In a new series of interviews, we're meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the European Conference on Artificial Intelligence (ECAI-2025) . In the second interview of the series, we caught up with Frida Hartman to find out how her PhD is going so far, and plans for the next steps in her investigations. Frida, along with co-authors Mario Mirabile and Michele Dusi, was also the winner of the ECAI-2025 Diversity & Inclusion Competition, for work entitled . This award was presented at the closing ceremony of the conference. Could start by giving us a quick introduction to yourself and the topic that you're working on?

Michael Scott, the protagonist from the US version of The Office, is using an AI recruiter to hire a receptionist. The text-based system asks applicants five questions that delve into how they responded to past work situations, including dealing with difficult colleagues and juggling competing work demands. Potential employees type their answers into a chat-style program that resembles a responsive help desk. The real – and unnerving – power of AI then kicks in, sending a score and traits profile to the employer, and a personality report to the applicant. This demonstration, by Melbourne-based startup Sapia.ai,

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Artificial intelligence (AI) and machine learning (ML) engineers are quickly becoming crucial for any company looking to secure a spot in the data-driven future. In reality, we have already entered this world, meaning any organization not utilizing the technology in some way is at a disadvantage. The world's major tech companies are dedicating entire departments to AI research, or completely buying out AI startups to implement into their organizations. But it's not just companies like Google, Facebook, and Apple that are applying AI to their data. Companies of every size are using AI to innovate their business models.

There are several challenges that exist for AI systems. In this edition of the newsletter I discuss some of the key challenges including shortage of talent, high costs for the required talent, data and machine learning algorithms, compute infrastructure costs, AI bias, and the lack of transparent AI systems. AI doesn't come cheap, there is a huge cost associated with having the required personnel to build and maintain AI systems. A traditional AI team has one or multiple data scientists and DevOps or AI development engineers. Data scientists are well-versed and experts in the field of math and statistics and are required to work with the underlying machine learning and deep learning algorithms.

A few years back, Amazon had to stop using their AI (Artificial Intelligence) recruitment tool because human bias bled into machine learning. As it turns out, Amazon's AI was more likely to pick men over women because they used resumes to teach their models; and most of the resumes they used were from men. This led to the AI filtering out resumes that mentioned women in any capacity, essentially eliminating women from the hiring pool, essentially sabotaging the entire point of the tool. There is a lesson to be learned from this: Machine learning can learn our own biases and apply it to the real world. Machine learning really is up-and-coming, and we are still learning about how we can teach these machines to replicate our own decisions.

When considering beginning your AI project, you're likely inclined to have a blend of excitement and concern. Stunning, this can be astonishing. All the examples of success stories, the number of sales grow, income development etc. In any case, on the other hand, imagine a scenario where it turns out badly. How might you alleviate the risk of wasting money and time on something that simply isn't practical in any way?

Historical data can be an authoritative source of intel for a company looking to make smarter and faster decisions. When efficiencies are possible, it's best to use the tools available to achieve them. Today's recruiters need a more efficient workflow, which is driving the advancements being made in hiring tools, including the introduction of predictive analytics. In recent years, predictive analytics have become an essential tool for any recruiter looking to acquire top talent. Predictive analytics (PA) is the use of historical data to make better decisions for the future using artificial intelligence (AI) and machine learning (ML).

In late 2018, Amazon discontinued the use of their AI-based recruitment system because they found that it was biased against women. According to sources close to the matter, the tool gave low ratings to resumes with the terms "woman" or "women's" in applications for technical roles, and went as far as downgrading applicants from two all-women's colleges. This problem is not new. In 2003, the National Bureau of Economic Growth (NBER) conducted an experiment to track the presence of racial bias in hiring. In the test, they sent out two sets of fictitious resumes with identical information about education and experience.

In late 2018, Amazon discontinued the use of their AI-based recruitment system because they found that it was biased against women. According to sources close to the matter, the tool gave low ratings to resumes with the terms "woman" or "women's" in applications for technical roles, and went as far as downgrading applicants from two all-women's colleges. This problem is not new. In 2003, the National Bureau of Economic Growth (NBER) conducted an experiment to track the presence of racial bias in hiring. In the test, they sent out two sets of fictitious resumes with identical information about education and experience.