“Technically Wrong” Is Absolutely Right

April 11th, 2018

I’ve worked in high tech for 35 years. Over the years I’ve developed a love-hate relationship with this industry. I love technologies that are needed and work well. I love technology companies that respect their customers and employees. All too often, however, technologies and the companies that make them don’t deserve our love. Sara Wachter-Boettcher echoes this sentiment in her wonderful book Technically Wrong. Sara is not anti-technology, but she firmly believes that we should hold technologies and the companies that create them responsible for their failures, especially when they do harm.

Systemic problems in the ways that tech companies are managed and products are created are surfacing more and more often these days. In the last few days, Facebook is the tech company whose irresponsible behavior has dominated the news. Facebook is not alone. Tech companies can function responsibly and ethically, but those that do are the exceptions, not the norm. Tech companies have created the mystique that they are special, and for this reason we give them a pass. I’ve always been uncomfortable with this mystique, which veils the dysfunction of tech companies. People who work in tech are no more special on average than those who work in other organizations. They are neither smarter nore more talented, despite the fact that they are compensated as if they were.

Most tech companies are dominated by the rather narrow perspective of privileged white men, which contributes to many of their problems. Their lack of diversity and assumption that they’re smarter than others leads to a myopic view of the world—one that misunderstands the needs of a large portion of their users. They think of a significant portion of their users as “edge cases,” and edge cases aren’t significant enough to consider.

Yes, I’m a privileged white guy myself, but I know that my success has been due in many respects to good fortune—the luck of privileged birth. Perhaps my background in the humanities and social sciences has helped me to see the world more broadly than many of my privileged high-tech brethren.

The book Technically Wrong exposes these problems eloquently and suggests solutions. Here’s the description that appears on the book’s dust cover:

Buying groceries, tracking our health, finding a date: whatever we want to do, odds are that we can now do it online. But few of us ask why all these digital products are designed the way they are. It’s time we change that. Many of the services we rely on are full of oversights, biases, and downright ethical nightmares. Chatbots that harass women. Signup forms that fail anyone who’s not straight. Social media sites that send peppy messages about deal relatives. Algorithms that put more black people behind bars.

Sara Wachter-Boettcher takes an unflinching look at the values, processes, and assumptions that lead to these and other problems. Technically Wrong demystifies the tech industry, leaving those of us on the other side of the screen better prepared to make informed choices about the services we use—and demand more from the companies behind them.

We should have started demanding more of tech companies long ago. If we had, many problems could have been prevented. It’s not too late, however, to turn this around, and turn it around we must.

Know Your Audience — Good Luck with That

March 15th, 2018

I’ve long appreciated the fact that knowing your audience is an important prerequisite for effective communication. Over time, however, I’ve learned that this can rarely be achieved with specificity. The reason is simple: audiences are rarely homogeneous. If your audience is composed of two or more people, it is to some extent diverse. Consequently, it is only possible to finely tailor communication for an audience of one, and even then it’s challenging.

In most scenarios, we should do our best to communicate in ways that work well for people in general rather than for particular individuals. At best, we can assess the interests, abilities, proclivities, and experiences of our audience to determine a range of communication approaches that are suitable and to perhaps discard some approaches that don’t fit. For example, if you were the warm-up act at a Trump political rally, you could safely assume that discourse suitable for a convention of physicists should be avoided. You could also assume that emotionally charged statements would carry more weight for most of your audience than a rational presentation of facts. (To be fair, this is true of most audiences.) You could not, however, narrow your approach to suit people who exhibit a particular intelligence as defined by Howard Gardner’s seven intelligences (visual-spatial, bodily kinesthetic, musical, etc.), although you could certainly cover the same content in multiple ways to broaden its effectiveness. As diversity in audiences increases, our communication approach must increasingly be informed by general rather than specific principles of communication. In the business of communication, knowing what works best for most people is more often useful than knowing what works best for particular people.

