Hello World: Being Human in the Age of Algorithms

4.5 4.5 out of 5 stars | 2,212 ratings

Price: 19.95

Last update: 01-31-2025


About this item

Shortlisted for the 2018 Royal Society Investment Science Book Prize

A look inside the algorithms that are shaping our lives and the dilemmas they bring with them.

If you were accused of a crime, who would you rather decide your sentence - a mathematically consistent algorithm incapable of empathy or a compassionate human judge prone to bias and error? What if you want to buy a driverless car and must choose between one programmed to save as many lives as possible and another that prioritizes the lives of its own passengers? And would you agree to share your family’s full medical history if you were told that it would help researchers find a cure for cancer?

These are just some of the dilemmas that we are beginning to face as we approach the age of the algorithm, when it feels as if the machines reign supreme. Already, these lines of code are telling us what to watch, where to go, whom to date, and even whom to send to jail. But as we rely on algorithms to automate big, important decisions - in crime, justice, healthcare, transportation, and money - they raise questions about what we want our world to look like. What matters most: Helping doctors with diagnosis or preserving privacy? Protecting victims of crime or preventing innocent people being falsely accused?

Hello World takes us on a tour through the good, the bad, and the downright ugly of the algorithms that surround us on a daily basis. Mathematician Hannah Fry reveals their inner workings, showing us how algorithms are written and implemented, and demonstrates the ways in which human bias can literally be written into the code. By weaving in relatable, real world stories with accessible explanations of the underlying mathematics that power algorithms, Hello World helps us to determine their power, expose their limitations, and examine whether they really are improvement on the human systems they replace.


Top reviews from the United States

Heavy Reader
5.0 out of 5 stars Excellent wake-up call for non-techies
Reviewed in the United States on May 22, 2021
Although I've used computers since the 1980s, I am nothing near tech-savvy when it comes to understanding how they work. But I found this discussion of algorithms to be informative and down to earth. She discusses what algorithms are and why they are useful in many areas, including policing & criminal justice, healthcare, entertainment, retail sales, self-driving cars, etc. She devotes a chapter to the now-well-known truism that "if you are not paying for the data, you ARE the data," and how the info we volunteer so readily is used and monetized. But the most important take-away from her book is her warning that algorithms will NEVER be perfected -- it's impossible -- and anyone who thinks they can be is setting up unrealistic expectations which, she says, are already rampant in the general public. She documents a number of mishaps and errors, some amusing but others downright terrifying (up to and including ruining peoples' lives), resulting from from our over-dependence on and misplaced trust in the infallibility of algorithms. Though software is an inevitable part of everyday life now, I'll never again think of any "smart" technology the same way.
Math and Software guy
4.0 out of 5 stars Good—a friendlier Weapons of Math Destruction
Reviewed in the United States on December 30, 2018
It was pretty good. In general, I thought the writing was good, and the subject matter important. However, I’m extremely familiar with these topics, so much of it was either known to me or slightly different cases than I already know. It felt like a more lighthearted version of Weapons Of Math Destruction(which is excellent btw).

If you’re less familiar with algorithms, data products, and machine intelligence, this will likely be an interesting read.

A few places I really appreciated were Fry's friendly writing, good examples, and obvious understanding.
Mustard Seed Ministries
5.0 out of 5 stars New world challenges
Reviewed in the United States on July 24, 2024
Although the cat may already be out of the bag, this book shows the alternatives still available to us for controlling A I in our daily lives. The author gives a worthy analysis and reasonable solutions which are not too late to deploy
Joshua de la Bruere
5.0 out of 5 stars An enjoyable and informative read!
Reviewed in the United States on May 2, 2023
I got this book as a gift and I very much enjoyed the humor and information provided here. Its a neat look at how Machine Learning can impact our lives and would reccomend this and Weapons of Math destruction to anyone wanting to get an overview to the field
Ian R. Bruce
4.0 out of 5 stars The mathematics of our modern life
Reviewed in the United States on October 29, 2023
A very readable and entertaining overview of how equations and mathematical models are now part of our everyday world. The book feels a bit dated in places -- recent genAI advances, for example. But a fun introduction.
Amazon Customer
5.0 out of 5 stars Best book on AI. Well documented. Many examples. I've been involved in AI since 1978, only in those
Reviewed in the United States on June 12, 2024
Times we talked automation, I developed many algorithms for telecommunications systems and call centers and many of examples given on this book are same experiences I had on those days,
Marilynne
3.0 out of 5 stars Good Read
Reviewed in the United States on September 20, 2019
Good read....if you are interested in broaden your thinking about mathematics.
Ryan James Spencer
5.0 out of 5 stars Good description of the nuances of machine learning with solid anectdotes
Reviewed in the United States on April 9, 2019
Hannah Fry has managed to draw from a range of topics to explore the applications, and their subsequent perils, of machine learning algorithms and techniques.

The fact that we tend to blindly encode our old behavior into systems and assume them to be now be somehow superior because of their improved consistency or accuracy does not exclude the fact that we are encoding our prior biases and mistakes into an automated system. Many such examples of where this can go horribly astray are listed although there is a refreshing amount of content describe the positive benefits, both actual and projected, of using these systems.

I definitely recommend as a light read to those in-the-know with machine learning to reify the importance of recognizing and combatting bias but also to the layperson who wants a better understanding of what modern day advances in machine learning are doing, where they are still struggling, and how they still need improvement.

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