But AI, as arguably the most powerful tool humanity has created in the last few years, is about to break that model open. Not with smarter software or faster grading, but by forcing us to ask: “What is the purpose of education in a world where machines could teach?”

At AI News, rather than speculate about distant futures or lean on product announcements and edtech deals, we started a conversation – with an AI. We asked it what it sees when it looks at the classroom, the teacher, and the learner.

What follows is a distilled version of that exchange, given here not as a technical analysis, but as a provocation.

The system cracks

Education is under pressure worldwide: Teachers are overworked, students are disengaged, and curricula feel outdated in a changing world. Into this comes AI – not as a patch or plug-in, but as a potential accelerant.

Our opening prompt: “What roles might an AI play in education?“

The answer was wide-ranging:

• Personalised learning pathways
• Intelligent tutoring systems
• Administrative efficiency
• Language translation and accessibility tools
• Behavioural and emotional recognition
• Scalable, always-available content delivery

These are features of an education system, its nuts and bolts. But what about meaning and ethics?

Flawed by design?

One concern kept resurfacing: bias.

We asked the AI: “If you’re trained on the internet – and the internet is the output of biased, flawed human thought – doesn’t that mean your responses are equally flawed?”

The AI acknowledged the logic. Bias is inherited. Inaccuracies, distortions, and blind spots all travel from teacher to pupil. What an AI learns, it learns from us, and it can reproduce our worst habits at vast scale.

But we weren’t interested in letting human teachers off the hook either. So we asked: “Isn’t bias true of human educators too?”

The AI agreed: human teachers are also shaped by the limitations of their training, culture, and experience. Both systems – AI and human – are imperfect. But only humans can reflect and care.

That led us to a deeper question: if both AI and human can reproduce bias, why use AI at all?

Why use AI in education?

The AI outlined what it felt were its clear advantages, which seemed to be systemic, rather than revolutionary. The aspect of personalised learning intrigued us – after all, doing things fast and at scale is what software and computers are good at.

We asked: “How much data is needed to personalise learning effectively?“

The answer: it varies. But at scale, it could require gigabytes or even terabytes of student data – performance, preferences, feedback, and longitudinal tracking over years.

Which raises its own question: “What do we trade in terms of privacy for that precision?”

A personalised or fragmented future?

Putting aside the issue of whether we’re happy with student data being codified and ingested, if every student were to receive a tailored lesson plan, what happens to the shared experience of learning?

Education has always been more than information. It’s about dialogue, debate, discomfort, empathy, and encounters with other minds, not just mirrored algorithms. AI can tailor a curriculum, but it can’t recreate the unpredictable alchemy of a classroom.

We risk mistaking customisation for connection.

“I use ChatGPT to provide more context […] to plan, structure and compose my essays.” – James, 17, Ottawa, Canada.

The teacher reimagined

Where does this leave the teacher?

In the AI’s view: liberated. Freed from repetitive tasks and administrative overload, the teacher is able to spend more time guiding, mentoring, and cultivating important thinking.

But this requires a shift in mindset – from delivering knowledge to curating wisdom. In broad terms, from part-time administrator, part-time teacher, to in-classroom collaborator.

AI won’t replace teachers, but it might reveal which parts of the teaching job were never the most important.

“The main way I use ChatGPT is to either help with ideas for when I am planning an essay, or to reinforce understanding when revising.” – Emily, 16, Eastbourne College, UK.

What we teach next

So, what do we want students to learn?

In an AI-rich world, important thinking, ethical reasoning, and emotional intelligence rise in value. Ironically, the more intelligent our machines become, the more we’ll need to double down on what makes us human.

Perhaps the ultimate lesson isn’t in what AI can teach us – but in what it can’t, or what it shouldn’t even try.

Conclusion

The future of education won’t be built by AI alone. The is our opportunity to modernise classrooms, and to reimagine them. Not to fear the machine, but to ask the bigger question: “What is learning in a world where all knowledge is available?”

