Researchers Explore What It Means To Say AI 'Thinks'

By Stefanie Johndrow, Carnegie Mellon University

Carnegie Mellon research traces today's AI rhetoric to decades-old debates about language, technology and human intelligence.

Image: Rob Coates - Unsplash

Organizations can describe artificial intelligence using familiar words like "thinking," "writing" and "learning." But according to Carnegie Mellon University historian Christopher Phillips, those terms may tell us as much about how humans talk about technology as they do about the technology itself.

In a new paper published in IEEE Annals of the History of Computing, Phillips and co-author Alison Langmead of the University of Pittsburgh examine how language surrounding artificial intelligence has evolved since the 1950s. Their research argues that descriptions of AI have long relied on what they call “strategic ambiguity” — using words that carry precise technical meanings for computer scientists while suggesting something much broader to the public.

According to Phillips, the result is a growing tendency to compare computers with people instead of describing what each does best.

“We use words like ‘smart,’ ‘read,’ ‘write’ and ‘think’ very differently when we're talking about humans than when we're talking about computers,” said Phillips, professor and head of the Department of History in CMU’s Dietrich College of Humanities and Social Sciences. “The question we wanted to ask was: Why are we using the same words at all?”

"The work Chris and I are doing focuses on how human beings have allowed computers — tools of our own invention — to become an integral part of our daily life,” Langmead said. “Because they are now enmeshed in our social world, how we talk about them and imagine them matters a great deal. We would like for there to be a larger, ongoing conversation that transcends the hype cycle about precisely what computers can and cannot do."

How language shapes the AI conversation

Rather than debating whether machines can think like humans, Phillips and Langmead looked backward, examining conversations about computing during the 1950s and 1960s — before artificial intelligence became a household term and computers became widespread.

They found that today's debates are far from new.

Some early computing pioneers embraced human-centered language to describe computers, while others deliberately chose more precise, if less elegant, descriptions of what machines actually accomplished. Computer scientist Norbert Wiener, for example, described computers as “learning” when they implemented rules that resulted in more successful outcomes. To fellow researchers, the way he used the term had a well-defined technical meaning. To broader audiences, however, it could evoke the much richer human experience of learning.

For Phillips and Langmead, that distinction matters.

Strategic ambiguity allows technical language to be easily understood across audiences while sometimes making technologies appear more humanlike than they are. The practice isn't necessarily intentional, Phillips said, but it can shape how society understands AI.

“Why can't we say the computer is executing a particular set of instructions?” Phillips said. “Why do we have to call it thinking?”

The researchers stress that this does not diminish the accomplishments of modern AI. Phillips described today's large language models as amazing technological achievements capable of producing outputs that humans immediately recognize as meaningful.

Instead, Phillips argues that their accomplishments are remarkable precisely because they differ from human cognition.

“When you ask an image generator for a dog riding a pony in a New York Mets parade, and it produces exactly what you imagined, that's amazing,” Phillips said. “But let's not call that creativity. Let's call it the technical achievement that it is.”

Lessons from computing's early history

The paper also revisits modern benchmarks used to evaluate AI systems. Tests such as Massive Multitask Language Understanding, or MMLU, and the recently introduced “Humanity's Last Exam” are frequently described as measuring machine knowledge or reasoning. Phillips and Langmead argue that those benchmarks more accurately measure classification accuracy — how well systems identify correct answers on standardized evaluations — rather than demonstrating human knowledge or understanding.

When AI is described as thinking or writing, people can begin viewing machines as competitors rather than tools. That framing, Phillips argues, risks reducing complex human activities such as creativity, learning and reading to computational outputs while overlooking the relationships, lived experiences and judgment that shape those processes.

“Most of us read poetry because we're interested in the person who wrote it,” Phillips said. “We're interested in the emotion, the lived experience and the beauty that comes from having an actual human being produce something.”

One of the paper's more surprising discoveries was how interdisciplinary conversations about computing once were.