In the interest of communicating in the ways that suit people’s interests, abilities, proclivities, and experiences, we often shape our audiences to narrow their diversity. Schools do this by grouping students into grade levels and by offering multiple courses in a particular subject to suit the interests and abilities of particular groups. With unlimited time and resources, we could finely select our audiences to match a tailored communication approach, but this isn’t practical.

One of the best ways to accommodate the diverse needs of an audience is to practice empathy. If we can see them, we can pay attention to them. We can read their reactions. In my data visualization workshops, I’ve always limited the number of participants to 70, in part to make sure that I could see everyone well enough to read their reactions and adapt my teaching accordingly. Obviously, there are limits to what I can discern in facial expressions and physical gestures, but such cues can be quite informative. It is also for this reason that I’ve never taught my courses remotely, but only in face-to-face settings. Web-based courses, though sometimes necessary given the circumstances, are an inferior substitute for face-to-face interaction.

Another way that we can accommodate the diverse needs of an audience is to address the same content in multiple ways. Though redundant to some degree, this redundancy is useful and it doesn’t annoy the audience. It takes more time to cover the same content in multiple ways, so it comes with a cost, but it usually pays off.

“Know your audience” is useful advice, but it can only be applied to communications in limited ways. In the business of communications, it is more useful overall to understand how people process information in general and to base most of our communications on that knowledge.

Take care,

Randomness is Often Not Random

March 12th, 2018

In statistics, what we often identify as randomness in data is not actually random. Bear in mind, I am not talking about randomly generated numbers or random samples. Instead, I am referring to events about which data has been recorded. We learn of these events when we examine the data. We refer to an event as random when it is not associated with a discernible pattern or cause. Random events, however, almost always have causes. We just don’t know them. Ignorance of cause is not the absence of cause.

Randomness is sometimes used as an excuse for preventable errors. I was poignantly reminded of this a decade or so ago when I became the victim of a so-called random event that occurred while undergoing one of the most despised medical procedures known to humankind: a colonoscopy. In my early fifties at the time, it was my first encounter with this dreaded procedure. After this initial encounter, which I’ll now describe, I hoped that it would be my last.

While the doctor was removing one of five polyps that he discovered during his spelunking adventure into my dark recesses, he inadvertently punctured my colon. Apparently, however, he didn’t know it at the time, so he sent me home with the encouraging news that I was polyp free. Having the contents of one’s colon leak out into other parts of the body isn’t healthy. During the next few days severe abdominal pain developed and I began to suspect that my 5-star rating was not deserved. Once admitted to the emergency room at the same facility where my illness was created, a scan revealed the truth of the colonoscopic transgression. Thus began my one and only overnight stay so far in a hospital.

After sharing a room with a fellow who was drunk out of his mind and wildly expressive, I hope to never repeat the experience. Things were touch and go for a few days as the medical staff pumped me full of antibiotics and hoped that the puncture would seal itself without surgical intervention. Had this not happened, the alternative would have involved removing a section of my colon and being fitted with a stylish bag for collecting solid waste. To make things more frightening than they needed to be, the doctor who provided this prognosis failed to mention that the bag would be temporary, lasting only about two months while my body ridded itself of infection, followed by another surgery to reconnect my plumbing.

In addition to a visit from the doctor whose communication skills and empathy were sorely lacking, I was also visited during my stay by a hospital administrator. She politely explained that punctures during a routine colonoscopy are random events that occur a tiny fraction of the time. According to her, these events should not to be confused with medical error, for they are random in nature, without cause, and therefore without fault. Lying there in pain, I remember thinking, but not expressing, “Bullshit!” Despite the administrator’s assertion of randomness, the source of my illness was not a mystery. It was that pointy little device that the doctor snaked up through my plumbing for the purpose of trimming polyps. Departing from its assigned purpose, the trimmer inadvertently forged a path through the wall of my colon. This event definitely had a cause.

