At the Automation Bias event, held as part of Illuminate Adelaide 2026, an interactive HP*ATM installation by the studio ENESS was presented. This machine, named Human Psyche AI Teller Machine, draws attention to how humanity interacts with artificial intelligence by analyzing people's willingness to share personal information with intelligent systems that claim to understand a person's personality.
Visitors first decide whether to interact with the machine or walk past it. This choice is part of the machine's study regarding consent and curiosity. Those who decide to continue go through a sequence of steps, including facial analysis, palm scanning, and button pressing. The system uses the collected data to create a poetic personalized profile, which is presented in the format of a horoscope.
Although the reading mimics the language of traditional fortune-telling, it is based on the interpretation and collection of multiple types of personal data. Through this installation, ENESS aims not to criticize AI, but to prompt visitors to reflect on their relationship with this technology. By transforming an outdated ATM into an AI oracle, the installation uses a playful form of interaction to raise sharp questions, such as: 'Why do we so readily believe the machine's interpretation of who we are?' and 'When does a confident algorithm start to seem more reliable to us than our own intuition?'
The installation's design includes placing the ATM inside a massive inflatable shell decorated with nostalgic graphic elements, such as a landline telephone. This contrast between outdated technologies and the modern AI boom serves as a reminder that any technology, no matter how revolutionary it once seemed, eventually becomes a relic.
When submitting resumes and tailoring documents for different opportunities, candidates may notice that the responses received are often similar. An investigation conducted by Stanford University suggests that this similarity may not be a coincidence.
The researchers analyzed millions of applications and concluded that various companies may employ artificial intelligence systems that replicate the same acceptance and rejection criteria. The research, titled Algorithmic Monocultures in Hiring, examined about 4 million applications submitted by over 3.4 million individuals to 156 companies across 11 economic sectors.
A crucial factor identified was that all these organizations were using algorithms provided by the same vendor. This detail allowed for the identification of the phenomenon known as 'algorithmic monoculture,' a term inspired by agriculture, where vast areas are dedicated to a single type of crop. When many companies adopt similar tools, the probability increases that they will evaluate applicants following essentially the same logic, which applies to both successes and failures inherent in the models.
Another relevant finding concerns similar candidate profiles. According to the study, individuals with analogous characteristics tend to receive consistent evaluations, even when competing for positions at different corporations. The primary results reveal that approximately 10% of candidates participating in four selection processes are rejected in all of them. Additionally, about 4% of those who apply for ten positions suffer ten consecutive rejections.
The rejections occur more frequently than expected in independently made decisions. It is notable that many resumes are eliminated before even being reviewed by a human recruiter. To confirm whether this behavior was random, the researchers compared the data with a theoretical baseline and previous studies on recruitment without algorithmic centralization, demonstrating that successive rejections reflect a common pattern across different selection processes.
According to the simulations conducted, continuing to submit resumes is still advantageous. The study points out that increasing the volume of applications improves the chances of obtaining an opportunity, although this benefit decreases when companies use identical systems. In a scenario where decisions are autonomous, about ten applications would be sufficient to achieve a high probability of receiving at least one positive recommendation. However, when processes are mediated by centralized platforms, this number rises to approximately 25 applications to ensure a 99.9% probability.
The authors also issued a warning about the concentration of the technology market focused on recruitment. Since few vendors serve many companies, any existing biases or limitations can spread rapidly. Furthermore, the lack of transparency in these platforms hinders independent research and complicates the understanding of how such tools impact employment access, especially since, for many candidates, this entire process occurs without them knowing that an algorithm performed the initial resume screening.