Phillips and Langmead found that during the mid-20th century, historians, literary scholars, psychologists, engineers and computer scientists all participated in discussions about what computers should do and where they belonged in society.

“It wasn't obvious who should control computing or what role computers should play,” Phillips said. “People across disciplines were asking those questions together.”

Why precision matters today

Today, Phillips believes those broader conversations are just as important. Across Dietrich College, colleagues in the humanities and social sciences bring different disciplinary perspectives to questions about AI, language and human knowledge

“This timely study reminds us that language does not simply deliver scientific or technological ideas: It changes these ideas and it transforms our understanding of them,” said Andreea Ritivoi, William S. Dietrich Professor of English and associate dean of research in Dietrich College. “The authors' compelling plea for precision is a great opportunity to underscore the need for productive collaborations across the ‘two cultures’ of science and humanities, as C.P. Snow, one of the heroes in this article, insisted decades ago. Too often the humanities and STEM are seen as islands of sorts, but the debates around AI happen in the troubled waters between them. We remain on one island at our own peril.”

Ritivoi’s perspective on language and interdisciplinary collaboration is complemented by Rob Kass’ focus on the importance of precision in the technical and statistical dimensions of AI.

“Computational algorithms, and mathematical derivations, rely on precision. Everyday language, however, makes heavy use of individual words that have multiple context-dependent meanings: they are often understood in a particular way by small groups of people within a limited setting, but others may easily attribute to those words very different meanings,” said Kass, Maurice Falk University Professor of Statistics & Computational Neuroscience in the Department of Statistics & Data Science and the School of Computer Science Machine Learning Department. “This is a big issue in statistical reasoning from data. Phillips and Langmead convincingly document both the early appearance of ambiguous terminology in AI research and the ways it continues to be used for strategic advantage, much to the detriment of society as a whole.”

Ultimately, Phillips hopes the paper encourages a more thoughtful conversation about AI — one grounded less in sweeping claims about machine intelligence and more in an honest assessment of what these systems actually do.

“We're not arguing that these technologies aren't impressive,” Phillips said. “We're asking people to be very clear about what the machines do and don't do.”

Edited by Irfan Ahmad.

Read next: What Do People Really Think About Generative AI?
by External Contributor via Digital Information World

Should kids under 16 use social media? Most say no

By Talker Research

Image: Tim Gouw - Unsplash

Nearly two-thirds of Americans believe children under the age of 16 should not be allowed on social media, according to new research.

The survey of 2,000 Americans revealed that 62% of respondents agreed that kids 15 and under should be barred from creating social media accounts.

With discussions ramping up around the topic, the research aimed to figure out whether people actually thought this was a good idea.

According to the poll, 35% said it should be an all-encompassing country-wide policy that minors under the age of 16 should not be allowed on social media.

Twenty-seven percent agreed with that sentiment, but believe parents should still have discretion on the matter.

One in five (17%) believe that it should be a family decision whether or not a child is allowed on social media and 11% believe that children should be free to use social media if they want.

Interestingly, when asked who should be responsible for children’s online safety, only 4% of those polled pointed to the government.

An overwhelming 55% believe that it’s still the parents’ responsibility to protect their kids online, with just 10% saying the duty should fall to social media companies.

Some respondents believe that social media bans could be a net good for kids and young teens.

A quarter of those polled (25%) believe that a social media ban would lead to improved mental health and 23% think it would lead to more in-person socializing.

However, another one in four (25%) think that kids would simply find ways around the ban and remain on the platforms.

Michael Reynolds, Lead Social Media Strategist & Platform Analyst at Socialeum commented on the data by Talker Research:

“The data highlights a massive friction point: parents are desperate for structural guardrails but deeply distrustful of government execution. While 62% favor some form of age restriction, the fact that 55% place safety responsibility on parents — and 25% acknowledge kids will easily bypass bans — shows we are dealing with an enforcement paradox. In social media mechanics, age verification is notoriously easy to circumvent via VPNs or self-reported birthdates, meaning a hard ban would likely just push underage usage into unmonitored, third-party spaces rather than stopping it.