Random events are typically rare, but the cause of something rare is not necessarily unknown and certainly not unknowable. The source of the problem in this case was known, but what was not known was the specific action that initiated the puncture. Several possibilities existed. Perhaps the doctor involuntarily flinched in response to an itch. Perhaps he was momentarily distracted by the charms of his medical assistant. Perhaps his snipper tool got snagged on something and then jerked to life when the obstruction was freed. Perhaps the image conveyed from the scope to the computer screen lost resolution for a moment while the computer processed the latest Windows update. In truth, the doctor might have known why the puncture happened, but if he did, he wasn’t sharing. Regardless, when we have reliable knowledge of several potential causes, we should not ignore an event just because we can’t narrow it down to the specific culprit.

The hospital administrator engaged in another bit of creative wordplay during her brief intervention. Apparently, according to the hospital, and perhaps to medical practice in general, something that happens this rarely doesn’t actually qualify as an error. Rare events, however harmful, are designated as unpreventable and therefore, for that reason, are not errors after all. This is a self-serving bit of semantic nonsense. Whether or not rare errors can be easily prevented, they remain errors.

We shouldn’t use randomness as an excuse for ongoing ignorance and negligence. While it makes no sense to assign blame without first understanding the causes of undesirable events, it also makes no sense to dismiss them as inconsequential and as necessarily beyond the realm of understanding. Think of random events as invitations to deepen our understanding. We needn’t make them a priority for responsive action necessarily, for other problems that are understood might deserve our attention more, but we shouldn’t dismiss them either. Randomness should usually be treated as a temporary label.

Take Care,

When Metrics Do Harm

March 6th, 2018

We are obsessed with data. One aspect of this obsession is our fixation on metrics. Quantitative measures—metrics—can be quite useful for monitoring and managing performance, but only when they are skillfully used in the right circumstances for the right purposes. In his wonderful new book, The Tyranny of Metrics, Jerry Muller convincingly argues that the balance has shifted toward counterproductive and often harmful misuses of metrics.

As an historian, Muller brought a high degree of scholarship to his examination of metrics. I’ll let the description that appears on the inside flap of the book’s slip cover give you sense of its contents.

Today, organizations of all kinds are fueled by the belief that the path to success is quantifying human performance, publicizing the results, and dividing up the rewards based on the numbers. But in our zeal to instill the evaluation process with scientific rigor, we’ve gone from measuring performance to fixating on measuring itself. The result is a tyranny of metrics that threatens the quality of our lives and most important institutions. In this timely and powerful book, Jerry Muller uncovers the damage our obsession with metrics is causing—and shows how we can begin to fix the problem.

Filled with examples from education, medicine, business and finance, government, the police and military, and philanthropy and foreign aid, this brief and accessible book explains why the seemingly irresistible pressure to quantify performance distorts and distracts, whether by encouraging “gaming the stats” or “teaching to the test.” That’s because what can and does get measured is not always worth measuring, may not be what we really want to know, and may draw effort away from the things we care about. Along the way, we learn why paying for measured performance doesn’t work, why surgical scorecards may increase deaths, and much more. But metrics can be good when used as a complement to—rather than a replacement for—judgment based on personal experience, and Muller also gives examples of when metrics have been beneficial

Complete with a checklist of when and how to use metrics, The Tyranny of Metrics is an essential corrective to a rarely questioned trend that increasingly affects us all.

I appreciate it when thoughtful people courageously challenge popular opinion by questioning what we blindly assume is good. It is the rare individual who struggles to row against the current. It is in this direction that we must set our course, however, when the wellspring of truth is located upstream.

Many skilled professionals who work with metrics already recognize ways in which metrics do harm when they are ill-defined, inappropriately chosen, improperly measured, or misapplied. If you’re one of these professionals, this book will help you make your concerns heard above the din that keeps your organization distracted and confused. This is a welcome voice of sanity in a world that worships data but seldom uses it meaningfully and skillfully.

