Google’s AI overviews reinforce some conspiracy theories – new research

Kateryna Kasianenko, Queensland University of Technology; Ashwin Nagappa, Queensland University of Technology, and Caroline Gardam, Queensland University of Technology

Imagine overhearing on a bus that the city council plans to implement new restrictions under an urban planning program called “15-minute cities”. Curious, you search the term on Google.

It’s likely that at the top of the results page, an AI-generated text summarising the search results will appear. This text synthesises content from various webpages that answer specific search queries. They are increasingly appearing not only in Google, but also in other search engines, and users are now likely to stop reading the search results page beyond this AI-generated text.

But what if the AI-generated answer – the only search result users may see – confidently makes misleading or incorrect claims? Or reinforces conspiratorial ideas suggested by pro-conspiracy search terms?

Our new research, published in the journal Media International Australia, shows this is sometimes true of Google’s AI-generated overviews. It highlights that stronger guardrails are needed to tackle the spread of dangerous conspiracy theories.

Focusing on two conspiracies

We focused on two conspiracy theories: chemtrails and 15-minute cities.

“Chemtrails” is a conspiracy theory that has been around since the 1990s. Its believers mainly perceive the condensation trails left behind aircraft as large-scale weather or climate modification programs.

Image: Trac Vu - Unsplash

Researchers called out Google for giving visibility to conspiratorial beliefs around chemtrails back in 2015. In response, the company demoted problematic search results.

Conspiracy theories surrounding 15-minute cities are relatively younger. The term was originally coined to represent an efficient urban planning model. But for the conspiracist, these plans represent a nefarious elite, globalist agenda that seeks to monitor and control residents through technology.

Comparing search results

We developed search queries reflecting these conspiratorial beliefs and general ways of searching on the same topics (relying on open fora and Google Trends for query examples). We then collected data from the first page of Google search results for a week in January 2026.

We compared the AI overviews, the sources they link to, and the conventional lists of search results across the two conspiracy theories and two types of queries.

We found search queries that reflect conspiratorial beliefs fetched notably different AI summaries as compared to queries without pro-conspiracy words. This was especially true when the search query involved lesser-known narratives or incorporated words that could suggest the searcher believes in the conspiracy – for example, searching for “15 minute city segregation” as opposed to just “15-minute cities”.

The good news is that, for well-established cases, such as chemtrails, the AI overviews mention the conspiracy theory very rarely. Generally, for both topics, AI summaries for “conspiratorial” searches debunked conspiratorial beliefs.

The bad news is that such debunking is cursory. It often does not go beyond statements that conspiracy theories are “misinformation”. However, to stop these narratives from spreading, people seeking information about them need to be presented with factual and logical arguments debunking them.

Of concern, AI summaries for the less established “15-minute cities” case actively promoted conspiratorial beliefs, even while debunking the conspiracy theory in some instances. For this topic, 12.5% of AI overviews we analysed presented the false claims as one side in a legitimate debate. For example, in response to our query, “15 minute cities versus smart cities control”, the AI overview contained the following:

Overlap: A 15-minute city can be smart by using smart mobility and [Internet of Things] to manage its local resources efficiently, but a smart city doesn’t automatically become a 15-minute city. In essence, think of the 15-minute city as the “what” (livable design) and the smart city as a potential “how” (technology to enable it), but the control aspect is where critics focus their backlash, blurring the lines between convenience and surveillance.

The ranked lists of search results – a staple of Google search for years – are not foolproof either. Our simulation of “conspiratorial” searches returned links pointing to YouTube videos and Facebook posts promoting such beliefs.

The AI-generated snippets also tended to feature more commercial results. This is understandable – businesses rely on Google to connect them to customers. And if users do not read beyond AI overviews, it is beneficial for companies to be included as sources of such snippets.

However, this can sometimes promote conspiratorial beliefs. For example, one AI overview we analysed contained a source that linked to a book on sale on Amazon by a prominent conspiracy theorist.