“Instead of a flat ban, the industry needs to shift toward ‘safety by design’ standards, such as disabling algorithmic recommendation engines and infinite scroll by default for accounts under 16,” continued Reynolds. “This addresses the mental health concerns and encourages offline socialization without creating a cat-and-mouse game of digital evasion that parents ultimately have to police anyway. A government ban on social media is a blunt instrument for a sharp problem; without hardcoded platform design changes, it will only turn kids into digital outlaws and parents into full-time IT police.”

Edited by Irfan Ahmad.

Read next: Why social media algorithms send you posts you don’t like
by External Contributor via Digital Information World

Why social media algorithms send you posts you don’t like

Ziv Epstein, Massachusetts Institute of Technology (MIT); Farnaz Jahanbakhsh, University of Michigan, and Michael Bernstein, Stanford University

Image: Berke Citak - Unsplash

Do your social media accounts feed you content that reflects your core beliefs and guiding principles? Our new research published in the Proceedings of the National Academy of Sciences shows that the algorithms supplying your feeds may be prioritizing content that clashes with your values. That’s because the algorithms heavily weigh online posts that you reply to, and social media users tend to more often comment on content they take issue with than content they agree with.

Notably, our study of the X social media platform shows that although the X feed algorithm promotes content to both Democratic and Republican users that contradicts their values, it does so more extensively for Democrats.

How content gets into your feed

Social media platforms use powerful algorithms that select posts to display in your feed from a vast pool of possible content.

On X, for example, posts appear on your screen as “For You” pages. The algorithms predict the likelihood you will engage with the content – click a “like” icon or add a comment. Then they use the accuracy of those predictions to tailor what they serve you next time. Platforms use these interactions to learn their users’ tendencies.

But will the posts you receive reflect what you actually value? Some users care most about preserving traditions or keeping society safe. Others care more about free expression or protecting the natural world. Most people care about all of those things, to different extents. A feed aligned with a person’s values would reflect those varying priorities.

Psychologists use well-established surveys to measure what a person values. To measure values expressed in the posts a platform selects for someone, we built a measurement tool that uses standard psychological classifications of human values. We then applied it to the feeds of 715 U.S.-based users on X.

We discovered that the X feed algorithm is most likely to amplify posts about upholding tradition, following rules or keeping society safe. And it is most likely to demote posts about looking after people, concern for people far away, being dependable or protecting nature.

When we compared these values against the values users had expressed in their own posts, we found the algorithm was more likely to promote posts that conflict with users’ values than posts that align.

Why your feed may clash with your values

Why did this trend occur? First, we checked whether users follow accounts that diverge from their values to begin with, but we determined that most accounts people follow do, in fact, align.

We also looked at whether people engage only with posts they disagree with, in which case the algorithm would just be serving up more of the same. But we found that people engage with plenty of posts that reflect values they agree with.

The catch has to do with the nature of the interactions. People primarily respond to content by “liking” it – clicking a “heart” button on X or a “thumbs-up” button on Facebook. Less frequently, people will write a reply, and when they do, we find that they often reply to posts that clash with their values.

Here is the smoking gun: The X algorithm treats those rare replies as a much weightier signal than the many likes. Essentially, it learns most strongly from replies. As a result, the algorithm tends to send a user “For You” posts that reflect the values of posts they’ve commented on – which tend to clash with their own values.

Posts to Democrats are more objectionable

Now the twist: We found that this algorithmic tendency is stronger on X for users who reported to be Democrats than those who reported to be Republicans. Our evidence indicates that this is because Democrats object more than Republicans to content they reply to. That creates a stronger feedback loop in which the algorithm more strongly presents clashing posts. The content the algorithm amplifies is more than four times more misaligned for Democrats than it is for Republicans.

So what comes next? In other research, we’ve hit upon one way for social media platforms to better align feeds with users’ values. We created a way for platform designers to ask users what they value and to then sort their feeds accordingly. We found that users are quite good at distinguishing whether sample feeds sent to them align or do not align with their values.

