It's our mistakes that make us human

What we learn distinguishes us from tech.

Silvianne Aspray

Silvianne Aspray is a theologian and postdoctoral fellow at the University of Cambridge.

A man staring at a laptop grimmaces and holds his hands to his head.

Francisco De Legarreta C. on Unsplash.

The distinction between technology and human beings has become blurry: AI seems to be able to listen, answer our questions, even respond to our feelings. It becomes increasingly easy to confuse machines with humans. In this situation, it is increasingly important to ask: What makes us human, in distinction from machines? There are many answers to this question, but for now I would like to focus on just one aspect of what I think is distinctively human: As human beings, we live and learn in time.

To be human means to be intrinsically temporal. We live in time and are oriented towards a future good. We are learning animals, and our learning is bound up with the taking of time. When we learn to know or to do something, we necessarily make mistakes, and we take practice. But keeping in view something we desire – a future good – we keep going.

Let’s take the example of language. We acquire language in community over time. Toddlers make all sorts of hilarious mistakes when they first try to talk, and it takes them a long time even to get single words right, let alone to try and form sentences. But they keep trying, and they eventually learn. The same goes with love: Knowing how to love our family or our neighbours near and far is not something we are good at instantly. It is not the sort of learning where you absorb a piece of information and then you ‘get’ it. No, we learn it over time, we imitate others, we practice and even when we have learned, in the abstract, what it is to be loving, we keep getting it wrong.

This, too, is part of what it means to be human: to make mistakes. Not the sort of mistakes machines make, when they classify some information wrongly, for instance, but the very human mistake of falling short of your own ideal. Of striving towards something you desire – happiness, in the broadest of terms – and yet falling short, in your actions, of that very goal. But there’s another very human thing right here: Human beings can also change. They – we – can have a change of heart, be transformed, and at some point in time, actually start to do the right thing – even against all the odds. Statistics of past behaviours, do not always correctly predict future outcomes. Part of being human means that we can be transformed.

Transformation sometimes comes suddenly, when an overwhelming, awe-inspiring experience changes somebody’s life as by a bolt of lightning. Much more commonly, though, such transformation takes time. Through taking up small practices, we can form new habits, gradually acquire virtue, and do the right thing more often than not. This is so human: We are anything but perfect. As Christians would say: We have a tendency to entangle ourselves in the mess of sin and guilt. But we also bear the image of the Holy One who made us, and by the grace and favour of that One, we are not forever stuck in the mess. We are redeemed: are given the strength to keep trying, despite the mistakes we make, and given the grace to acquire virtue and become better people over time. All of this to say that being human means to live in time, and to learn in time.

So, this is a real difference between human beings and machines: Human beings can, and do strive toward a future good.

Now compare this to the most complex of machines. We say that AI is able to “learn”. But what does it mean to learn, for AI? Machine learning is usually categorized into supervised learning, unsupervised and self-supervised learning. Supervised learning means that a model is trained for a specific task based on correctly labelled data. For instance, if a model is to predict whether a mammogram image contains a cancerous tumour, it is given many example images which are correctly classed as ‘contains cancer’ or ‘does not contain cancer’. That way, it is “taught” to recognise cancer in unlabelled mammograms. Unsupervised learning is different. Here, the system looks for patterns in the dataset it is given. It clusters and groups data without relying on predefined labels. Self-supervised learning uses both methods: Here, the system uses parts of the data itself as a kind of label – such as, for instance, predicting the upper half of an image from its lower half, or the next word in a given text. This is the predominant paradigm for how contemporary large-scale AI models “learn”.

In each case, AI’s learning is necessarily based on data sets. Learning happens with reference to pre-given data, and in that sense with reference to the past. It may look like such models can consider the future, and have future goals, but only insofar as they have picked up patterns in past data, which they use to predict future patterns – as if the future was nothing but a repetition of the past.

So this is a real difference between human beings and machines: Human beings can, and do strive toward a future good. Machines, by contrast, are always oriented towards the past of the data that was fed to them. Human beings are intrinsically temporal beings, whereas machines are defined by temporality only in a very limited sense: it takes time to upload data, and for the data to be processed, for instance. Time, for machines, is nothing but an extension of the past, whereas for human beings, it is an invitation to and the possibility for being transformed for the sake of a future good. We, human beings, are intrinsically temporal, living in time towards a future good – which machines do not.

In the face of new technologies we need a sharpened sense for the strange and awe-inspiring species that is the human race, and cultivate a new sense of wonder about humanity itself.

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Comment

Justice

Leading

Politics

7 July 2025

5 min read

The consequences of truth-telling are so severe our leaders can’t admit their mistakes

When accountability means annihilation, denial is the only way to survive

Leah Brown

Leah is a campaigner for restorative practices and founder of The WayFinders Group.

Baroness Casey.

BBC.

Why do our leaders struggle so profoundly with admitting error?

Media and inquiries regularly report on such failures in the NHS, the Home Office, the Department of Work and Pensions, HMRC, the Metropolitan Police, the Ministry of Defence, and so many more public institutions. Often accompanied by harrowing personal stories of the harm done.

