AI in recruitment: risks and opportunities in Ticino
Ticino companies are integrating AI into their 2026 hiring processes. What are the risks of discrimination and the critical issues for candidates? A status update.
Contesto
Artificial intelligence is reshaping employment dynamics in Switzerland and the Canton of Ticino, introducing significant innovations in recruitment processes. According to the Manpower quarterly report published for the second quarter of 2026, employers maintain a cautious stance regarding hiring forecasts, while seeking to optimize operational efficiency through the integration of automated recruitment systems. Although the stated goal is to accelerate talent acquisition and foster flexibility, the use of algorithms is raising profound ethical and regulatory questions. ## The issue of historical data The central issue concerns the very nature of algorithms, which learn based on historical data. Since past databases often reflect corporate structures where professional continuity was the dominant parameter, systems tend to favor linear and uninterrupted profiles. This algorithmic bias penalizes those who have experienced less conventional career paths. As highlighted by Bea Knecht, founder of Zattoo, the risk is that the system, by not finding documented traces of certain paths, ends up ignoring the complexity of working life, especially regarding women. Solange Ghernaouti, a professor at the University of Lausanne, warns that automation risks transforming latent prejudices into indisputable technical decisions. This phenomenon turns AI into an amplifier of inequalities, where discrimination against women and minorities is embedded in the software, making it difficult to identify and counter. Local companies, while seeking to modernize, must therefore manage not only the efficiency of the software but also the responsibility for the decisions made by the systems they use, in a context where human oversight remains, at least at this stage, an indispensable element to p...
Dettagli operativi
The practical implications of AI use in the Ticino job market are far-reaching and directly impact candidates' daily lives. When a company adopts an automated selection system, a CV is no longer reviewed solely by a human resources manager but instead passes through an algorithmic filter that assigns a score based on keywords and temporal continuity. For workers, this means gaps in employment, part-time roles, or career shifts may be interpreted by the software as red flags, leading to automatic exclusion even before the interview stage. ### Before vs After: Comparison Scenarios To grasp the impact of this transition, it's essential to examine how evaluation processes have evolved in recent years. Before the widespread adoption of AI, a human recruiter could contextualize gaps in a CV, understanding family or personal needs. Today, algorithms tend to dismiss profiles based on rigid patterns. Companies have started reporting discrepancies between expectations and outcomes, citing cases of unsuitable profiles recommended by systems or, conversely, the loss of valuable talent due to overly restrictive filters. The table below highlights the key differences observed in selection processes: | Characteristic | Traditional Selection | AI-Driven Selection | | :--- | :--- | :--- | | CV Evaluation | Holistic analysis of career path | Pattern-based analysis | | Screening Time | Longer, high interaction | Shorter, highly automated | | Handling Gaps | Considered (e.g., maternity leave) | Often penalized | | Transparency | Explained by the recruiter | Often opaque (black box) | The main concern raised by HR managers revolves around the quality of profiles proposed by these systems. It’s not uncommon for algorithms, in their quest for the 'ideal' candidate based on historical male...
Punti chiave
Addressing the integration of AI into the labor market requires an approach that goes beyond mere technical expertise. According to experts, it is not enough to simply increase gender representation within scientific teams; a pluralistic vision capable of proposing ethical alternatives is needed. Political scientist Anna Jobin has strongly emphasized the need not to delegate control solely to technicians, calling for a collective responsibility that explicitly integrates the female perspective during the design and monitoring phases of selection systems. ### Recommended procedures and steps for candidates To navigate this scenario, candidates must be aware that their CV might be evaluated by software. Here are some strategic steps to optimize profile presentation: 1. Tailor the CV to the specific keywords of the job posting, as scanning systems look for precise matches. 2. Include clear descriptions of skills acquired even during periods of inactivity or part-time work, transforming 'gaps' into periods of training or the acquisition of transferable skills. 3. Check if the company specifies the use of AI systems in selection, trying to see if direct human contact is possible to supplement the profile presentation. 4. Maintain an updated professional presence on industry digital platforms, which are often analyzed by algorithms as a complementary data source. Companies, for their part, have an obligation to monitor the results produced by algorithms, periodically verifying that no biases or discrimination against specific groups occur. The challenge for 2026 and beyond will not be to eliminate AI, but to learn how to govern it. As suggested by Bea Knecht, the key lies in accessibility: women and candidates in general must understand how these tools work to turn them to...
Punti chiave
[{"q":"Why do selection algorithms often penalize women?","a":"Algorithms are based on historical data that reflect a past labor market where male profiles with linear careers were predominant. Since the system learns from the past, if female career paths—often characterized by breaks or part-time work—have not been historically documented or valued, AI tends not to recognize them as valid, perpetuating or amplifying existing discrimination."},{"q":"What are the main risks reported by companies in Ticino?","a":"Companies mainly report two critical issues: the exclusion of qualified candidates due to overly rigid filters that fail to correctly interpret non-linear career paths, and the recommendation of unsuitable profiles by these systems. In some cases, systematic biases embedded in the software have been identified, making selection decisions opaque and potentially discriminatory."},{"q":"Is it possible to counter algorithmic discrimination?","a":"Experts suggest a collective approach that does not leave the topic of AI solely to technicians. Careful regulation and constant human supervision that incorporates a female perspective are necessary. Furthermore, candidates can optimize their CVs by including relevant keywords, while companies must continuously audit their systems to ensure they do not become amplifiers of inequality."}]
Frequently Asked Questions
- Why do selection algorithms often penalize women?
- Algorithms are based on historical data that reflect a past labor market where male profiles with linear careers were predominant. Since the system learns from the past, if female career paths—often characterized by breaks or part-time work—have not been historically documented or valued, AI tends not to recognize them as valid, perpetuating or amplifying existing discrimination.
- What are the main risks reported by companies in Ticino?
- Companies mainly report two critical issues: the exclusion of qualified candidates due to overly rigid filters that fail to correctly interpret non-linear career paths, and the recommendation of unsuitable profiles by these systems. In some cases, systematic biases embedded in the software have been identified, making selection decisions opaque and potentially discriminatory.
- Is it possible to counter algorithmic discrimination?
- Experts suggest a collective approach that does not leave the topic of AI solely to technicians. Careful regulation and constant human supervision that incorporates a female perspective are necessary. Furthermore, candidates can optimize their CVs by including relevant keywords, while companies must continuously audit their systems to ensure they do not become amplifiers of inequality.