Tony Stark is Not a Real Dude

March 2nd, 2018

The world that has emerged from the imagination of Stan Lee and his Marvel Comics colleagues is great fun. In recent years, DeadPool has become my new favorite superhero, with Wolverine close on his heels. Today, however, I want to talk about another Marvel superhero—Iron Man—or more specifically about Tony Stark, the man encased on that high-tech armor.

It’s important that, when we consider fictional characters like this wealthy high-tech entrepreneur and inventor, we clearly distinguish fantasy from reality. Tony Stark isn’t real. Furthermore, no one like Tony Stark actually exists. You know this, right? Our best and brightest high-tech moguls—Bill Gates of Microsoft, Elon Musk of Tesla and SpaceX, Larry Page and Sergey Brin of Google, Mark Zuckerberg of Facebook, and even the late Steve Jobs of Apple—don’t come close to the abilities of Tony Stark. No one does and no one can. Even if we combined all of these guys together into a single person and threw in a top scientist such as Stephen Hawking into the mix, we still wouldn’t have someone who could do what Tony Stark not only does but does with apparent ease.

What am I getting at? There is a tendency today to believe that high-tech entrepreneurs and their inventions are much more advanced than they actually are. High-tech entrepreneurs and their inventions are buggy as hell. Most high-tech products are poorly designed. Even though good technologies can and do provide wonderful benefits, they are not magical in the ways that Marvel’s universe or high-tech marketers suggest. Technologies cannot always swoop in and save us in the nick of time. Furthermore, technologies are not intrinsically good as their advocates often suggest.

We should pursue invention, always looking for that next tool that will extend our reach in useful ways, but we should not bet our future on technological solutions. We dare not allow the Doomsday Clock to approach midnight hoping for a last second invention that will turn back time. We must face the future with a more realistic assessment of our abilities and the limitations of technologies.

Earlier today, in his state of the nation address, Vladimir Putin announced the latest in Russian high-tech innovation: nuclear projectiles that cannot be intercepted. Assuming that his claim is true, and it probably is, Putin has just placed himself and Russia at the top of the potential threats list. A bully with the ability to destroy the world brings back frightening memories of my youth when we had to perform duck-and-cover drills, trusting that those tiny metal and wooden desks would shield us from a nuclear assault.

There is no Tony Stark to save us. Iron Man won’t be paying a visit to Putin to put that bully in his place. As I was reading the news story about Putin’s announcement in the Washington Post this morning, an ad appeared in the middle of the text with a photo of Taylor Swift and the caption “Look Inside Taylor Swift’s New $18 Million NYC Townhouse.” A news story that is the stuff of nightmares is paired with celebrity fluff, lulling us into complacency. If the news story makes you nervous, you can easily escape into Taylor’s luxurious abode and pull her 10,000 thread count satin sheets over your head. Perhaps we have nothing to fear, for our valiant and brave president, Donald Trump, will storm the Kremlin and take care of Putin with his bare hands if necessary (after the hugging is over, of course), just as he would have dispatched that high school shooter in Parkland, Florida.

We have every reason to believe in his altruism and utter superiority in a fight, don’t we? Anything less would be fake news.

Ah, but I digress. I was distracted by the allure of Taylor Swift and her soft sheets. Where was I? Oh yeah, Tony Stark is not a real dude. If we hope to survive, let alone thrive, we’ll need to focus on building character, improving our understanding of the world, and making some inconvenient decisions. Technologies will play a role, but they aren’t the main actors in this real-world drama. We are.

Big Data, Big Dupe: A Progress Report

February 23rd, 2018

My new book, Big Data, Big Dupe, was published early this month. Since its publication, several readers have expressed their gratitude in emails. As you can imagine, this is both heartwarming and affirming. Big Data, Big Dupe confirms what these seasoned data professionals recognized long ago on their own, and in some cases have been arguing for years. Here are a few excerpts from emails that I’ve received:

I hope your book is wildly successful in a hurry, does its job, and then sinks into obscurity along with its topic.  We can only hope! 