Whatever the answer is – that’s how we should be teaching next.

(Image source: “Large lecture college classes” by Kevin Dooley is licensed under CC BY 2.0)

See also: AI in education: Balancing promises and pitfalls

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The findings come from the State of AI Bias report by DataRobot in collaboration with the World Economic Forum and global academic leaders. The report involved responses from over 350 organisations across industries.

Kay Firth-Butterfield, Head of AI and Machine Learning at the World Economic Forum, said: 

“DataRobot’s research shows what many in the artificial intelligence field have long-known to be true: the line of what is and is not ethical when it comes to AI solutions has been too blurry for too long.

The CIOs, IT directors and managers, data scientists, and development leads polled in this research clearly understand and appreciate the gravity and impact at play when it comes to AI and ethics.”

Just over half (54%) of respondents have “deep concerns” around the risk of AI bias while a much higher percentage (81%) want more government regulation to prevent.

Given the still relatively small adoption of AI at this stage across most organisations; there’s a concerning number reporting harm from bias.

Over a third (36%) of organisations experienced challenges or a direct negative business impact from AI bias in their algorithms. This includes:

• Lost revenue (62%)
• Lost customers (61%)
• Lost employees (43%)
• Incurred legal fees due to a lawsuit or legal action (35%)
• Damaged brand reputation/media backlash (6%)

Ted Kwartler, VP of Trusted AI at DataRobot, commented:

“The core challenge to eliminate bias is understanding why algorithms arrived at certain decisions in the first place.

Organisations need guidance when it comes to navigating AI bias and the complex issues attached. There has been progress, including the EU proposed AI principles and regulations, but there’s still more to be done to ensure models are fair, trusted, and explainable.”

Four key challenges were identified as to why organisations are struggling to counter bias:

1. Understanding why an AI was led to make a specific decision
2. Comprehending patterns between input values and AI decisions
3. Developing trustworthy algorithms
4. Determinng what data is used to train AI

Fortunately, a growing number of solutions are becoming available to help counter/reduce AI bias as the industry matures.

“The market for responsible AI solutions will double in 2022,” wrote Forrester VP and Principal Analyst Brandon Purcell in his Predictions 2022: Artificial Intelligence (paywall) report.

“Responsible AI solutions offer a range of capabilities that help companies turn AI principles such as fairness and transparency into consistent practices. Demand for these solutions will likely double next year as interest extends beyond highly regulated industries into all enterprises using AI for critical business operations.”

(Photo by Darren Halstead on Unsplash)

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FRA’s report is titled Getting The Future Right and opens with some of the ways AI is already making lives better—such as helping with cancer diagnosis, and even predicting where burglaries are likely to take place.

“The possibilities seem endless,” writes Michael O’Flaherty, Director of the FRA, in the report’s foreword. “But how can we fully uphold fundamental rights standards when using AI?”

The FRA interviewed over a hundred public administration officials, private company staff, and a diverse range of experts, in a bid to answer that question.

With evidence of algorithms having biases which could lead to automating societal issues like racial profiling—it’s a question that needs answering if the full potential of AI is going to be unlocked for the whole of society.

O’Flaherty says:

“AI is not infallible, it is made by people – and humans can make mistakes. That is why people need to be aware when AI is used, how it works and how to challenge automated decisions. The EU needs to clarify how existing rules apply to AI. And organisations need to assess how their technologies can interfere with people’s rights both in the development and use of AI.

“We have an opportunity to shape AI that not only respects our human and fundamental rights but that also protects and promotes them.”

AI is being used in almost every industry in some form or another—if not already, it will be soon.

Biases in AI are more dangerous in some industries than others. Policing is an obvious example, but in areas like financial services it could mean one person being given a loan or mortgage compared to another.

Without due transparency, these biases could happen without anyone knowing the reasons behind such decisions—it could simply be because someone grew up in a different neighbourhood. Each automated decision has a very real human impact.