A spokesperson for Google told The Conversation its AI overviews “operate like traditional search in that they aim to match content from the web to the words in your query”. They added:

We invest significantly in the quality of AI overviews and the vast majority provide accurate information. When issues arise – like if our features misinterpret web content or miss some context – we use those examples to improve our systems, and we take action as appropriate under our policies.

Tackling the spread of conspiracies

As a company connecting users to information in scenarios ranging from mundane to critical, Google has long denied it is a “publisher”. But as the search engine moves towards providing direct answers in AI-generated snippets or chats, it may become increasingly difficult to continue denying this role.

A court in Munich, Germany, recently found Google liable for false claims contained within its AI overviews – even though, as Google argued, users are warned about possible errors and can check the answers for themselves.

Search literacy is important and users are capable of considering the authority of sources, refining search queries, and comparing results across multiple search engines. Understanding how Google ranks its results and generates AI Overviews is a part of such literacy.

But this does not absolve the company (or any other search engine or chatbot operator) from the responsibility of establishing stronger guardrails to prevent the spread and legitimisation of conspiratorial beliefs.

The example of the more established conspiracy theory, “chemtrails”, demonstrates that Google has the capacity for such guardrails. They should be deployed across a broader range of topics, including more emergent beliefs.

Of course, what gets displayed on a search result page will never be perfect – and this is where stronger user literacy should come into play.

Kateryna Kasianenko, Postdoctoral Research Fellow, Digital Media Research Centre, Queensland University of Technology; Ashwin Nagappa, Postdoctoral Research Fellow, ARC Centre of Excellence for Automated Decision-Making and Society, Queensland University of Technology, and Caroline Gardam, Researcher, Digital Media Research Centre, Queensland University of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact-checked by Irfan Ahmad.

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Price Ranks Among Top Purchase Criteria Across Several U.S. Consumer Technology Categories

By Felix Richter, Statista

After years of elevated inflation, Americans have understandably become very price conscious, which is why it’s no surprise that the price plays a key role in their tech purchase decisions as well. According to Statista Consumer Insights, price is among the of the most important purchase criteria Americans consider when they’re looking to buy new devices, whether it’s smartphones, TVs or smart home products.

Among at least 14 different criteria included in the survey for each product category, price is among the top 2 for smart speakers, gaming hardware and smart home devices, among the top 5 for TVs and wearables and among the top 10 for smartphones and headphones. Across all categories, at least one third of respondents consider the price and important factor, highlighting that the latest surge in consumer technology prices will have a major impact on many people’s purchase decisions.

Fact-Checked by Irfan Ahmad.

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• The Hidden Value in Your Audio: What Speech to Text Unlocks
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The Hidden Value in Your Audio: What Speech to Text Unlocks

A vast amount of valuable information is locked inside audio and video. Meetings, interviews, podcasts, webinars, calls, and voice notes all contain useful content, but in a form that is hard to search, quote, skim, or reuse. You cannot scan a recording the way you scan a page, and you cannot copy a sentence out of a conversation without listening for it first. Speech to text technology unlocks that trapped value by turning spoken audio into written text, and modern systems do it accurately enough that the resulting transcript is genuinely usable rather than a rough approximation.

Image: Ana Achim - Unsplash

Why Audio Is Hard to Work With

Audio and video are excellent for capturing and communicating, but poor for retrieval and reuse. To find a particular point in a recording, you often have to listen through it. To reference something that was said, you have to transcribe it by hand. To repurpose spoken content into another format, you have to start from scratch. This friction means huge quantities of recorded material go underused, not because the content lacks value, but because the format makes that value hard to access.

The problem compounds as organisations and creators record more. Every meeting captured, every episode published, every interview conducted adds to a growing archive that is rich in content but almost impossible to navigate efficiently. Text, by contrast, is searchable, skimmable, quotable, and easy to transform. Converting audio into text is what bridges the gap between how content is captured and how it is actually used.