I hope this short book makes it into the hands of decision-makers everywhere just in time for their budget meetings… I can’t imagine the waste of time and money that this buzz word has cost over the past decade.

Like yourself I have been doing business intelligence, data science, data warehousing, etc., for 21 years this year and have never seen such a wool over the eyes sham as Big Data…The more we can do to destroy the ruse, the better!

I’m reading Big Data, Big Dupe and nodding my head through most of it. There is no lack of snake oil in the IT industry.

Having been in the BI world for the past 20 years…I lead a small (6 to 10) cross-functional/cross-team collaboration group with like-minded folks from across the organization. We often gather to pontificate, share, and collaborate on what we are actively working on with data in our various business units, among other topics.  Lately we’ve been discussing the Big Data, Big Dupe ideas and how within [our organization] it has become so true. At times we are like ‘been saying this for years!’…

I believe deeply in the arguments you put forward in support of the scientific method, data sensemaking, and the right things to do despite their lack of sexiness.

As the title suggests, I argue in the book that Big Data is a marketing ruse. It is a term in search of meaning. Big Data is not a specific type of data. It is not a specific volume of data. (If you believe otherwise, please identify the agreed-upon threshold in volume that must be surpassed for data to become Big Data.) It is not a specific method or technique for processing data. It is not a specific technology for making sense of data. If it is none of these, what is it?

The answer, I believe, is that Big Data is an unredeemably ill-defined and therefore meaningless term that has been used to fuel a marketing campaign that began about ten years ago to sell data technologies and services. Existing data products and services at the time were losing their luster in public consciousness, so a new campaign emerged to rejuvenate sales without making substantive changes to those products and services. This campaign has promoted a great deal of nonsense and downright bad practices.

Big Data cannot be redeemed by pointing to an example of something useful that someone has done with data and exclaiming “Three cheers for Big Data,” for that useful thing would have still been done had the term Big Data never been coined. Much of the disinformation that’s associated with Big Data is propogated by good people with good intentions who prolong its nonsense by erroneously attributing beneficial but unrelated uses of data to it. When they equate Big Data with something useful, they make a semantic connection that lacks a connection to anything real. That semantic connection is no more credible than attributing a beneficial use of data to astrology. People do useful things with data all the time. How we interact with and make use of data has been gradually evolving for many years. Nothing that is qualitatively different about data or its use emerged roughly ten years ago to correspond with the emergence of the term Big Data.

Although no there is no consensus about the meaning of Big Data, one thing is certain: the term is responsible for a great deal of confusion and waste.

I read an article yesterday titled “Big Data – Useful Tool or Fetish?” that exposes some failures of Big Data. For example, it cites the failed $200,000,000 Big Data initiative of the Obama administration. You might think that I would applaud this article, but I don’t. I certainly appreciate the fact that it recognizes failures associated with Big Data, but its argument is logically flawed. Big Data is a meaningless term. As such, Big Data can neither fail nor succeed. By pointing out the failures of Big Data, this article endorses its existence, and in so doing perpetuates the ruse.

The article correctly assigns blame to the “fetishization of data” that is promoted by the Big Data marketing campaign. While Big Data now languishes with an “increasingly negative perception,” the gradual growth of skilled professionals and useful technologies continue to make good uses of data, as they always have.

Take care,

P.S. On March 6th, Stacey Barr interviewed me about Big Data, Big Dupe. You can find an audio recording of the interview on Stacey’s website.

Different Tools for Different Tasks

February 19th, 2018

I am often asked a version of the following question: “What data visualization product do you recommend?” My response is always the same: “That depends on what you do with data.” Tools differ significantly in their intentions, strengths, and weaknesses. No one tool does everything well. Truth be told, most tools do relatively little well.