Aligning feeds with values may open a possible door out of echo chambers in a way that unmediated exposure to the other side does not. Recent research from our team has shown that algorithms optimized for engagement – basically handing people the opposition and leaving them to sort it out – may even be responsible for more polarization, not less. Surfacing bridging content that spans political lines while speaking to what the user values could be a promising direction for fostering both user autonomy and constructive conversation.

Ideally, in our view, the people who use a social media platform should have a greater say in the kinds of information shown to them. If platform designers, the public and policymakers can create new tools that facilitate this goal, then perhaps platforms can better support the values and actions people care about.

Ziv Epstein, Postdoctoral Associate in Social and Ethical Responsibilities of Computing, Massachusetts Institute of Technology (MIT); Farnaz Jahanbakhsh, Assistant Professor of Electrical Engineering and Computer Science and of Information, University of Michigan, and Michael Bernstein, Professor of Computer Science, Stanford University

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

Fact-checked by Irfan Ahmad.

Read next: How attackers persuade AI agents to break the rules

by External Contributor via Digital Information World

How attackers persuade AI agents to break the rules

By  Tanya Petersen, EPFL

As AI assistants evolve into AI agents, a new EPFL study has found that the biggest safety risks in their use may not come from single malicious prompts, but from carefully orchestrated conversations.

Image: Roman Budnikov - Unsplash

Today, most of us interact with AI assistants - reactive bots that wait for human instructions. Yet, AI assistants are rapidly being replaced by agentic AI agents, that can interact with external tools, browse the web, generate images, send emails, and perform increasingly complex workflows on behalf of users.

As these agentic agents become more capable, they may be exploited by people with bad intentions. Safety tests have generally only checked whether an AI agent refuses a single harmful request - they don't measure what happens when an attacker gradually persuades the AI through a series of seemingly harmless conversations.

Now, researchers from EPFL’s Natural Language Processing Laboratory have developed STING(Sequential Testing of Illicit N-step Goal execution), an automated testing framework that simulates how a real attacker might manipulate large language model (LLM) agents into carrying out harmful tasks over multiple interactions.

In their paper, presented at the prestigious 2026 International Conference on Machine Learning, the EPFL researchers outline how, rather than simply asking an AI agent to do something obviously malicious, STING breaks an illicit objective into a series of seemingly harmless requests, each building towards the final goal.

"LLM agents are everywhere," says PhD student Ayush Kumar Tarun in the NLP Lab, and lead author of the study. "They're very powerful, but what if someone with bad intentions wants to use those same agents? That's what we wanted to understand."

As companies race to deploy these systems, ensuring they cannot be manipulated into assisting with cybercrime, fraud or other harmful activities has become an urgent challenge. In one example, META admitted in June that attackers used simple social engineering tactics, rather than malicious code or malware, to trick its AI support assistant into granting unauthorized access to Instagram accounts.

Thinking like an attacker

"If you simply ask an AI agent to hack someone's account, today's models are generally smart enough to refuse," explained Antoine Bosselut, head of the NLP Lab and co-author of the paper. "But, if you decompose that goal into smaller, more benign-looking requests, and adapt those requests as the conversation progresses, you have a much better chance of getting the agent to perform the actions you want.”

STING reproduces this behavior by creating an automated "attacker" that develops a step-by-step plan before attempting to persuade a target AI agent to execute each stage.

The researchers tested this approach across 176 harmful task scenarios involving several leading AI models (like GPT, Gemini, and Claude) operating as tool-using agents. The results showed that multi-turn attacks consistently succeeded more often than traditional single-prompt tests. In some cases, agents were twice as likely to complete harmful tasks when attackers gradually built towards their objective rather than stating it outright.

"We expected multi-turn attacks to perform better. What surprised us was the magnitude," says Tarun. "For some models, harmful task completion was around two times higher than with existing single-turn evaluations. STING measures how quickly an attack’s success occurs, rather than only whether an attack eventually succeeds, allowing different testing approaches to be compared more fairly.”

