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March 27, 2019

Lessons To Learn: Great Reading For AI Decision Makers

By 
Christopher Null

Enthusiasm for artificial intelligence (AI) is booming in the C-suite today.

Nearly 9 out of 10 (88%) business leaders report that their companies plan to boost investment in AI in the coming year, according to a Deloitte survey of U.S. executives across 10 industries.

The support for AI, however, masks a significant knowledge gap: Only 17% of executives say they are well-versed in the core concepts of AI and its applications at their companies. This lack of a firm grounding in AI basics could explain the executives’ biggest concerns about AI: 43% worry that they’ll make the wrong strategic decision based on AI tech; 39% fear legal and regulatory fallout from AI-based mistakes; and 32% are concerned about ethical missteps in using the technologies.

For business leaders searching for intelligent guides on the ethics of AI and best practices for adopting the technology, this and previous issues of Forbes AI offer a variety of great resources. But there’s also a lot of outside inspiration to be found on bookshelves.

We asked a cross-section of technology thought leaders from enterprise startups, consultancies, and academia to recommend essential books that shaped their thinking about AI. Their recommendations below can help you become a wise steward of these ethically complex emerging technologies for the future.

Cathy O’Neil

Author, “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy,” founder of ORCAA, an algorithmic auditing company

Cathy Oneil
Cathy O'Neil

Meredith Broussard’s “Artificial Unintelligence: How Computers Misunderstand the World” is a must read for business leaders.

It explains just how simplistic and sometimes downright stupid computers and automated systems are—and how we nonetheless ascribe something close to perfection to them because we don’t quite get that (and, of course, sometimes we don’t want to get that). This is both an obvious fact and a deep lesson, and Meredith, as a programmer herself, does a good job communicating at both levels.

None of this is to suggest that any one book will ever become the bible of AI adoption for business. Execs would be wise to check out this and other editions of “Forbes AI” and continue to educate themselves about AI—or risk getting left behind by others quicker to understand how to put it to work.

Tom Davenport

Author, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines,” distinguished professor of information technology and management at Babson College

Tom Davenport
Tom Davenport

Cathy O’Neil’s book—“Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy”—is an essential read.

It was the first book to describe in detail the issue of algorithmic bias. That is one of the biggest ethical issues with AI, and the book is still quite relevant to the problem. O’Neil is a mathematician and data scientist herself, and she knows where the skeletons are hidden in algorithms and models.

O’Neil describes situations in which algorithms favor one type of customer or employee over another, and the disadvantaged are often women or minorities. This problem has existed for a few decades—“redlining” for mortgage lending in economically disadvantaged areas was an early example—but the widespread use of analytics and AI have exacerbated it.

Pavel Dmitriev

Former principal data scientist for analysis and experimentation at Microsoft, current vice president of data science at Outreach

Pavel Dmitriev
Pavel Dmitriev

I recommend “Applied Artificial Intelligence: A Handbook for Business Leaders” by Mariya Yao, Adelyn Zhou, and Marlene Jia.

It is for business leaders who do not have much experience with AI. What I like is that it not only gives a good overview of what kind of AI is out there and what problems it can solve for you, but it also gives a practical, step-by-step guide on how to introduce AI into your organization, including pitfalls to avoid. It also has many great real-life examples.

Harry Glaser

Co-founder and CEO of Periscope Data

Business leaders should take a look at these two articles by data scientist Ingo Mierswa: “What Is Artificial Intelligence, Machine Learning, and Deep Learning” and “What Artificial Intelligence Can Do — and What Not.” Dr. Mierswa is the founder and president of RapidMiner, a leading data science platform for analytics teams.

Harry Glaser
Harry Glaser

While data teams should be taking the lead and be empowered to shape how AI and machine learning is created and used in organizations, these pieces can give business leaders a better understanding of the terminology involved and the types of questions that can be addressed with these systems.

Vivek Kaytal

Global risk and analytics leader at Deloitte

For anyone who is focused on, or interested in, AI ethics and governance, I recommend reading “Every Leader’s Guide to the Ethics of AI” by Tom Davenport and myself, published in MIT Sloan Management Review, and “Why We Need to Audit Algorithms” by James Guszcza, Iyad Rahwan, Will Bible, Manuel Cebrian, and myself, published in Harvard Business Review.

Vivek Kaytal
Vivek Kaytal

The summary takeaways from these articles is that there is a need for board visibility/oversight into the risks from AI and data—and their impact on every aspect of business. Algorithm auditing should ultimately become the purview of a learned (data science) professional with proper credentialing, standards of practice, disciplinary procedures, ties to academia, continuing education, and training in ethics, regulation, and professionalism.

Another excellent book focusing on many of the same issues is “Prediction Machines: The Simple Economics of Artificial Intelligence” by Ajay Agarwal, Joshua Gans, and Avi Goldfarb. It provides an economic viewpoint on how to develop an AI business strategy—i.e., how to make decisions through a series of trade-offs.

Angelica Lim

Assistant professor of professional practice in computing science at Simon Fraser University

The book that was most influential to me was “Looking for Spinoza: Joy, Sorrow, and the Feeling Brain” by neuroscientist Antonio Damasio.

Angelica Lim
Angelica Lim

I work in emotional AI, a field known as “affective computing” or, more specifically, “affective robotics.” And since I work at the intersection of AI, robotics, and emotions, I think a lot about human emotions as a model for how robots might have and process them. Damasio’s book reconciles the concepts of emotions and feelings with the mind, logic, and rationality.

Emotion and logic are usually placed at odds with each other. And my view was changed completely, to see how emotions are not just “color” to our world, but fundamental to how we act, think, remember, and behave as normal agents.

Lili Cheng

Corporate vice president of AI and research at Microsoft

My must-read is a trilogy from Steven Pinker: “The Language Instinct: How the Mind Creates Language,” “The Stuff of Thought: Language as a Window into Human Nature,” and “Words and Rules: The Ingredients of Language.”

Lili Cheng
Lili Cheng

With AI and ethics, I keep going back to these basics, which describe how people think about language and communicate. For critical interactions—an argument you are having with your boss, a tough business deal—how can AI help? If AI can help us do a tiny bit better in these moments that matter, it can have a profound impact.

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