I’m always taken by surprise when the folks who ask me for a recommendation fail to understand that I can’t recommend a tool without first understanding what they do with data. A fellow emailed this week to request a tool recommendation, and when I asked him to describe what he does with data, he responded by describing the general nature of the data that he works with (medical device quality data) and the amount of data that he typically accesses (“around 10k entries…across multiple product lines”). He didn’t actually answer my question, did he? I think this was, in part, because he and many others like him don’t think of what they do with data as consisting of different types of tasks. This is a fundamental oversight.

The nature of your data (marketing, sales, healthcare, education, etc.) has little bearing on the tool that’s needed. Even the quantity of data has relatively little effect on my tool recommendations unless you’re dealing with excessively large data sets. What you do with the data—the tasks that you perform and the purposes for which you perform them—is what matters most.

Your work might involve tasks that are somewhat unique to you, which should be taken into account when selecting a tool, but you also perform general categories of tasks that should be considered. Here are a few of those general categories:

  • Exploratory data analysis (Exploring data in a free-form manner, getting to know it in general, from multiple perspectives, and asking many questions to understand it)
  • Rapid performance monitoring (Maintaining awareness of what’s currently going on as reflected in a specific set of data to fulfill a particular role)
  • A routine set of specific analytical tasks (Analyzing the data in the same specific ways again and again)
  • Production report development (Preparing reports that will be used by others to lookup data that’s needed to do their jobs)
  • Dashboard development (Developing displays that others can use to rapidly monitor performance)
  • Presentation preparation (Preparing displays of data that will be presented in meetings or in custom reports)
  • Customized analytical application development (Developing applications that others will use to analyze data in the same specific ways again and again)

Tools that do a good job of supporting exploratory data analysis usually do a poor job of supporting the development of production reports and dashboards, which require fine control over the positioning and sizing of objects. Tools that provide the most flexibility and control often do so by using a programming interface, which cannot support the fluid interaction with data that is required for exploratory data analysis. Every tool specializes in what it can do well, assuming it can do anything well.

In addition to the types of tasks that we perform, we must also consider the level of sophistication to which we peform them. For example, of you engage in exploratory data analysis, the tool that I recommend would vary significantly depending on the depth of your data analysis skills. For instance, I wouldn’t recommend a complex statistical analysis product such as SAS JMP if you’re untrained in statistics, just as I wouldn’t recommend a general purpose tool such as Tableau Software if you’re well trained in statistics, except for performing statistically lightweight tasks.

Apart from the tasks that we perform and the level of skill with which we perform them, we must also consider the size of our wallet. Some products require a significant investment to get started, while others can be purchased for an individual user at little cost or even downloaded for free.

So, what tool do I recommend? It depends. Finding the right tool begins with a clear understanting of what you need to do with data and with your ability to do it.

Take care,

Framing AI as Life

January 31st, 2018

George Lakoff introduced the concept of “framing” in his book Metaphors We Live By. The terms and metaphors that we use to describe things serve as frames that influence our perceptions and our values. In his book, Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark frames future artificial intelligence (AI) as life.

Before proceeding, I should say that I appreciate much of Tegmark’s work. He is one of the few people involved in AI who are approaching the work thoughtfully and carefully. He is striving to safeguard AI development to support the interests of humanity. For this, I am immensely grateful. I believe that framing future AI as life, however, is inappropriate and at odds with the interests of humanity.

The version metaphor (1.0, 2.0, etc.), borrowed from the realm of software development, has been used in recent years to describe new stages in the development of many things. You’re probably familiar with the “Web 2.0” metaphor that Tim O’Reilly introduced several years ago. As the title if his book suggests, Tegmark refers to an imagined future of machines with general intelligence that matches or surpasses our own as “Life 3.0.” How could computers, however intelligent or even sentient, be classified as a new version of life? This is only possible by redefining what we mean by life. Here’s Tegmark’s definition:

Let’s define life very broadly, simply as a process that can retain its complexity and replicate…In other words, we can think of life as a self-replicating information-processing system whose information (software) determines both its behavior and the blueprints for its hardware.