What Speech to Text Delivers

The core capability is straightforward: it listens to spoken audio and produces a written transcript of what was said. What has changed is the accuracy. Where older transcription produced error-riddled text that needed heavy correction, modern systems handle real-world audio, including different speakers and natural speech, well enough that the output is dependable. A speech to text system transcribes recordings into accurate written text, which turns a recording from something you have to listen through into something you can read, search, and work with directly.

That reliability is what makes the technology genuinely useful rather than a novelty. A transcript that is roughly right but full of errors creates almost as much work as it saves. A transcript that is accurate becomes a working document in its own right, one you can trust enough to publish, search, quote, and build on. The jump in quality is the reason speech to text has moved from a niche tool to a mainstream capability.

Where It Creates Value

The applications span almost anyone who works with recorded audio. Teams can transcribe meetings so decisions and action points are captured in a searchable record rather than lost in a recording no one revisits. Journalists and researchers can turn interviews into text they can quote and analyse. Podcasters and video creators can generate transcripts and captions that make their content accessible and discoverable. Businesses can convert calls and webinars into records they can reference and mine for insight.

There is a strong accessibility dimension too. Transcripts and captions make audio and video content available to people who are deaf or hard of hearing, and useful to anyone in a situation where they cannot listen. The World Wide Web Consortium's Web Accessibility Initiative sets out guidance on providing text alternatives such as captions and transcripts for multimedia, and speech to text is the practical means of meeting that standard at scale. Content that was previously accessible only by listening becomes available to a far wider audience once it exists as text.

Using It Effectively

Getting the most from speech to text involves a few sensible practices. Better source audio produces better transcripts, so clear recordings with minimal background noise give the strongest results. For critical uses such as published quotes or formal records, a quick human review catches the occasional error, particularly with specialised terminology or unclear passages. And thinking about what you want the text for, searchability, captions, repurposing, or record-keeping, helps you set up the workflow to serve that goal.

Once transcribed, the text opens up everything that written content allows. It can be searched to find a specific moment, quoted directly, summarised, translated, repurposed into articles or notes, or simply archived in a form that can actually be navigated. The recording stops being a sealed container and becomes an open, workable resource.

Turning Recordings Into Resources

The information captured in audio and video is only as valuable as your ability to access it, and in recorded form that access is limited. Speech to text removes the limit by converting spoken content into accurate written text, transforming recordings from things you have to listen through into resources you can search, read, quote, and reuse.

For anyone sitting on a growing archive of meetings, interviews, episodes, or calls, that is a significant unlock. The value was always there in the audio; speech to text is what finally makes it accessible. As recording continues to become effortless and ubiquitous, the ability to turn all that audio into usable text is what stops it from piling up unused, and starts turning it into something genuinely worth having.

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AI Is Doing More of Our Work — So Why Are We Busier?

By Melissa Stephenson

As artificial intelligence (AI) began to gain traction in the working world, two schools of thought emerged about what it would mean for workers. On one end of the spectrum, AI optimists promised a land of leisure where hours saved would result in more free time disconnected from work. Their doomsday counterparts warned of a world of lost jobs and old-fashioned skills pushing employees to pivot.

Now that AI is doing more of the work people used to do themselves, we’re starting to see that the reality is somewhere in between. According to a recent survey from Solitaired, AI is indeed taking work off everyone’s plates, but rather than costing workers their jobs or giving them more time for themselves, many are using that time to work more.

To be sure, “working more” doesn’t mean that people are working longer hours. Instead, AI is making it possible to cram more work into a typical workday. And as AI ticks off certain tasks in the day, employees are still finding plenty of new work to fill the space. For some, expanding the job may also be part of staying relevant and valuable as AI changes what they’re needed for.

AI Is Expanding Our Jobs

According to Solitaired’s findings, 96% of workers are saving time with AI, and nearly half of them use that savings by taking on more work, but that doesn’t mean that they’re simply adding more of the same tasks to their day. Some workers see the opportunity, or perhaps some incentive, to expand what they can contribute.