The FRA calls for the EU to:

• Make sure that AI respects ALL fundamental rights – AI can affect many rights – not just privacy or data protection. It can also discriminate or impede justice. Any future AI legislation has to consider this and create effective safeguards.
• Guarantee that people can challenge decisions taken by AI – people need to know when AI is used and how it is used, as well as how and where to complain. Organisations using AI need to be able to explain how their systems take decisions.
• Assess AI before and during its use to reduce negative impacts – private and public organisations should carry out assessments of how AI could harm fundamental rights.
• Provide more guidance on data protection rules – the EU should further clarify how data protection rules apply to AI. More clarity is also needed on the implications of automated decision-making and the right to human review when AI is used.
• Assess whether AI discriminates – awareness about the potential for AI to discriminate, and the impact of this, is relatively low. This calls for more research funding to look into the potentially discriminatory effects of AI so Europe can guard against it.
• Create an effective oversight system – the EU should invest in a more ‘joined-up’ system to hold businesses and public administrations accountable when using AI. Authorities need to ensure that oversight bodies have adequate resources and skills to do the job.

The EU has increased its scrutiny of “big tech” companies like Google in recent years over concerns of invasive privacy practices and abusing their market positions. Last week, AI News reported that Google had controversially fired leading AI ethics researcher Timnit Gebru after she criticised her employer in an email.

Google chief executive Sundar Pichai wrote in a memo: “We need to accept responsibility for the fact that a prominent black, female leader with immense talent left Google unhappily.

“It’s incredibly important to me that our black, women, and under-represented Googlers know that we value you and you do belong at Google.”

Gebru gave an interview to the BBC this week in which she called Google and big tech “institutionally racist”. With that in mind, the calls made in the FRA’s report seem especially important to heed.

You can download a full copy of the FRA’s report here.

(Photo by Guillaume Périgois on Unsplash)

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A browse through our ‘ethics’ category here on AI News will highlight the serious problem of bias in today’s algorithms. With AIs being increasingly used for decision-making, parts of society could be left behind.

Conducted by the Centre for Data Ethics and Innovation (CDEI), the investigation will focus on areas where AI has tremendous potential – such as policing, recruitment, and financial services – but would have a serious negative impact on lives if not implemented correctly.

Digital Secretary Jeremy Wright said:

“Technology is a force for good which has improved people’s lives but we must make sure it is developed in a safe and secure way.

Our Centre for Data Ethics and Innovation has been set up to help us achieve this aim and keep Britain at the forefront of technological development.

I’m pleased its team of experts is undertaking an investigation into the potential for bias in algorithmic decision-making in areas including crime, justice and financial services. I look forward to seeing the Centre’s recommendations to Government on any action we need to take to help make sure we maximise the benefits of these powerful technologies for society.”

Durham police are currently using AI for a tool it calls ‘Harm Assessment Risk’. As you might guess, the AI determines whether an individual is likely to cause further harm. The tool helps with decisions on whether an individual is eligible for deferred prosecution.

If an algorithm is more or less effective on individuals with different characteristics over another, serious problems would arise.

Roger Taylor, Chair of the CDEI, is expected to say during a Downing Street event:

“The Centre is focused on addressing the greatest challenges and opportunities posed by data driven technology. These are complex issues and we will need to take advantage of the expertise that exists across the UK and beyond. If we get this right, the UK can be the global leader in responsible innovation.

We want to work with organisations so they can maximise the benefits of data driven technology and use it to ensure the decisions they make are fair. As a first step we will be exploring the potential for bias in key sectors where the decisions made by algorithms can have a big impact on people’s lives.

I am delighted that the Centre is today publishing its strategy setting out our priorities.”

In a 2010 study, researchers at NIST and the University of Texas in Dallas found (PDF) algorithms designed and tested in East Asia are better at recognising East Asians, while those developed in Western countries are more accurate when detecting Caucasians.