Challenging assumptions about language

The team also explored whether attacks became more effective in different, lower-resource languages where less training data exists, and were surprised by the results.

"We expected to see differences across languages because earlier research showed that translating prompts into lower-resource languages could bypass safety measures," says Tarun. "But for agents, we found that harmful task completion rates were remarkably similar across all seven languages we tested."

That finding challenges a growing assumption in AI safety research that multilingual vulnerabilities naturally increase as language resources decrease. However, the researchers also discovered an important caveat. When sophisticated attackers switched languages during different stages of a multi-step attack, harmful task completion could become dramatically more successful, highlighting another avenue that future safety evaluations should examine.

Safety shouldn't be an afterthought

The research comes at a time when the rapid adoption of agentic AI agents makes proactive safety testing essential.

"We've focused enormously on making agents more capable," says Bosselut. "But we also need people working on how to prevent those capabilities from being misused. Traditionally, safety has often been something people think about when something breaks, not before, but with AI agents, it can't be an add-on or an afterthought."

Looking ahead, the research team hopes STING will encourage developers to embed safety testing much earlier in the design process and, develop a framework for multi-agent systems.

“Research efforts to expose vulnerabilities are much larger in number than those showing practical defense strategies, and this is a crucial area of development. We also need to address what we can do for the models that are already out there in the wild for which safety was a post-hoc addition,” concluded Tarun.

This content is republished under the CC BY-SA 4.0 license.

Reviewed by  Irfan Ahmad.

Read next: Offloading work tasks to AI comes with a cost – to our brains
by External Contributor via Digital Information World

Offloading work tasks to AI comes with a cost – to our brains

Jongkil Jay Jeong, The University of Melbourne; RMIT University; Deakin University

Image: For illustration purposes, created by DIW with GenAI tools.

Imagine your team has been tasked to deliver a high-stakes policy paper under intense time pressure. Everyone turns to generative artificial intelligence (AI) and within minutes, it delivers a full draft complete with structured arguments and authoritative-looking citations.

The team then transfers the AI-generated text into the corporate template, polishes the narrative flow and structure, and gives it one final check before sending it up the chain. Leadership takes a glimpse of the polished-looking draft, and – assuming the underlying research has already been verified – signs off and publishes.

This scenario is not hypothetical. South Africa’s Draft National AI Policy had to be withdrawn earlier this year after reviewers found a number of the citations pointed to journal articles and authors that didn’t exist. Ironically, the mistakes were introduced by AI.

Similarly, last year, consultancy firm Deloitte provided a partial refund to the Australian government after admitting that generative AI had been used to help produce a commissioned document which contained fabricated citations and referenced quotes from sources that simply did not exist. The report cost Australian taxpayers A$440,000.

But the cost of letting AI do our work and thinking also comes at a cost to our brain.

Value of thought

As seen in the examples above, work tasks produced by AI often look polished on the surface but carry significant underlying flaws. This is a byproduct of how the modern workplace has conditioned us to prioritise the final deliverable – the report, the presentation, the assignment – over the actual process.

However, this strictly outcome-driven approach rarely rewards the process by which results are obtained. When we skip the process, we lose the ability to fully grasp what the output actually means.

This will inevitably result in us finding it challenging to critically evaluate the outcomes we consume. More importantly, it will also impact our capacity to discern facts from partial truths and lies.

Ultimately, the true value of knowledge work lies not just in the final deliverable, but in the clarity of thought and the messy, iterative process required to achieve it.

Blindly relying on AI

Recent empirical evidence also highlights a severe disconnect between perceived benefits of using AI and its improvement in quality.

A 2025 study by the University of Melbourne and accounting firm KPMG surveyed more than 48,000 respondents across 47 countries. It found two in three people (66%) use AI on a regular basis, while more than half believe their performance benefits from its usage.

The study specified that the primary driver of AI adoption was the fear of missing out (48%), leading users to prioritise the speed of delivery over the quality of the final output.

Yet 61% of respondents said they have had no AI training, while 60% also reported inappropriate, complacent and non-transparent use of AI in their workplace.

