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By Rodrigo Santos Andrade
Can AI feel safer than human advice for consumers who face discrimination?
visibility comment0 event October 14, 2026 timer 11:17
A study reveals how discriminated consumers are more likely to turn to AI-based recommendations rather than human advice to avoid judgement or what could be uncomfortable conversations.
KEY TAKEAWAYS
A study highlights that consumers who have experienced discrimination are more likely to turn to AI-based recommendations rather than human advice.
The fear of being judged or feeling embarrassed can lead people to use AI.
If AI systems are themselves biased, they risk exacerbating the very discrimination that consumers were trying to escape.
What should you cook for dinner on a budget of 20 euros when you have a guest with a gluten intolerance? A shopper might ask a shop assistant for advice or consult, for example, Hopla, the AI-powered shopping assistant launched by French supermarket chain Carrefour. Behind this very ordinary everyday choice lies a broader question: when faced with a recommendation, who do we trust more? A human being or an AI?
Recent research – on decision-making processes, the impact of errors, etc. – has highlighted a clear and well-established trend: people tend to prefer other people (as opposed to AI systems). This phenomenon, known as “algorithm aversion”, has been widely documented, even when algorithms objectively demonstrate better performance.
However, this preference is not universal. Our research highlights that it can be reversed in a common context: in facing discrimination on platforms and offline in services ranging from car dealership to financial consultancy. More specifically, consumers who are victims of discrimination – particularly in public places – are significantly more likely to turn to AI-based recommendations rather than human advice even in a subsequent domain fully independent of the discrimination domain!
What is the emotional mechanism behind this shift? Embarrassment. When people feel
socially judged or treated unfairly because of an irrelevant personal attribute, they seek refuge in systems perceived as “neutral and free from bias”.
How does this mechanism work? What does this mean for the organisations concerned? And what does it reveal about the hidden drivers behind the adoption of AI?
Implicit discrimination
Discrimination in consumer contexts is by no means rare. Studies show that certain groups face less favourable conditions than others when it comes to similar services, for example in terms of access to credit or insurance. Beyond their economic consequences, these experiences have significant psychological repercussions. They increase stress, undermine the sense of fairness, and influence future behaviour.
It is important to clarify what discrimination means in a market context. A market, in the broadest sense, refers to any setting where consumers interact with service providers, be it a car dealership, a restaurant, a bank or a healthcare platform. Within these spaces, differential treatment is not inherently discriminatory. For example, a VIP customer receiving premium service is treated differently on the basis of a relevant commercial criterion.
Discrimination, however, occurs when the factor used to differentiate is irrelevant and illegitimate, such as skin colour, ethnic origin, or gender. What makes it particularly insidious is that it can be hidden, implicit or even unintentional, whilst causing real psychological harm to those who suffer it.
An AI system ‘devoid of emotions’
As part of a field studywe conducted under real-world conditions, “agents” – who were in fact people involved in the research project (i.e. confederates) – assessed loan applications on a university campus using almost identical questionnaires; almost because one version of the questionnaire included an additional question asking participants to self-assess their skin colour (an element by design to evoke discriminatory feelings).
All applicants were subsequently rejected; those who had been asked the question about skin colour reported feeling that they had been discriminated against (phase 1). During the follow-up data collection, carried out at a later date, all participants were offered the choice between a human assessor and an AI assessor (phase 2).
A significantly higher proportion of those who felt they had been discriminated against in the first phase (compared with those who did not feel they had been discriminated against in that same phase) chose, in the second phase, to be assessed by the AI system, perceived to be “devoid of emotions and biases”.
Feeling of embarrassment
Consumers who have experienced discrimination appear significantly more likely to rely on AI-based recommendations than on human advice. In other words, aversion to algorithms can turn into a preference for them, also known as “algorithm appreciation”. This shift can be explained by a specific psychological mechanism: embarrassment. This social emotion arises when individuals feel they are being judged negatively by others.
In our experiments, participants exposed to discriminatory situations reported higher levels of embarrassment, and this emotion directly influenced their subsequent choices. The more embarrassed they felt, the more they sought to limit human interactions.
In this context, AI appears to be a safer alternative.
#AI is perceived as being more neutral
Compared to human beings, algorithmic systems are perceived as more neutral, more
objective and less prone to bias or prejudice.
The reality is more complex. Algorithms can be biased, depending on the data and training they receive. Yet many people continue to regard them as neutral and objective. In this context, it is these perceptions that matter most. Opting for AI allows consumers to reduce the risk of feeling that they are once again being judged or treated unfairly. This effect depends on the context in which decisions are made. We find that it is particularly pronounced in public situations, when choices are visible to others. By contrast, when decisions are made in private, this effect (i.e. preference for AI systems vs. human ones) weakens considerably.
Previous social experiments
These findings shed new light on the adoption of AI. They suggest that the use of automated systems is driven not only by considerations of efficiency or performance, but also by psychological needs shaped by past social experiences.
For businesses, the implications are significant. In sectors where interactions may be perceived as sensitive – such as banking, healthcare, or customer service in general – offering AI-based alternatives can help some consumers feel more at ease. For these individuals, AI is not merely a technological tool; it becomes a means of escaping social judgement and regaining control following negative experiences.
At the same time, these findings come with an important caveat: if AI systems are
themselves biased – trained on biased data or designed without regard for equity – they risk reproducing, or even amplifying, the very discrimination that consumers were trying to escape. Even more seriously, this type of discrimination often goes unnoticed because it is hidden and difficult to detect. The roll-out of AI as a “safe” alternative only makes sense if that safety is real, and not merely perceived.
As AI becomes increasingly integrated into everyday life, understanding these mechanisms will be essential for designing more inclusive and adaptive customer experiences.
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Les auteurs ne travaillent pas, ne conseillent pas, ne possèdent pas de parts, ne reçoivent pas de fonds d’une organisation qui pourrait tirer profit de cet article, et n’ont déclaré aucune autre affiliation que leur organisme de recherche.
Tags: Academic Journalism, The Conversation, The Conversation Academic News
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