According to Tegmark, Life 1.0 consisted of all biological organisms prior to humans. It was entirely governed by its DNA. Life 2.0 arose in humans as the ability to alter brain function. Our brains can be rewired to adapt and improve, free from the strict confines of genetic determinism. Life 1.0 was completely determined by its “hardware” (biology only), but Life 2.0 introduced the ability to rewrite its “software” (biology plus culture). Life 3.0, as Tegmark imagines it, will introduce the ability to go beyond rewriting its software to redesigning its hardware as well, resulting in unlimited adaptability. As he frames it, life proceeds from the biological (1.0) to the cultural (2.0) and eventually to the technological (3.0).

Notice how this frame ignores fundamental differences between organisms and machines, and in so doing alters the definition of life. According to the definition that you’ll find in dictionaries, plants and animals—organic entities—are alive; rocks, steel girders, and machines—inorganic entities—are not alive. A computer program that can spawn a copy of itself and place it on improved hardware might correctly be deemed powerful, but not alive.

Why does Tegmark argue that intelligent machines of the future would constitute life? He gives a hint when he writes,

The question of how to define life is notoriously controversial. Competing definitions abound, some of which include highly specific requirements such as being composed of cells, which might disqualify both future intelligent machines and extraterrestrial civilizations. Since we don’t want to limit our thinking about the future of life to the species that we’ve encountered so far, let’s instead define life very broadly…

Indeed, definitions are often controversial when we scrutinize them deeply. This is because concepts—the boundaries that we create to group and separate things in our efforts to make sense of the world—are always somewhat arbitrary, but these concepts make abstract thinking and communication possible. Responding to the complexity of definitions by excessively broadening them undermines their usefulness.

Tegmarks seems to be concerned that we would only value and embrace future AI if we classified it as living. Contrary to his concern, maintaining our existing definition of life would not prevent us from discovering new forms in the future. We can imagine and could certainly welcome biological organisms that are quite different from those that are already familiar. It is true, however, that keeping the definition of life firmly tied to biology would certainly and appropriately lead us to classify some newly discovered entities as something other than life. This needn’t concern Tegmark, for we value much that isn’t alive and devalue much that is alive. If super-intelligent AIs ever come into existence, we should think of them as similar to us in some ways and different in others, which is sensible, and we should value them to the degree that they are beneficial, not to the degree to which they qualify as life.

You might think that this is much ado about nothing. Why should we care if the definition of life is stretched to include machines? I care for two reasons: 1) in general, we should create and revise definitions more thoughtfully, and 2) specific to AI, we should recognize that machines are different from us in fundamental ways. To the first point, concepts, encapsulated in definitions, form our perceptions, and how we perceive things largely determines the quality of our lives and the utility of our decisions. To the second point, we dare not forget that the interests of a super-intelligent AI would be very different from our own. Recognizing the ways in which these potential machines of the future would be different from us will serve as a critical reminder that we must approach their development with care.

Tegmark states in the title of his book’s first chapter that AI is “the most important conversation of our time.” This is probably not the most important conversation of our time, but it is certainly important. I’m sharing my concerns as a part of this conversation. If we ever manage to equip computers with intelligence that equals our own, their faster processing speeds and greater storage capacities will enable them to rapidly achieve a level of intelligence that leaves us in the dust. That might justify the designation “Intelligence 3.0,” but not “Life 3.0.” I suggest that we frame super-intelligent AI in this manner instead.

Take care,

Only a Summary

January 30th, 2018

While listening to NPR today, I heard a Republican congressman say that we shouldn’t be concerned about the release of sensitive intelligence in the so-called “Nunes Memo,” which alleges abuses by the FBI and Justice Department in their investigation of Russian interference in the presidential election. Why should we not be concerned? Because the Nunes Memo is “just a summary.” When I heard this I let out an involuntary exclamation of exasperation. This congressman is either naïve or intentionally deceitful in this assessment—probably both.

