<\/span><\/b>\u00a0<\/span><\/h3>\n
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