Ethical persuasion profiling?

Persuasion profiling — estimating the effects of available influence strategies on an individual and adaptively selecting the strategies to use based on these estimates — sounds a bit scary. For many, ‘persuasion’ is a dirty word and ‘profiling’ generally doesn’t have positive connotations; together they are even worse! So why do we use this label?

In fact, Maurits Kaptein and I use this term, coined by BJ Fogg, precisely because it sounds scary. We see the potential for quite negative consequences of persuasion profiling, so we try to alert our readers to this.1

On the other hand, we also think that, not only is persuasion profiling sometimes beneficial, but there are cases where choosing not to adapt to individual differences in this way might itself be unethical.2 If a company marketing a health intervention knows that there is substantial variety in how people respond to the strategies used in the intervention — such that while the intervention has positive effects on average, it has negative effects for some — it seems like they have two ethical options.

First, they can be honest about this in their marketing, reminding consumers that it doesn’t work for everyone or even trying to market it to people it is more likely to work for. Or they could make this interactive intervention adapt to individuals — by persuasion profiling.

Actually for the first option to really work, the company needs to at least model how these responses vary by observable and marketable-to characteristics (e.g., demographics). And it may be that this won’t be enough if there is too much heterogeneity: even within some demographic buckets, the intervention may have negative effects for a good number of would-be users. On the other hand, by implementing persuasion profiling, the intervention will help more people, and the company will be able to market it more widely — and more ethically.

A simplified example that is somewhat compelling to me at least, but certainly not airtight. In another post, I’ll describe how somewhat foreseeable, but unintended, consequences should also give one pause.

  1. You might say we tried to build in a warning for anyone discussing or promoting this work. []
  2. We argue this in the paper we presented at Persuasive Technology 2010. The text below reprises some of what we said about our “Example 4” in that paper.
    Kaptein, M. & Eckles, D. (2010). Selecting effective means to any end: Futures and ethics of persuasion profiling. Proceedings of Persuasive Technology 2010, Lecture Notes in Computer Science. Springer. []

Traits, adaptive systems & dimensionality reduction

Psychologists have posited numerous psychological traits and described causal roles they ought to play in determining human behavior. Most often, the canonical measure of a trait is a questionnaire. Investigators obtain this measure for some people and analyze how their scores predict some outcomes of interest. For example, many people have been interested in how psychological traits affect persuasion processes. Traits like need for cognition (NFC) have been posited and questionnaire items developed to measure them. Among other things, NFC affects how people respond to messages with arguments for varying quality.

How useful are these traits for explanation, prediction, and adaptive interaction? I can’t address all of this here, but I want to sketch an argument for their irrelevance to adaptive interaction — and then offer a tentative rejoinder.

Interactive technologies can tailor their messages to the tastes and susceptibilities of the people interacting with and through them. It might seem that these traits should figure in the statistical models used to make these adaptive selections. After all, some of the possible messages fit for, e.g., coaching a person to meet their exercise goals are more likely to be effective for low NFC people than high NFC people, and vice versa. However, the standard questionnaire measures of NFC cannot often be obtained for most users — certainly not in commerce settings, and even people signing up for a mobile coaching service likely don’t want to answer pages of questions. On the other hand, some Internet and mobile services have other abundant data available about their users, which could perhaps be used to construct an alternative measure of these traits. The trait-based-adaptation recipe is:

  1. obtain the questionnaire measure of the trait for a sample,
  2. predict this measure with data available for many individuals (e.g., log data),
  3. use this model to construct a measure for out-of-sample individuals.

This new measure could then be used to personalize the interactive experience based on this trait, such that if a version performs well (or poorly) for people with a particular score on the trait, then use (or don’t use) that version for people with similar scores.

But why involve the trait at all? Why not just personalize the interactive experience based on the responses of similar others? Since the new measure of the trait is just based on the available behavioral, demographic, and other logged data, one could simply predict responses based on those measure. Put in geometric terms, if the goal is to project the effects of different message onto available log data, why should one project the questionnaire measure of the trait onto the available log data and then project the effects onto this projection? This seems especially unappealing if one doesn’t fully trust the questionnaire measure to be accurate or one can’t be sure about which the set of all the traits that make a (substantial) difference.

I find this argument quite intuitively appealing, and it seems to resonate with others.1 But I think there are some reasons the recipe above could still be appealing.

One way to think about this recipe is as dimensionality reduction guided by theory about psychological traits. Available log data can often be used to construct countless predictors (or “features”, as the machine learning people call them). So one can very quickly get into a situation where the effective number of parameters for a full model predicting the effects of different messages is very large and will make for poor predictions. Nothing — no, not penalized regression, not even a support vector machine — makes this problem go away. Instead, one has to rely on the domain knowledge of the person constructing the predictors (i.e., doing the “feature engineering”) to pick some good ones.

So the tentative rejoinder is this: established psychological traits might often make good dimensions to predict effects of different version of a message, intervention, or experience with. And they may “come with” suggestions about what kinds of log data might serve as measures of them. They would be expected to be reusable across settings. Thus, I think this recipe is nonetheless deserves serious attention.

  1. I owe some clarity on this to some conversations with Mike Nowak and Maurits Kaptein. []

Persuasion profiling and genres: Fogg in 2006

Maurits Kaptein and I have recently been thinking a lot about persuasion profiling — estimating and adapting to individual differences in responses to influence strategies based on past behavior and other information. With help from students, we’ve been running experiments and building statistical models that implement persuasion profiling.

