This is a popular level, readable, up-to-date and well-informed introduction to algorithms and machine intelligence, published in 2018. The title is taken from the rudimentary first program that is taught in most programming courses – how to make a line of text appear on the screen.
Hannah Fry is a mathematician who is well known as a correspondent in BBC popular science programmes, and she excels in providing clear and easily comprehensible descriptions of complex mathematical and technical concepts. She is strongly in favour of the appropriate use of maths and technology, but keen that we don’t put too much faith in them. As a cautionary tale she tells the story of Robert Jones who followed his satnav until it led him and his car over the edge of a cliff. “We have somehow managed to be simultaneously dismissive of algorithms, intimidated by them and in awe of their capabilities”. Fry points to the problem of our human willingness to take algorithms at face value without wondering what’s going on behind the scenes.
This short book provides a helpful description of the development of sophisticated data engineering, including the role of data brokers, Cambridge Analytica, micro-targeted advertising and the use of social media to manipulate people’s emotions. The book highlights the many risks and biases from algorithmic systems that are currently in use, whilst debunking some of the more extreme claims made by enthusiasts. Fry provides many valuable references and a useful index enabling individual stories to be followed up.
Fry is cautiously optimistic that government regulation will curb the worst excesses of covert data manipulation on the internet. But she cautions “Whenever we use an algorithm, especially a free one, we need to ask ourselves about the hidden incentives. Why is this app giving me all this stuff for free? What is this algorithm really doing? Is this a trade I’m comfortable with? Would I be better off without it?”
Fry points out the potential benefits of employing algorithms to improve consistency in judicial sentencing decisions, whilst recognising the ability of algorithms to perpetuate the inequalities of the past. The answer is a partnership between algorithms and human judges. She argues for an algorithmic regulating board to control the data industry much as the US Food and Drug Administration regulates pharmaceuticals.
The book looks at a number of different applications for machine learning including analysis of medical images, self-driving cars (explaining the use of Bayesian statistics to infer the positions of moving objects) and crime prediction amongst others. Fry explains the technology behind facial recognition surveillance and discusses the pros and cons of its use for crime prevention. “To my mind the urgent need for algorithmic regulation is never louder or clearer than in the case of crime, where the very existence of these systems raises serious questions without easy answers. Somehow we are going to have to confront these difficult dilemmas…”
Fry concludes that “everywhere you look – in the judicial system, in healthcare, in policing even online shopping – there are problems with privacy, bias, error, accountability and transparency that aren’t going to go away easily. Just by virtue of some algorithms existing we face issues of fairness that cut to the core of who we are as humans, what we want our society to look like and how far we can cope with the impending authority of dispassionate technology.”
In addition to the importance of government regulation, Hannah Fry argues for a fundamental shift in the way that algorithms are constructed. They should be built to be contestable from the ground up, ensuring that automated systems are as easy to challenge as they are to implement.
And we as human beings have a vital role to play as we interact with algorithmic systems. Our role is in “…questioning their decisions, scrutinising their motives, acknowledging our emotions, demanding to know who stands to benefit, holding them to account for their mistakes and refusing to become complacent…”
“…In the age of the algorithm, humans have never been more important.”