In a recent white paper (From harm to healing: rebuilding trust in Britain’s publicly funded institutions), I defined “harm” as a holistic concept occurring where physical injury or mental distress is committed and sustained and explained that harm is generally something that is caused, possibly resulting in injury or loss of life.

When we look at harm from an institutional perspective, structural power dynamics inevitably oppress certain groups, limit individual freedoms, and negatively affect the safety and security of individuals. But when we look at it through the lens of the individuals who run those institutions, we see people who often believe that they are acting in good faith, believe that their decisions won’t have a significant impact, who don’t have time to think about the decisions they are making, or worse still, prefer to protect what is in their best interest.

Even well-intentioned leaders can become complicit in cycles of harm - not just through malice, but through their lack of self-awareness and unwillingness to put themselves in the shoes of the person on the receiving end of their decisions.

Martin Luther King Jr supposedly said, “the ultimate measure of a man is not where he stands in moments of comfort and convenience, but where he stands at times of challenge and controversy.” In contemporary politics, leaders are neither selected nor (largely) do they remain, because of their humility. Humility is synonymous with weakness and showing weakness must be avoided at all cost. Responsibility is perceived as something that lies outside of us, rather than something we can take ownership of from within.

So, why do leaders struggle so profoundly with admitting error?

The issue is cultural and three-fold.

First, we don’t quantify or systematically address human error, allowing small mistakes to escalate.

We then enable those responsible to evade accountability through institutional protection and legal barriers.

Finally, we actively discourage truth-telling by punishing whistle-blowers rather than rewarding transparency. Taken together, these create the very conditions that transform errors into institutional harm.

Nowhere is this plainer than in Baroness Casey’s recent report on Group-based Child Sexual Exploitation and Abuse that caused the Government to announce a grooming gangs inquiry. In this case, the initial harm was compounded by denial and obfuscation, resulting not just in an institutional failure to protect children, but system-wide failures that have enabled the so-called “bad actors” to remain in situ.

Recently, this trend was bucked at Countess of Chester Hospital where the police arrested three hospital managers involved in the Lucy Letby investigation. Previously, senior leadership had been protected, thus allowing them to evade accountability. Humble leadership would look like acting when concerns are raised before they become scandals. However, in this case, leadership did act; they chose to bury the truth rather than believe the whistle-blowers.

Until we separate admission of error from institutional destruction, we will continue to incentivise the very cover-ups that erode public trust.

The answer to our conundrum is obvious. In Britain, accountability is conflated with annihilation. Clinging onto power is the only option because admitting error has become synonymous with career suicide, legal liability, and is tantamount to being hanged in the gallows of social media. We have managed to create systems of governing where the consequences of truth-telling are so severe that denial is the only survival mechanism left. We have successfully weaponised accountability rather than understanding it as the foundation of trust.

If Rotherham Metropolitan Borough Council had admitted even half of the failures Alexis Jay OBE identified in her 2013 report and that Baroness Casey identifies in her 2025 audit, leaders would face not only compensation claims but media storms, regulatory sanctions, and individual prosecutions. It’s so unthinkable to put someone through that that we shrink back with empathy as to why someone might not speak up. But this is not justice. Justice is what the families of Hillsborough have been seeking in the Public Authority (Accountability) Bill: legal duties of candour, criminal offences for those who deliberately mislead investigations or cover-up service failures, legal representation, and appropriate disclosure of documentation.

Regardless of your political persuasion, it has to be right that when police misconduct occurs, officers should fear not only disciplinary action and criminal charges. When politicians admit mistakes, they should face calls for their resignation. Public vilification is par for the course. Being ejected from office is the bare minimum required to take accountability for their actions.

The white paper shows that the cover-up always causes more damage than the original error. Institutional denial - whether relating to the Post Office sub-postmasters, the infected blood scandal victims, grooming gang victims, Grenfell Towers victims, Windrush claimants, or Hillsborough families - compounds the original harm exponentially.

In a society beset with blame, shame, and by fame, it is extraordinary that this struggle to admit error is so pervasive. Survivors can and will forgive human fallibility. What they will not forgive is the arrogance of institutions that refuse to acknowledge when they have caused harm.

The white paper refers to a four-fold restorative framework that starts with acknowledgment, not punishment. The courage to say “we were wrong” is merely the first step. Next is apology and accountability followed by amends. It recognises that healing - not just legal resolution - must be at the heart of justice, treating both those harmed and those who caused it as whole human beings deserving of dignity.

Until we separate admission of error from institutional destruction, we will continue to incentivise the very cover-ups that erode public trust. I was recently struck by Baroness Onora O’Neill who insisted that we must demand trustworthiness in our leaders. We cannot have trustworthiness without truth-telling, and we cannot have that without valuing the act of repairing harm over reputation management. True authority comes from service, through vulnerability rather than invulnerability; strength comes through the acknowledgement of weakness not the projection of power.

We must recognise that those entrusted with power have a moral obligation to those they serve. That obligation transcends institutional self-interest. Thus, we must stop asking why leaders struggle to admit error and instead ask why we have made truth-telling so dangerous that lies seem safer.