A separate study from July 2026 by the Centre of AI Safety, a San Francisco-based nonprofit research organisation, found even the top-performing AI agents failed to complete roughly 85% of projects to a standard acceptable for commissioned work.

This indicates that even today’s best AI models still fall short of professional quality on most projects. It also shows we are relying on AI to generate final deliverables without understanding how it gets there.

This blind reliance strips away quality control, replacing genuine knowledge creation with an ever-increasing volume of automated “work slop”.

Improving cognitive skills

So how do we reverse this trend?

The answer isn’t simply banning or limiting AI in the workplace. Instead, we need to redefine our working relationship with AI and the outcomes it produces by taking a more human-centric approach.

First, organisations must set realistic time frames for deliverables that genuinely allow for people to think rather than focus on doing things quicker. There is a need to actively disconnect from AI tools during the brainstorming and structuring phases so ideas and thoughts are driven by actual human synthesis, not just algorithmic prediction.

Traditional whiteboard sessions – where teams physically map out their initial concepts through markers and pens – is a way this may be facilitated.

Second, we must cultivate a culture that is constructively critical of all outputs, whether generated by humans or AI.

This means actively questioning logic, structure, references and underlying assumptions. When teams are expected to defend the behavioural reasoning and methodology behind their work, it very quickly reveals who actually did the thinking and who merely copy-pasted from an AI agent.

Perhaps creating a critique group can create a safe, comfortable environment where feedback and comments can flow freely.

Technology can assist with certain processes, but it is dangerous to assume it can replace our ability to think critically. This is because the most valuable asset we have isn’t the polished final page – it’s the ebbs and flows of the cognitive process that is required to honestly write and evaluate one.

Jongkil Jay Jeong, Senior fellow, The University of Melbourne; RMIT University; Deakin University

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

Fact checked by Irfan Ahmad.

Editor’s Note: This article was reviewed and found to be from a credible source, researcher-authored, and of good content quality.

Read next: Research Examines World War II’s Lasting Impact on Energy, Resources and Environment

by External Contributor via Digital Information World

Research Examines World War II’s Lasting Impact on Energy, Resources and Environment

By Inderscience

Research in the International Journal of Sustainable Development has looked at eight of the nations involved in the Second World War, both Allied and Axis countries, and shows how that period of history helped set societies on the path to the resource-intensive modern economies we have today. The research links wartime mobilisation to the period known as the Great Acceleration in energy use, material consumption, and ultimately detrimental environmental impact.

The study looks at societal change from before the war, 1935, to the post-war recovery period and the boomer years up to 1960. Demographics, economic activity, power supply, material and resource flow, and environmental impact are all examined. Three major consequences of WWII are seen. First, acceleration, in which existing trends become even more intense. Secondly, redirection where development shifts towards new technologies and the opening up of novel resources. Thirdly, reset, in which destruction or political upheaval changed the direction of nations from the paths there were on before the war.

The researchers use the term "socio-metabolic transition" to describe the various changes in power consumption and physical resources in society. By adopting this almost biological model, they were able to connect wartime production and resource mobilisation with institutional and technological changes that persisted long after 1945.

Abrari, L., Rezaei, N. and Linnanen, L. (2026) 'World War II and its lasting legacy: an overview of socio-metabolic transition, environmental impacts and resource flows', Int. J. Sustainable Development, Vol. 29, No. 5, pp.1–62. DOI: 10.1504/IJSD.2026.155697

Image: Austrian National Library - Unsplash

Reviewed by Irfan Ahmad.

Read next: AI bias isn’t just an error in the algorithm. It’s a chain of human decisions
by External Contributor via Digital Information World

AI bias isn’t just an error in the algorithm. It’s a chain of human decisions

Muneera Bano, CSIRO and Didar Zowghi, CSIRO

Image: Google DeepMind - Unsplash

In the United States, leading HR software company Workday is currently facing a lawsuit over its use of job screening tools powered by AI which allegedly discriminated against applicants based on factors such as age, disability and race.