Anyone who works with data knows that summaries are especially subject to bias and manipulation. Even raw data is biased to some degree, but summaries are much more so, for they are highly subjective and interpretive. This congressman argued that no harm could possibly be done by releasing this summary and allowing members of the general public to assess its merits for themselves. It isn’t possible, of course, to evaluate the merits of the summary without examining the source data on which it is based. The source data, however, is being withheld from the public.

It’s ironic that alleged biases exhibited by some members of the intelligence committee are being countered by an obviously biased, politically motivated summary of those biases. The fact that Republicans are refusing to make public an alternative summary of the data that was prepared by Democrats reveals the obvious political motivation behind the action. Republicans in Congress believe that the public is incredibly stupid. I hope they’re wrong, but ignorance about data and the lack of skills that are needed to make sense of it are indeed rife. What a joke that we live in the co-called “information age.” It is probably more accurate to say that we live in the “misinformation age.”

Take care,

Scholarly Peer Reviews Must Involve Experts

January 23rd, 2018

The manner in which scholarly peer reviews are being performed in some settings today is not serving as an effective gatekeeper. Peer review is supposed to filter out invalid or otherwise inadequate work and to encourage good work. Most of the poor work that is being produced could be blocked from publication if the peer review system functioned as intended.

Although some historical instances of peer review can be traced back to the 18th century, it didn’t become a routine and formal part of the scholarly publication process until after World War II. With dramatic increases in the production of scholarly content by the mid-20th century, a means of filtering out inadequate work became imperative.

According to Wikipedia,

Scholarly peer review (also known as refereeing) is the process of subjecting an author’s scholarly work, research, or ideas to the scrutiny of others who are experts in the same field, before a paper describing this work is published in a journal, conference proceedings or as a book. The peer review helps the publisher…decide whether the work should accepted, considered acceptable with revisions, or rejected.

Peer review requires a community of experts in a given (and often narrowly defined) field, who are qualified and able to perform reasonably impartial review.

Scholarly publications, such as academic journals, are only useful if the claims within them are credible. As such, the peer review process performs a vital role. When the process was first established, it was called peer review based on the assumption that those who produced scholarly work were experts in the relevant field. An expert’s peers are other experts. A “community of experts” is essential to peer review.

Over time, in some fields of study, the production of scholarly work has increasingly involved students who are still fairly early in the process of developing expertise. Corresponding with this transition, peer reviewers also increasingly lack expertise. During their advanced studies, it is absolutely useful for students to be involved in research and the production of scholarly work, but this work should not be published based solely on reviews by their peers. Reviews from anyone who’s interested in the subject matter can potentially provide useful feedback to an author, but only reviews by experts can support the objectives of the peer review process.

Characterizing this problem strictly as one that stems from the involvement of students is not entirely accurate. Scholarly work that is submitted for publication is rarely authored by students alone. Almost always, a professor’s name is attached to the work as well. Unfortunately, even if we assume that a professor is an expert in something, we cannot assume expertise in the domain addressed by the work that’s submitted for publication. In my own field of data visualization, many professors who teach courses and do research in data visualization lack expertise in fundamental aspects of the field. For example, it is not uncommon for professors to focus solely on the development of data visualization software with little or no knowledge of the scientific foundations of data visualization theory or actual experience in the practice of data visualization. One of the first times that I became aware of this, much to my surprise, was when the professor who introduced me when I gave a keynote presentation at the Vis Week Conference a decade ago admitted to me privately that he had little knowledge of data visualization best practices.

Do you know how the expertise of peer reviewers is often determined? Those who apply to participate in the process rate themselves. On every occasion when I participated in the process, I completed a questionnaire that asked me to rate my own level of expertise in various domains. There are perhaps exceptions to this self-rating approach—I certainly hope so—but this appears to be typical in the domains of data visualization, human-computer interaction, and even statistics.

Something is amiss in the peer review process. As long as people who lack expertise are deciding which scholarly works to accept or reject for publication, the quality of published work will continue be unreliable. We dare not forget the importance of expertise.

Take care,