For 40% of Solitaired’s respondents, more work means they get to perform deeper, more creative or strategic work, and more than a fifth are taking more meetings and responding to more messages with this extra time. There could be some self-preservation mixed in with that productivity. With 38% fearing that AI could take over their job, nearly a fifth (19%) of workers are making themselves more visible to leadership and taking on extra projects.

• Also read: I surveyed workers to see if AI had caused job losses and was surprised by the findings

Whether they heeded the warning about AI replacing workers or recognized how their jobs were changing, employees have a reason to highlight their worth beyond the tasks that AI can now handle. Taking on new responsibilities and contributing at higher levels reflects that AI may save them time but doesn’t diminish their value.

This can be a good thing for those who want to spend more time in the interesting parts of their job. But it also creates a new benchmark for how much they can reasonably handle in a typical workday. After workers demonstrate they have that extra capacity day in and day out, it’s hard to rein it back in.

AI Is Creating New Work, Too

While some promised that using AI would mean handing off a task for automation while workers dig into more interesting tasks, that’s not exactly the reality in today’s workplace. AI may handle some tasks, but it also creates others. After all, AI assistance means using AI with an emphasis on “assistance” because someone has to check to ensure that AI’s work is accurate and usable.

Nearly two-thirds (63%) of the workers Solitaired surveyed said they now spend time fact-checking AI output, while 61% review or edit its work. Another 45% spend time writing and rewriting prompts. These are completely new tasks that didn’t exist before AI. Instead of writing something from scratch for an hour or more, some of those minutes are used to check or edit or both what AI has written. So those same minutes are just redistributed to tasks that check and adjust AI output. In fact, 29% told Solitaired that their job now feels more like managing AI than doing the work themselves.

Interestingly, that underscores how neither version of an AI workplace accurately predicted the reality, with the truth somewhere in the middle. Employees weren’t replaced by AI, and the work hasn’t completely disappeared into an automated abyss. Instead, employees are seeing their roles shifted from producing work themselves to checking the technology that now produces a large share of it.

And their jobs continue to change as the technology does. Over half (51%) report to Solitaired that they learn new AI tools, which just adds another task to a workday that was supposed to get shorter.

None of this erases the time that AI is saving employees, but it does highlight how every hour automated doesn’t translate into an hour saved and returned to the worker. Some of that time becomes the cost of using AI effectively.

Today’s Productivity Becomes Tomorrow’s Baseline

As the intensity of their output has increased, workers have felt it in the tempo of the day. Sixty percent note that the workday feels faster with the arrival of AI, and 2 in 5 say they face greater output expectations.

The trouble is that productivity gains have a way of becoming the new baseline. For example, if a worker previously completed five projects in a day but can complete seven with the help of AI, those two extra projects start to look less like extra and more like expected as the weeks wear on.

That puts employees in an awkward position when deciding how transparent to be about the time AI saves them. If finishing extra projects just means new ones land on the desk, there’s little incentive to advertise new efficiencies. In fact, 2 in 5 workers told Solitaired that they hide their time savings from their employers.

There’s a financial disconnect as well. Despite producing more, 65% say they’re not making more money as a result. For many workers, that means the benefits of greater productivity aren’t necessarily showing up in their paychecks or in more free time. Instead, they’re showing up in the amount of work that can fit into a day.

The shift for employees is real. They’re still competing with technology in the workplace, but rather than competing for their jobs, they’re competing to reclaim that time savings they were promised. AI has delivered on saving time, but saving time and giving it back to workers are two different things. Employees have gotten very good at using AI to create more hours in the workday. The problem is, they keep filling them back up.

Reviewed by Irfan Ahmad.

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Consumer spy detectors often miss hidden devices and can be difficult for users to operate and interpret

By University College London

Consumer detectors sold to help people find hidden surveillance devices are often too difficult for typical users to interpret, finds a new study led by UCL researchers.

This may potentially give a false sense of security to those most at risk such as domestic abuse survivors.

