Interest in the possibilities afforded by algorithms and big data continues to blossom as early adopters gain benefits from AI systems that automate decisions as varied as making customer recommendations, screening job applicants, detecting fraud, and optimizing logistical routes.1 But when AI applications fail, they can do so quite spectacularly.2 Consider the recent example of Australia's "robodebt" scandal.3 In 2015, the Australian government established its Income Compliance Program, with the goal of clawing back unemployment and disability benefits that had been made inappropriately to recipients. It set out to identify overpayments by analyzing discrepancies between the annual income that individuals reported and the income assessed by the Australian Tax Office.

When discussing the threats of artificial intelligence, the first thing that comes to mind are images of Skynet, The Matrix, and the robot apocalypse. The runner up is technological unemployment, the vision of a foreseeable future in which AI algorithms take over all jobs and push humans into a struggle for meaningless survival in a world where human labor is no longer needed. Whether any or both of those threats are real is hotly debated among scientists and thought leaders. But AI algorithms also pose more imminent threats that exist today, in ways that are less conspicuous and hardly understood. In her book, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, mathematician Cathy O'Neil explores how blindly trusting algorithms to make sensitive decisions can harm many people who are on the receiving end of those decisions.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. When discussing the threats of artificial intelligence, the first thing that comes to mind are images of Skynet, The Matrix, and the robot apocalypse. The runner up is technological unemployment, the vision of a foreseeable future in which AI algorithms take over all jobs and push humans into a struggle for meaningless survival in a world where human labor is no longer needed. Whether any or both of those threats are real is hotly debated among scientists and thought leaders. But AI algorithms also pose more imminent threats that exist today, in ways that are less conspicuous and hardly understood.

Contrary to what some data scientists may like to believe, we can never reduce the world to mere numbers and algorithms. When it comes down to it, decisions are made by humans, and being an effective data scientist means understanding both people and data. When OPower, a software company, wanted to get people to use less energy, they provided customers with plenty of stats about their electricity usage and cost. However, the data alone were not enough to get people to change. In addition, OPower needed to take advantage of behavioral science, namely, studies showing people were driven to reduce energy when they received smiley emoticons on their bills showing how they compare to their neighbors!

While artificial intelligence (AI) has been historically confined to the Sci-Fi section of the movie genre, it has recently started picking up steam in the real world. In 2018, VCs pumped $9.3 billion into AI startups, which marked a 72% increase in funding compared to the previous year. SenseTime, the world's most valuable startup, turned profitable three years after establishment with an average annual revenue growth of 400% in those three years. With this explosive growth, though, keeping up with the innovations happening in artificial intelligence can seem intimidating. After spending the summer reading through several pieces of literature, I've come up with a list of the top three AI books that can provide you a well-rounded perspective on the industry, where it's headed, and what impacts it will make.

Every summer there is a mass exodus from New York City towards the white beach at Jones Beach State Park. Here, looking out over the Atlantic Ocean, you can sunbathe, catch a concert or play a game of mini-golf. And get away from the bustle of the city. But you have to get there first. And there's something odd about the route you take. The flyovers over the Southern State Parkway that leads to Jones Beach are low.

As befits the topic, we start our list with a comprehensive introduction into AI technology: "Introduction to Artificial Intelligence." Written by Phillip C. Jackson, Jr., the book is one of the classics that's still read by experts in the field and non-specialists alike. This book provides a summary of the previous two decades of research into the science of computer reasoning, and where it could be heading. Published in 1985, some of the information might be outdated, but if nothing else, the book could serve as a valuable historical document.

I'm not a data scientist, yet I still care about ethics in data science. I care about it for the same reason I care about civics: I'm not a lawyer or a legislator, but laws impact my life in a way that I want to understand well enough that I know how to navigate the civic landscape effectively. By analogy, data citizens are impacted by the models, methods, and algorithms created by data scientists, but they have limited agency to affect them. Data citizens must appeal to data scientists in order to ensure that their data will be treated ethically. Data science ethics is a new field and it may seem like we need to invent all the tools and methods we will need to build that field from scratch.

In writing the book, O'Neil has a big advantage. She is a data scientist and knows whereof she speaks. Big data does not lead to the truth. It just leads to a different truth. By definition, algorithms feeding off data inputs can tell us useful things about the areas giving rise to that data.

Fed up with the bribery, insider trading, embezzlement and money laundering committed by white-collar criminals? What if there was an app that could help nab these crooks by using the same machine learning tools and geospatial data increasingly relied upon by police to predict where the next burglary, drug deal or assault might go down? Sam Lavigne, co-creator of the White Collar Crime Risk Zones app, was onstage at the recent Strata Data Conference in New York, claiming to be able to do just that. "We used instances of financial malfeasance; density of nonprofit organizations, liquor stores, bars and clubs; and density of investment advisers," a straight-faced Lavigne said to an audience of data experts who immediately got the dark humor. For although the White Collar Crime Risk Zones app was indeed built -- using historical data from the Financial Industry Regulatory Authority -- its purpose is not to track white-collar crime, but to draw attention to the danger these kinds of applications, and the data they rely upon, present.