To do more efficiently, and to complete it in less time are interrelated goals. To this effect, AI has become a strong force and a giant leap for mankind. The speed of AI progress is fast-paced to a degree that is almost difficult to keep up with. However, if we stare too closely at light rays, we end up with blighted eyesight. As such, while many are carried away by the strength of AI, there remains a large dark cloud almost unnoticed by many who are lost in the glory and glamour of AI abilities.
When the English author G. K. Chesterton first visited America in 1921, his hosts took him to see Times Square at night. Chesterton stood staring in silence for several increasingly awkward moments. When someone finally asked him for his thoughts, Chesterton replied: “I was thinking how beautiful this would be if I couldn’t read.” Like Chesterton, in the AI world of today, we can read the signs, and we can see its ugliness.
When we talk of AI, for many people, what comes to mind are generative AIs like ChatGPT, Gemini, Claude, etc., but artificial intelligence tools expand beyond generative AIs to computer algorithms. Through machine and deep learning capabilities, computer technologies now have the ability to learn without being explicitly programmed. Computer technology can now process information and make decisions by itself—a particular strength that is missing from pre-modern technologies like the telegraph, the telephone, the typewriter, and the radio.
Leveraging these AI capabilities, many organizations and governments have now given AI the power to make decisions on different issues, especially those that are believed to arouse human bias, such as securing admission or scholarships, getting loans from banks, obtaining health insurance, recruitment, etc.
More cases in point: Santander Bank (global headquarters in Spain) is known to use AI for credit scoring and loan approvals, leveraging machine learning to assess creditworthiness and speed up loan processing. The Bank of England and FCA’s 2022 survey (cited in Skadden’s 2023 article) found that 79% of U.K. financial firms use AI for critical operations like fraud detection, cybersecurity, and loan processing.
Also, many universities globally, especially in the U.S. and Europe, have adopted AI tools for admissions to analyze applications, predict student success, and assist with enrollment decisions, though specific names aren’t always public due to proprietary systems.
Moreover, a report from BSA Law (May 2024) highlights that the UAE government has invested heavily in AI to assess health claims, predict customer behavior, and improve risk modeling, allowing insurers to automate processes and offer more personalized insurance plans. Since 2021, Morocco has implemented a digital system based on USR (Unified Social Register) algorithms to determine eligibility for social aid and health insurance, aligning with the government’s political and economic goals.
In recruitment exercises, a company such as Unilever uses HireVue’s virtual interviewing tool to score candidates’ spoken words, comparing them to traits of high-performing employees. L’Oréal (Global) also developed an AI-enabled interview tool using StepStone’s “Mya” chatbot. Mya asks candidates specific questions based on traits of successful L’Oréal employees, evaluating responses, sentence structure, and vocabulary to streamline candidate assessment.
While the foregoing decision-making processes have been handed over to AI with the overriding idea that AI is unbiased and that through its mathematical codes and modeling, we can have a more efficient, egalitarian, and unbiased society, the truth is that there are still human opinions embedded in codes. To reduce subjective and intersubjective decision-making processes to mathematical codes, the opinion of the programmer must come into play. How does this happen?
To build an algorithm (AI), you need two major things: data and definition of success. Data, loosely put, is an aggregate of a noun’s (person, animal, place, or thing) history or past. While definition of success is the opinion of what the programmer thinks success should be in that particular niche.
For instance, in April 2025, TechCabal announced that a Nigerian lending software startup, Lendsqr, was building an artificial intelligence model that analyzes borrowers’ voices and faces to determine if they qualify for a loan. The company admits that the model is 76% accurate, but how do we account for the 24% inaccuracy? More importantly, if a customer is wrongly rejected, to whom is he or she going to report the AI?
For Lendsqr, loan approval hypothetical situations (opinions in codes) can be: when the picture is blurry, don’t approve the loan; when the voice is weak, don’t approve the loan; when one eye is not well opened, don’t approve the loan, etc. These hypothetical situations or whatever ideas the programmer embeds in their code remain and become their definition of success on that expedition.
Organizations and societies that employ these AI technologies have therefore pushed humanity backward because they have misunderstood what it means to be human and to be alive. An example given: a national health-care system that deploys algorithms to monitor my health. At one extreme, the system could take an overly rigid approach and ask its algorithm to predict what illnesses I am likely to suffer from.
The algorithm then goes over my genetic data, my medical file, my social media activities, my diet, and my daily schedule and concludes that I have a 91 percent chance of suffering a heart attack at the age of fifty. If this rigid medical algorithm is used by my insurance company, it may prompt the insurer to raise my premium.
If it is used by my bankers, it may cause them to refuse me a loan. If it is used by potential spouses, they may decide not to marry me. But it is a mistake to think that the rigid algorithm has really discovered the truth about me. The human body is not a fixed block of matter but a complex organic system that is constantly growing, decaying, and adapting. Our minds too are in constant flux. Thoughts, emotions, and sensations pop up, flare for a while, and die down.
Moreover, another big issue lies in the idea of data (aggregate of our past) as a viable means of predicting the present or future of an individual. The question has always been: do your past actions determine your tomorrow? For instance, does a violent childhood determine that a child is going to be a criminal adult? Let’s take a look at the story of the Tyson brothers. Mike Tyson (famous boxer) was born on the wrong side of the tracks. His early life included a father who wasn’t really his father, who soon fled the coop anyway, serial homes in condemned buildings, and a heavy-drinking mother who could be violent to anyone who crossed her, children included.
In a profile in Rolling Stone magazine, Tyson is described as ‘finding his identity’ by robbing houses, beating people up, and doing drugs. He first snorted coke when he was 11 and, by 13, had been arrested 38 times.
He finished up in the Tryon School for Boys, an institution described elsewhere as New York’s most infamous juvenile prison. As he sums it up in his autobiography: ‘I did a lot of bad sh**.’ Without excusing Tyson’s behavior, we might say this is how it is if your life begins in brutality: history is destiny. That is, we might be tempted to say so, except for Mike Tyson’s brother.
Mike Tyson’s brother, Rodney Tyson—who features in stories of childhood theft and arrest in Mike’s autobiography where he is described confronting Mike with a gun—became a specialist surgical assistant in a hospital trauma department in Los Angeles, where his job included helping to patch up the victims of crime.
How does that affect our view of the power of a criminal childhood to shape a young person’s future? Maybe it changes little. Maybe we start compiling a mental list of ifs and buts that might explain the difference.
Maybe we change tack and wonder if Rodney Tyson is also haunted by his past but, unlike Mike, reacted against it, rejecting the norms of his background, rejecting the violence and crime in his home and neighborhood, and committing his life to trying to heal wounds. You see, he was shaped by his past too, like his brother, but in an opposite, compassionate direction.
Two individuals, same history but different outcomes. If an algorithm had been written to offer them a loan, it would have rejected them both, but the reality of the human situation is different. As continuous advancements emerge in the AI world, we must start asking ourselves important questions about the world we are creating for ourselves. Is AI going to be a tool or authority? Are we humans going to remain as spectators or agents?