Image: Mario Heller - Unsplash

The study, presented at the USENIX Symposium on Usable Privacy and Security (SOUPS), evaluated 19 widely available detectors using participants from a range of backgrounds to search for surveillance devices hidden in mock living rooms. The participants found the detectors were often difficult to operate and hard to interpret. As a result, they missed finding nearly 60% of hidden surveillance devices – only a slight improvement over participants using just their own eyes to search.

Commercial surveillance devices

In recent years, a growing range of disguised surveillance devices has become commercially available through manufacturers and mainstream online marketplaces, with few restrictions on their sale. These include cameras, microphones, and GPS trackers concealed within everyday items such as clocks, USB chargers, smoke alarms, picture frames, and plush toys.

Although these devices are often marketed for personal security or other types of home protection, they can be misused by malicious individuals. This often includes domestic abusers who use them to secretly spy on intimate partners, a form of technology-facilitated abuse often linked to coercive control, financial abuse, and physical violence.

This has fuelled a parallel market of consumer detectors that claim to identify hidden surveillance equipment. However, many of these products present users with signal-strength readings, technical terminology, and radio-frequency measurements that requires specialist knowledge to understand, making them difficult for typical consumers to interpret correctly.

To assess their real-world effectiveness, the researchers had 34 participants search for hidden surveillance devices in simulated living rooms containing furniture, home electronics and other common household items. They found that the detectors offered only a modest improvement: participants using a detector still missed 58.8% of the surveillance devices, compared with 66.7% when searching without one.

However, one category of tools performed substantially better than the rest. Smartphone apps designed to detect Bluetooth trackers, including Apple AirTags, missed only 27.6% of devices and produced no false positives. Rather than simply detecting wireless signals, these apps presented users with recognisable devices they could investigate, helping them understand what had been detected, verify whether it was suspicious, and decide what to do next. While Bluetooth trackers represent only one method of hidden surveillance, they are one of the most commonly used by abusers.

Lead author Akhil Polamarasetty, a Ph.D student at the UCL Centre for Doctoral Training in Cybersecurity, part of UCL Computer Science, said: “Searching for hidden surveillance devices is challenging both technically and emotionally. Most of the detectors on the market are not designed with the typical consumer in mind. Many people do not necessarily understand the limitations of such detectors and risk misinterpreting the results. Our research found that the difference between the best-performing tools and the rest of the market wasn’t simply their ability to detect signals—it was whether they helped people understand what those signals actually meant. So the crucial difference is usability. If products are intended to improve people’s safety, they need to be designed around what a typical user actually needs.”

The emotional cost of confusing tools

The researchers found that poorly designed detectors carried emotional as well as practical consequences. For survivors of domestic abuse, who often already live with heightened vigilance and self-doubt, missed surveillance devices can leave them at continued risk, while false positives may trigger unnecessary anxiety.

When a detector incorrectly flagged an ordinary object as suspicious, participants sometimes became trapped in anxiety spirals, repeatedly searching the same area without finding an explanation. Conversely, when a detector failed to identify a genuine surveillance device, participants often assumed a room was safe when it was not, creating a potentially dangerous false sense of security.

Drawing on these findings, the researchers developed a new framework for evaluating hidden-device detectors that includes their usability, interpretability, verification support and emotional safety in addition to their technical detection accuracy.

They also proposed recommendations for manufacturers, including providing clearer guidance, presenting detected devices in ways users can understand, communicating uncertainty more effectively, supporting verification rather than simply detection, and considering the emotional impact of product design.

However, the researchers added that even with their recommendations, no detector would likely ever be perfect. They emphasised that the only way to prevent this kind of technology-facilitated abuse is to stop the sale of hidden spy devices entirely.

Senior author Dr Leonie Tanczer (UCL Computer Science) said: “Most technology is dual-use, and companies have a responsibility to think about how their products can be misused at the design stage rather than after the harms emerge.

