Predictive process monitoring enables organizations to proactively react and intervene in running instances of a business process. Given an incomplete process instance, predictions about the outcome, next activity, or remaining time are created. This is done by powerful machine learning models, which have shown impressive predictive performance. However, the data-driven nature of these models makes them susceptible to finding unfair, biased, or unethical patterns in the data. Such patterns lead to biased predictions based on so-called sensitive attributes, such as the gender or age of process participants. Previous work has identified this problem and offered solutions that mitigate biases by removing sensitive attributes entirely from the process instance. However, sensitive attributes can be used both fairly and unfairly in the same process instance. For example, during a medical process, treatment decisions could be based on gender, while the decision to accept a patient should not be based on gender. This paper proposes a novel, model-agnostic approach for identifying and rectifying biased decisions in predictive business process monitoring models, even when the same sensitive attribute is used both fairly and unfairly. The proposed approach uses a human-in-the-loop approach to differentiate between fair and unfair decisions through simple alterations on a decision tree model distilled from the original prediction model. Our results show that the proposed approach achieves a promising tradeoff between fairness and accuracy in the presence of biased data. All source code and data are publicly available at https://doi.org/10.5281/zenodo.15387576.

Art is subjective and everyone has their own opinion about it. When I saw the expressionist painting Blue Poles, by Jackson Pollock, I was reminded of the famous quote by Rudyard Kipling, "It's clever, but is it Art?" Pollock's piece looks like paint messily spilled onto a drop sheet protecting the floor. The debate of what constitutes art has a long history that will probably never be settled, there is no definitive definition of art. Similarly, there is no broadly accepted objective definition for the quality of a piece of art, with the closest definition being from Orson Welles, "I don't know anything about art but I know what I like." Similarly, people recognize unfair bias when they see it, but it is quite difficult to create a single objective definition.

Art is subjective and everyone has their own opinion about it. When I saw the expressionist painting Blue Poles, by Jackson Pollock, I was reminded of the famous quote by Rudyard Kipling, "It's clever, but is it Art?" Pollock's piece looks like paint messily spilled onto a drop sheet protecting the floor. The debate of what constitutes art has a long history that will probably never be settled, there is no definitive definition of art. Similarly, there is no broadly accepted objective definition for the quality of a piece of art, with the closest definition being from Orson Welles, "I don't know anything about art but I know what I like." Similarly, people recognize unfair bias when they see it, but it is quite difficult to create a single objective definition.

Artificial Intelligence (AI) has become one of the biggest drivers of technological change, impacting industries and creating entirely new opportunities. From an engineering standpoint, AI is just a more advanced form of data engineering. Most good AI projects function more like muddy pickup trucks than spotless race cars -- they are a workhorse technology that humbly makes a production line 5% safer or movie recommendations a little more on point. However, more so than many other technologies, it is very, very easy for a well-intentioned AI practitioner to inadvertently do harm when they set out to do good. AI has the power to amplify unfair biases, making innate biases exponentially more harmful.

Art is subjective and everyone has their own opinion about it. When I saw the expressionist painting Blue Poles, by Jackson Pollock, I was reminded of the famous quote by Rudyard Kipling, "It's clever, but is it Art?" Pollock's piece looks like paint messily spilled onto a drop sheet protecting the floor. The debate of what constitutes art has a long history that will probably never be settled, there is no definitive definition of art. Similarly, there is no broadly accepted objective definition for the quality of a piece of art, with the closest definition being from Orson Welles, "I don't know anything about art but I know what I like." Similarly, people recognize unfair bias when they see it, but it is quite difficult to create a single objective definition.

Google has announced that its image recognition AI will no longer identify people in images as a man or a woman, reports Business Insider. The change was revealed in an email to developers who use the company's Cloud Vision API that makes it easy for apps and services to identify objects in images. In the email, Google said it wasn't possible to detect a person's true gender based simply on the clothes they were wearing. But Google also said that they were dropping gender labels for another reason: they could create or reinforce biases. Given that a person's gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias.

"Women and people of color are fighting many battles in the tech world and in the fast-growing world of artificial intelligence." When companies build diverse teams, they will usually have more diverse AI models that can help them overcome bias. One way to beat unfair bias is to say no to black box models that don't provide human-friendly explainable AI.

Fairness can be measured at group levels or at the individual level. Do you wish to ensure that on average you don't discriminate against a protected group or apply the protection to each and every individual? For example, you may hire female job applicants with the same average probability that you hire male applicants. That would achieve group fairness. However, you may be biased by giving junior roles with higher probability to females and senior jobs with higher probability to males. Achieving individual fairness is more difficult than achieving group fairness.

The growing use of artificial intelligence in sensitive areas, including for hiring, criminal justice, and healthcare, has stirred a debate about bias and fairness. Yet human decision making in these and other domains can also be flawed, shaped by individual and societal biases that are often unconscious. Will AI's decisions be less biased than human ones? Or will AI make these problems worse? Will AI's decisions be less biased than human ones?

As artificial intelligence and machine learning become the new industry norm, tech giants and service providers across the world are riding the emerging tech wave. However, with its applicability to enhance services, AI and machine learning have become ubiquitous for any technological advancements. As the world acknowledges the inevitability of AI and ML, the conversation has, however, shifted to its ethics, with governments and lawmakers bringing out stringent policies regarding the applicability of the technology. In Europe, countries like the UK and France have put ethics at the core of AI, while laying out stronger compliance rules for tech giants to adhere to. Taking note of the latest developments, tech giants like Google and Facebook, among many other companies, have brought out their ethical policies regarding deployment of AI and ML within their organisations.