A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control, by Kartik Hosanagar, published by Viking.

Wharton School Professor Kartik Hosanagar is the author of A Human’s Guide to Machine Intelligence

Every now and then, 87 million Spotify users receive a great new daily mixtape: 50 songs that feel like a gift from a music-loving friend, totally in synch with their musical proclivities. But these thoughtfully curated playlists, from Spotify, are cooked up by an algorithm.

Wharton School Professor Kartik Hosanagar’s timely new book A Human’s Guide to Machine Intelligence explores how algorithms and the artificial intelligence that underlies them have seeped into our lives. They have crept into areas of decision-making that were once the sole preserve of humans.

Since it’s so hard to spot, you might not even have noticed how much of your life is now influenced by algorithms which make everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news.

Having a machine tell you how long your commute will be, what music you should listen to, are all relatively harmless examples. But while you’re scrolling through your Facebook newsfeed, an algorithm somewhere is providing doctors with AI-based diagnostic guidance, helping recruiters shortlist job applicants, deciding university admissions, dispensing criminal justice and upending old ways of decision making.

“Chatbots like Siri and Alexa could ultimately be gateways through which we access information and transact online,” writes Hosanagar, who is the John C Hower Professor of Technology and Digital Business at Wharton, at the University of Pennsylvania.

“Companies are hoping to use chatbots to replace a large number of their customer service staff, employing them, for example as shopping assistants — gathering information about our taste in clothes, evaluating it, and making purchase decisions on our behalf,” he writes.

Hosanagar succeeds in making the case that we need to arm ourselves with a “better, deeper, more nuanced” understanding of the phenomenon of algorithmic thinking. To flag the lurking dangers, the author highlights how it took less than 24 hours for Twitter to corrupt an AI chatbot.

The book delves into how Microsoft famously unveiled Tay — a Twitter bot that the company described as an experiment in "conversational understanding." The more you chat with Tay, said Microsoft, the smarter it gets, learning to engage people through "casual and playful conversation."

Sadly, the conversations didn't stay playful for long. Soon after Tay launched, people starting tweeting the bot with all sorts of crazy misogynistic and racist remarks. Tay — being essentially a robot parrot with an internet connection — started repeating these sentiments back to users.

The author says Microsoft didn’t anticipate that Tay would develop so “aggressive a personality” with such alarming speed.

“The algorithim that controlled the bot did something that no one who programmed it expected it to do: it took on a life of its own,” writes Hosanagar.

Ultimately, Microsoft shut down the project’s website and MIT included Tay in its annual Worst in Tech rankings.

Hosanagar’s big picture thinking on algorithms contains some profound analysis of the new technology we have built, which has now unwittingly come to constrain us. He channels powerful examples to show readers how we’ve even delegated life-and-death decisions to algorithms — decisions once made by doctors, pilots, and judges.

Algorithms, for all their promise, can also lead to biased outcomes. Researchers have long been concerned about algorithmic fairness. For instance, Amazon’s AI-based recruiting engine turned out to dismiss female candidates for software developer jobs and other technical posts. That is because Amazon’s computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period. Most came from men, a reflection of male dominance across the tech industry.

The book argues that the possibility of discrimination is complicated by algorithms’ mathematical complexity: the computation that turns input into output can be difficult to trace, which has led to the characterization of algorithms as inscrutable “black boxes.”

But with the right regulations in place, algorithms could be more transparent than human cognition, the author argues. And the transparency could make it easier to detect and prevent discrimination. The author proposes effectively a “bill of rights” that limits algorithims’ powers and how users can hold them accountable. It clarifies the level of transparency, “explainability,” and control we should expect from the algorithms we use. The book includes Hosanagar’s bold Algorithmic Bill of Rights, which would give enterprises a framework to help them guide their AI projects in ethically responsible ways.

This gem of a book is a must-read for anyone wanting to grapple with how artificial intelligence is bringing sweeping new possibilities, while also raising seminal questions.