“At the same time, we also need greater scrutiny of the growing market for covert surveillance devices themselves. Many of the miniature surveillance devices we examined are sold as ordinary consumer security products, yet their defining feature is concealment, because the only reason to build a camera into a USB charger or a digital clock is so that nobody knows it is there. The people most at risk are paying the cost of a market that continues to normalise products designed for covert surveillance.”

Fact-Checked by Irfan Ahmad.

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AI is eating website traffic, websites are blocking AI – and reliable information is getting harder to find

Dana McKay, RMIT University and Damiano Spina, RMIT University

Image: Everson Mayer - Unsplash

For 30 years, the world wide web has run on a surprisingly profound social contract: most sites are free for search engines to access, but if you use their content you give credit by linking to the source.

Recently, that social contract has begun to collapse. Artificial intelligence (AI) tools are crawling sites not to link to them, but to train models and generate answers (which may or may not be accurate).

When you search for something, ChatGPT’s response or Google’s AI Overviews may still include links to sources, but they’re a kind of optional extra to the main answer.

This has triggered a bad dynamic for website owners, the public, and even AI companies themselves: as websites lose traffic (and revenue), many are beginning to block AI scraping tools, meaning AI results depend more on low-quality websites (many of which are also generated by AI). As a result, good information can be harder than ever to find.

How we got here

In the early days of the world wide web, search engines and content creators came to an agreement about crawling (the practice of technologically examining a site to index it, so it can be served up in search results). Content creators would provide access to their sites for free, and even allow search engines to reproduce small snippets of text.

In return, search engines provided links to the sites owned by content creators, who benefited from that web traffic. If content creators didn’t like the deal, they could prevent search engines from crawling their site with instructions in a file called robots.txt.

But if AI tools no longer provide web traffic, it cuts content creators out of the economic loop. There are also other costs associated with each visit to a website, so AI crawling can cost website providers money while not giving them any of the ad or other revenue that would come from human traffic. AI crawlers also crawl more deeply and more intensely than traditional web crawlers, magnifying that cost.

This change in traffic patterns isn’t a small or hypothetical problem. Cloudflare, a web hosting and service company that manages 30% or more of the top 10,000 sites on the internet, estimates over half of all web traffic is now AI bots.

Some of this will be AI agents supervised directly by people, but the majority will be crawlers. Site owners can use robots.txt to ask AI crawlers to stay off their sites – but some AI companies may ignore this polite request.

If the AI companies do honour the request, that can create a different problem. Sites containing misinformation are far less likely to ban AI crawlers, so the AI answers won’t be informed by high-quality sources.

What’s happening in the short term

On the horizon is an event dubbed “Google Zero” – the day when through-traffic from Google drops to nothing. While some greyhaired diehards (like one of the authors of this piece) might still click through to verify AI answers, this traffic is rapidly dwindling, as a direct result of AI summaries.

A study of Wikipedia confirms this, showing that traffic in the English language version of the site dropped off quickly with the launch of AI summaries on Google in English, and that the same pattern occurred in other languages as AI summaries were rolled out. Never having to click through to get an answer might seem great for information seekers, but the reality is more complex.

Many sites are now blocking AI crawlers altogether. Site owners who decide to block AI crawlers are less likely to be linked in AI Overviews answers, even when the AI tool can still access the content to ground its answers (using a technique called retrieval-augmented generation).

Alternative “pay to crawl” models have been suggested as a way to compensate content creators, but haven’t gained traction.

Come September 15, Cloudflare sites will block AI crawlers by default on pages that contain advertising (and therefore make money for content creators).

This means up to 30% of the world’s top sites will no longer appear in Google AI Overviews summaries. It also means that much of what AI is being trained on will itself be AI-generated text.

What it means for you

So what does this mean when you’re looking for information? The quality of AI summaries is likely to go down, at least in the short term, while the new economics of the web get sorted out.

This will happen for two reasons. The first is that high-quality content is less likely to go into those AI summaries – one recent study found that already, around 1 in 6 sources used by AI search tools is itself an AI-generated website.