My thinking on persuasion profiling is very much in BJ Fogg’s footsteps, since he has been talking about persuasion profiling in courses, lab meetings, and personal discussions since 2004 or earlier.

Just yesterday, I came across this transcript of BJ’s presentation for an FTC hearing in 2006. I was struck at how much it anticipates some of what Maurits and I have written recently (more on this later). I’m sure I watched the draft video of the presentation back then and it’s influenced me, even if I forgot some of the details.

Here is the relevant excerpt from BJ’s comments for the FTC:

Persuasion profiling means that each one of us has a different set of persuasion strategies that affect us. Just like we like different types of food or are vulnerable to giving in to different types of food on a diet, we are vulnerable to different types of persuasion strategies.

On the food example, I love old-fashioned popcorn, and if I go to a party and somebody has old-fashioned popcorn, I will probably break down and eat it. On the persuasion side of things, I know I’m vulnerable to trying new things, to challenges and to anything that gets measured. If that’s proposed to me, I’m going to be vulnerable and I’m going to give it a shot.

Whenever we go to a Web site and use an interactive system, it is likely they will be capturing what persuasion strategies work on us and will be using those when we use the service again. The mapping out of what makes me tick, what motivates me can also be bought or sold, just like a credit report.

So imagine I’m going in to buy a new car and the person selling me the car downloads my credit report but also buys my persuasion profile. I may or may not know about this. Imagine if persuasion profiles are available on political campaigns so that when I visit a Web site, the system knows it is B.J. Fogg, and it changes [its] approach based on my vulnerabilities when it comes to persuasion.

Persuasive technology will touch our lives anywhere that we access digital products or services, in the car, in our living room, on the Web, through our mobile phones and so on. Persuasive technology will be all around us, and unlike other media types, where you have 30-second commercial or a magazine ad, you have genres you can understand, when it comes to computer-based persuasion, it is so flexible that it won’t have genre boundaries. It will come to us in the ordinary course of our lives, as we are working on a Web site, as we are editing a document, as we are driving a car. There won’t be clear markers about when you are being persuaded and when you are not.

This last paragraph is about the “genrelessness” of many persuasive technologies. This isn’t directly on the topic of persuasion profiling, but I see it as critically relevant. Persuasion profiling is likely to be most effective when invisible and undisclosed to users. From this and the lack of genre-based flags for persuasive technology it follows that we will frequently be “persuasion profiled” without knowing it.

Using social networks for persuasion profiling

BusinessWeek has an exhuberant review of current industry research and product development related to understanding social networks using data from social network sites and other online communication such as email. It includes snippets from people doing very interesting social science research, like Duncan Watts, Cameron Marlow, and danah boyd. So it is worth checking out, even if you’re already familiar with the Facebook Data Team’s recent public reports (“Maintained Relationships”, “Gesundheit!”).

But I actually want to comment not on their comments, but on this section:

In an industry where the majority of ads go unclicked, even a small boost can make a big difference. One San Francisco advertising company, Rapleaf, carried out a friend-based campaign for a credit-card company that wanted to sell bank products to existing customers. Tailoring offers based on friends’ responses helped lift the average click rate from 0.9% to 2.7%. Although 97.3% of the people surfed past the ads, the click rate still tripled.

Rapleaf, which has harvested data from blogs, online forums, and social networks, says it follows the network behavior of 480 million people. It furnishes friendship data to help customers fine-tune their promotions. Its studies indicate borrowers are a better bet if their friends have higher credit ratings. This might mean a home buyer with a middling credit risk score of 550 should be treated as closer to 600 if most of his or her friends are in that range, says Rapleaf CEO Auren Hoffman.

The idea is that since you are more likely to behave like your friends, their behavior can be used to profile you and tailor some marketing to be more likely to result in compliance.

In the Persuasive Technology Lab at Stanford University, BJ Fogg has long emphasized how powerful and worrying personalization based on this kind of “persuasion profile” can be. Imagine that rather than just personalizing screens based on the books you are expected to like (a familiar idea), Amazon selects the kinds of influence strategies used based on a representation of what strategies work best against you: “Dean is a sucker for limited-time offers”, “Foot-in-the-door works really well against Domenico, especially when he is buying a gift.”

In 2006 two of our students, Fred Leach and Schuyler Kaye, created this goofy video illustrating approximately this concept:

My sense is that this kind of personalization is in wide use at places like Amazon, except that their “units of analysis/personalization” are individual tactics (e.g., Gold Box offers), rather than the social influence strategies that can be implemented in many ways and in combination with each other.

What’s interesting about the Rapleaf work described by BusinessWeek is that this enables persuasion profiling even before a service provider or marketer knows anything about you — except that you were referred by or are otherwise connected to a person. This gives them the ability to estimate your persuasion profile by using your social neighborhood, even if you haven’t disclosed this information about your social network.

While there has been some research on individual differences in responses to influence strategies (including when used by computers), as far as I know there isn’t much work on just how much the responses of friends covary. As a tool for influencers online, it doesn’t matter as much whether this variation explained by friends’ responses is also explained by other variables, as long as those variables aren’t available for the influencers to collect. But for us social scientists, it would be interesting to understand the mechanism by which there is this relationship: is it just that friends are likely to be similar in a bunch of ways and these predict our “persuasion profiles”, or are the processes of relationship creation that directly involve these similarities.

This is an exciting and scary direction, and I want to learn more about it.