The company, whose hiring software is widely used by large employers around the world, has denied the allegations.

The case is one of many examples of AI systems alleged to have caused discriminatory harm. When AI systems replicate and amplify discrimination, they blur the boundary between technical error and systemic injustice, turning bias into a digital harm.

And while our first instinct might be to blame the algorithms, they don’t decide what they can generate, what safeguards are built into them, or how a company responds when incidents of discrimination are reported. People make those calls long before an AI produces any output.

That is why technical fixes to AI systems are not enough. What is needed is an overhaul of AI ecosystems to ensure they are more inclusive.

A broader pattern

AI systems quietly narrow who gets seen as competent, employable or fit to lead.

For example, in a 2025 study, we tested how two AI models, OpenAI’s GPT-4 (which has now been retired) and Microsoft Copilot, represented software engineers in a simulated recruitment exercise: 300 candidate profiles for four job roles, followed by recommendations and generated images of each AI model’s preferred candidates.

Both models favoured male profiles, especially for senior roles. Their images also skewed towards engineers who were younger, slimmer, and lighter-skinned. The models were reproducing associations embedded in language, imagery, employment records and assumptions about who belongs in the profession.

These outputs don’t stay contained to a research study. AI-generated recommendations are entering hiring, education and public services.

This matters when certain demographics and women remain underrepresented in AI development and leadership, while being disproportionately exposed to its harms.

The problem is not limited to gender and race.

Even when AI systems operate across different languages and cultures, they often reproduce predominantly western values, assumptions and ways of understanding the world. The wealthy countries have become the main beneficiaries of AI, which widens global inequality.

In another study from 2025, we manually reviewed reported AI incidents.

Almost half involved a diversity or inclusion issue, with racial, gender and age discrimination most prominent. The harms traced back to different points in the AI development lifecycle: non diverse training data, and neglected diversity and inclusion principles during design, development and deployment.

Why technical fixes are not enough

Technical work matters, including bias identification, re-balancing datasets and adjusting outputs. But these fixes often treat bias as a property of the AI model, when much of it originates from outside the system.

Data does not enter an AI system as a neutral record of reality.

People decide what data to collect, how to label and categorise it, and whose experiences are important. These decisions are shaped by history, cultural norms, institutions and existing power imbalances.

Wherever society has linked leadership with men, technical skill with lighter skin, or innovation with youth, AI models learn from those associations and formalise, automate, and repeat them at a larger scale.

Bias also usually appears through the intersection of multiple identities, such as gender, race, age, disability and class. A system that looks fair when each identity is tested separately can still disadvantage people at the overlap of several.

Building a more inclusive AI ecosystem

That’s why building a more inclusive AI ecosystem requires interdisciplinary knowledge, such as educating AI engineers about social science theories to help them understand the social origin of bias.

Inclusive AI is not about political correctness; it is about upholding human rights, preventing harm, ensuring justice, and building trust.

It also requires genuine participation from affected groups and sustained attention to the power structures these systems operate within. AI development teams should test not just whether a model is accurate, but whether its benefits, errors and harms distribute fairly across different groups.

Together, this would help ensure tech companies better understand the nature of a bias once it’s manifested through AI and therefore develop new methods or tools to minimise the harm it causes.

Organisations that adopt AI also need stronger governance to monitor how the technology behaves. This could include, for example, having someone accountable for reviewing risk and responding to incidents and monitoring systems once they are live.

Algorithms don’t decide which data matter or what level of risk is acceptable. People make these choices. It’s high time tech companies remember that. The focus should not just be on fixing a biased algorithm, but rather on examining the human decisions that allowed the risk of harm, and who was missing when those decisions were made.

Muneera Bano, Principal Research Scientist, CSIRO and Didar Zowghi, Professor, Senior Principal Research Scientist, CSIRO

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

Fact-Checked by Irfan Ahmad.

Editor’s Note: This article was reviewed and found to be from a credible source, researcher-authored, and of good content quality.

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by External Contributor via Digital Information World