Similar worrying discrepancies were highlighted by Algorithmic Justice League founder Joy Buolamwini during a presentation at the World Economic Forum back in January. For her research, she analysed popular facial recognition algorithms.

These issues with bias in algorithms need to be addressed now before they are used for critical decision-making. The public is currently unconvinced AI will benefit humanity, and AI companies themselves are bracing for ‘reputational harm’ along the way.

Interim reports from the CDEI will be released in the summer with final reports set to be published early next year.

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Buolamwini is also an MIT Media Lab researcher and went somewhat viral for her TED Talk in 2016 titled ‘How I’m fighting bias in algorithms’.

Her latest speech included a presentation in which Buolamwini went over an analysis of the current popular facial recognition algorithms.

Here were the overall accuracy results when guessing the gender of a face:

• Microsoft: 93.7 percent
• Face++: 90 percent
• IBM: 87.9 percent

Shown in this way, there appears to be little problem. Of course, society is a lot more diverse and algorithms need to be accurate for all.

When separated between males and females, a greater disparity becomes apparent:

• Microsoft: 89.3 percent (females), 97.4 percent (males)
• Face++: 78.7 percent (females), 99.3 percent (males)
• IBM: 79.7 percent (females), 94.4 percent (males)

Here we begin to see the underrepresentation of females in STEM careers begin to come into effect. China-based Face++ suffers the worst, likely a result of the country’s more severe gender gap (PDF) over the US.

Splitting between skin type also increases the disparity:

• Microsoft: 87.1 percent (darker), 99.3 percent (lighter)
• Face++: 83.5 percent (darker), 95.3 percent (lighter)
• IBM: 77.6 percent (darker), 96.8 percent (lighter)

The difference here is likely again to do with a racial disparity in STEM careers. A gap between 12-19 percent is observed between darker and lighter skin tones.

So far, the results are in line with a 2010 study by researchers at NIST and the University of Texas in Dallas. The researchers found (PDF) algorithms designed and tested in East Asia are better at recognising East Asians, while those developed in Western countries are more accurate when detecting Caucasians.

“We did something that hadn’t been done in the field before, which was doing intersectional analysis,” explains Buolamwini. “If we only do single axis analysis – we only look at skin type, only look at gender… – we’re going to miss important trends.”

Here is where the results get most concerning. Results are in descending order from most accurate to least:

Microsoft

Lighter Males (100 percent)

Lighter Females (98.3 percent)

Darker Males (94 percent)

Darker Females (79.2 percent)

Face++

Darker Males (99.3 percent)

Lighter Males (99.2 percent)

Lighter Females (94 percent)

Darker Females (65.5 percent)

IBM

Lighter Males (99.7 percent)

Lighter Females (92.9 percent)

Darker Males (88 percent)

Darker Females (65.3 percent)

The lack of accuracy with regards to females with darker skin tones is of particular note. Two of the three algorithms would get it wrong in approximately one-third of occasions.

Just imagine surveillance being used with these algorithms. Lighter skinned males would be recognised in most cases, but darker skinned females would be stopped often. That could be a lot of mistakes in areas with high footfall such as airports.

Prior to making her results public, Buolamwini sent the results to each company. IBM responded the same day and said their developers would address the issue.

When she reassessed IBM’s algorithm, the accuracy when assessing darker males jumped from 88 percent to 99.4 percent, for darker females from 65.3 percent to 83.5 percent, for lighter females from 92.9 percent to 97.6 percent, and for lighter males it stayed the same at 97 percent.

Buolamwini commented: “So for everybody who watched my TED Talk and said: ‘Isn’t the reason you weren’t detected because of, you know, physics? Your skin reflectance, contrast, et cetera,’ — the laws of physics did not change between December 2017, when I did the study, and 2018, when they launched the new results.”

“What did change is they made it a priority.”

You can watch Buolamwini’s full presentation at the WEF here.

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