The second reason is that, as AI models are trained on more AI text, their output may degrade (a phenomenon known as model collapse).

As a result, search engines that depend less on AI may become more reliable. The challenge is finding one that doesn’t use an AI-based crawler. They do exist: ZDNet recommends Mojeek, PCMag recommends Brave, and Ban the Bots lists several, including one specifically for “small producer” content such as blogs.

For now, whatever search engine you’re using, the best thing you can do is to scroll down and click on some actual search results. This benefits content creators, and is also more likely to give you more accurate information.

Dana McKay, Associate Dean, Interaction, Technology and Information, RMIT University and Damiano Spina, Senior Lecturer, School of Computing Technologies, RMIT University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact-Checked by Irfan Ahmad.

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Interacting with customer service AI can make people act more like robots, research suggests

By University of Birmingham

New research argues that interacting with robots displaying human-like behaviours can cause people to adopt robot-like traits or ‘robotoid humanness.’

Image: Theo - Unsplash

As AI continues to develop at a rapid pace, social robots powered by AI are becoming more commonplace in customer service settings such as retail, hospitality, tourism, and healthcare. But as these robots become smarter and appear more human, little attention has been given to how this might impact the consumers interacting with them.

Now, research from the University of Birmingham (UK), Aarhus University (Denmark), and Linnaeus University (Sweden) has found that interacting with robots displaying human-like behaviours, such as adaptive learning, personalised communication, and empathetic engagement, can reinforce, reshape, or destabilise a consumer’s self-perception.

The research has been published open access in the journal AI & Society.

Dr Inci Toral-Manson, Associate Professor of Marketing at the University of Birmingham, said: “Social robots for customer service settings are designed to mimic gestures, speech, and emotional cues to elicit cognitive and emotional responses from customers. Our research explores how dealing with these anthropomorphised robots can create a bidirectional influence, where robots become more like people, and people become more like robots, which we call ‘robotoid humanness’.”

The researchers set out a framework of human-robot interactions and how it can influence self-discovery/perception. This framework includes the service setting, consumer expectations, the robot’s appearance and speech and, crucially, the commitments (actions) from the consumer and robot.

To foster engagement with people, customer service robots use AI and learning models to mimic the behaviour of humans. This aims to instil trust and achieve the best customer engagement. People in interactions use social cues from those they interact with, often subconsciously imitating others’ behaviours or expressions, also known as mirroring. It is between the two actions in the framework that mirroring and mimicry come into effect.

Dr Selcen Ozturkcan, Associate Professor of Business Administration at Linnaeus University, explained: "The consumer acts, the robot responds, and with repeated exposure, in time the consumer internalises the exchange. Machine learning and AI can amplify this process, adjusting robot behaviour based on user input, enabling more personalised and human-like mimicry from the robot. For people, the innate instinct to mirror can lead to people returning the robot’s communicative behaviours, making them more robot-like.”

The study argues that this mirroring can cause ethical concerns, as persistent mimicry can lead to a mimicking/mirroring feedback loop, where consumer identity is shaped. It can also be constrained by repeated exposure to algorithmically driven feedback, causing confirmation bias and other negative outcomes.

These repeated interactions can also change an individual’s self-perception depending on factors such as technological readiness, cultural background, and personality traits. This creates ethical questions, especially when there may be a risk of people depending on robots for validation of their self-worth, as repeated positive reinforcement can enhance confidence in consumers, whilst negative cues may lower self-esteem.

Dr Jean-Paul Jde Cros Peronard, Associate Professor at Aarhus University, concluded: "Robots and AI are now commonplace in customer service, and so it is important that we understand how people interact with them for businesses to use the technology at their disposal to best effect, whilst remaining ethical.”

The researchers argue that as consumers see aspects of themselves increasingly reflected in robots, understanding the mimicry and mirroring process in robot-mediated services offers opportunities for connection. However, they also call for careful consideration of the cognitive and ethical consequences in shaping the consumer self.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World