Implicit Bias | Part 4: Ten Debiasing Strategies

At this point it’s pretty clear why someone would be worried about bias. We’re biased (Part 1). Consciously suppressing our biases might not work (Part 2).  And our bias seems to tamper with significant, real-world decisions (Part 3). So now that we’re good and scared, let’s think about what we can do. Below are more than 10 debiasing strategies that fall into 3 categories: debiasing our stereotypes, debiasing our environment, and debiasing our decision procedures.

1.  Debiasing our stereotypes

In the last post, we learned that implicit attitudes and stereotypes can badly affect our judgments. Here is how stereotypes can be formed and reformed.

Conditioning

In conditioning, we repeatedly present ourselves with a pair of stimuli until we begin to associate one thing with the other (De Houwer, Thomas, and Baeyens 2001; Hofmann, De Houwer, Perugini, Baeyens, and Crombez 2010). So, for example, if someone consumes media that repeatedly presents certain ideas or groups in a negative light, then they will cultivate a negative implicit attitude about these ideas or groups (Arendt 2013; Arendt & Northup 2015; Matthes & Schmuk 2015). Or if a certain profession is dominated by white men, then people will associate membership in this profession with being white and male — and this might have a self-reinforcing effect on the profession’s makeup.

Counterconditioning

We can also use conditioning against our existing biases. Some call this counterconditioning (Stewart, Latu, Kawakami, and Myers 2010; VansteenWagen, Baeyens, and Hermans 2015). One fairly easy and effective way to countercondition a negative stereotype is just to imagine a positive version of a negatively valence concept (Blair, Ma, and Lenton 2001, Markland et al 2015).

Here are examples of how we might use counterconditioning. When nominating, recommending, or choosing someone for an opportunity, we might alot some time to think of non-stereotypical candidates from underrepresented groups. If we’re teaching, then we might assign material from underrepresented groups. If we’re an office manager, then we might choose imagery for the office that represents a more diverse group of people.

2.  Debiasing our environment

It turns out that altering the decision environment can also support debiasing. So if you have a say in the decision environment, then think about the following.

Ask for justification

Put decision-makers in a position that forces them to justify their decisions to a third-party (Lerner & Tetlock 1999, Simonson & Nye 1992). The point of this is to prevent overconfidence in our judgment, e.g., optimism bias (Ben-David, Graham, and Harvey 2013, Moore & Healy 2008).

Consider alternatives

Try to diversify the decision-makers (Bell, Villado, Lukasik, Belau, and Briggs 2011; Shor, Rijt, Miltsov, Kulkarni, and Skiena 2015). The hope is that your intuitions will be challenged. After all, “Training in normative rules often fails when people have strong intuitions and do not pause to think more deeply (McKenzie and Liersch 2011)” (Soll, Milkman, and Payne 2014). So find people who question our intuitions (Koriat, Lichtenstein & Fischhoff 1980). If you can’t find someone to disagree with you, disagree with yourself: simply imagine arguments for different conclusions (Herzog and Hertwig 20092014; Keeney 2012).

Make information easier to consume

Remove irrelevant data, e.g.,  a job candidate’s identity, the status quo, unnecessary complexity, etc. (Célérier & Vallée 2014; Thaler & Sunstein 2008).  And present the remaining data in a way that allows for easy comparison (Heath & Heath, 2010; Hsee 1996, Russo 1977). And present the data in the most relevant scale (Camilleri & Larrick 2014; Burson, Larrick, & Lynch, 2009; Larrick and Soll 2008). Talk about probabilities in terms of relative frequencies or, better yet, represent probabilities with visualizations [examples] (Fagerlin, Wange, and Ubel 2005; Galesic, Garcia- Retamero, & Gigerenzer 2009; Hoffrage, Lindsey, Hertwig, and Gigerenzer 2000).

3.  Debiasing our procedures

Certain reasoning strategies can help you with debiasing (e.g., Larrick 2004; Fong and Nisbett 1991, Larrick, Morgan, and Nisbett 1990). Here are some ways that we might debias our decision procedures.

First, decision-procedures checklists, criteria, and rubrics. Use them, especially if you are repeatedly making one kind of judgment, e.g., grading, reviewing applications, etc. (Gawande 2010; Heath, Larrick, and Klayman 1998; Hales & Pronovost 2006).

Second, quantitative models. Predictive linear models often do better than our own reasoning (Dawes 1979 and Dawes, Faust, & Meehl, 1989; Bishop & Trout 2008). So if we have a good model by which to make a decision, then we should be wary of deviating from the model until we have good reason to do so.

Finally, the status quo. We tend towards the status quo. So if it is already well-known that a particular choice optimizes our desired outcome(s), then default to that choice (Chapman, Li, Colby, and Yoon 2010; Johnson, Bellman, and Lohse 2002; Johnson & Goldstein 2003; Madrian & Shea 2001; Sunstein 2015). And — going back to section 2 — if people want to opt-out of the optimized status quo, then ask them for justification. 😉

Conclusion

As you can see, there are many debiasing tools in our toolkit. We can debias our stereotypes, our environment, and our procedures. While these tools have been shown to help, they might not entirely solve the problem. And there maybe be at least one more option at our disposal: feedback. That will be the topic of Part 5.

Series Outline

Part 1: I talk about the anxiety I had about being implicitly biased. “Am I discriminating without even knowing it?! How do I stop?!”

Part 2: What is implicit bias? Check out this post to learn about the theory and some of the findings.

Part 3: What are the real-world consequences of implicit bias? Research suggests implicit bias can impact hiring, pay, promotions, and more.

Part 4: [Jump to the top]

Part 5: Can feedback help? What if people are not used to giving each other feedback about their biases. Check out how to give and encourage feedback about our biases.

Implicit Bias | Part 3: Workplace Bias


Think about decisions that people make every day. A committee decides who to hire. A supervisor rates an employee’s performance. A teacher grades a student’s assignment. A jury arrives at a verdict. A Supreme Court judge casts their vote. An emergency medical technician decides which victim to approach first. A police officer decides whether to shoot. These are instances in which workplace bias can have significant consequences.

I won’t be able to highlight every area of research on workplace bias. So I cannot delve into the findings that police officers’ sometimes show racial bias in decisions to shoot (Sim, Correll, and Sadler 2013, Experiment 2; see Correll et al 2007, Ma and Correll 2011 Study 2 for findings that indicate no racial bias). And I cannot go into detail about how all-white juries are significantly more likely than other juries to convict black defendants (Anwar, Bayer, Hjalmarsson 2012).

GENDER BIAS AT WORK

Instead, I’ll focus on the instances of workplace bias to which most people can relate. If you’re like most people, then you need to work to live, right? So let’s talk about how bias can affect our chances of being hired. Continue reading Implicit Bias | Part 3: Workplace Bias

Implicit Bias | Part 2: What is implicit bias?

If our reasoning were biased, then we’d notice it, right? Not quite. We are conscious of very few (if any) of the processes that influence our reasoning. So, some processes bias our reasoning in ways that we do not always endorse. This is sometimes referred to as implicit bias. In this post, I’ll talk about the theory behind our implicit biases and mention a couple surprising findings.

The literature on implicit bias is vast (and steadily growing). So there’s no way I can review it all here. To find even more research on implicit bias, see the next two posts, the links in this series, and the links in the comments.† Continue reading Implicit Bias | Part 2: What is implicit bias?

Implicit Bias | Part 1: Bias Anxiety

The research on bias is kind of scary. It not only suggests that we are biased; It suggests that we are unaware of many of our biases. Further, it suggests that trying to suppress our biases can easily backfire. So, despite our best efforts, we could be doing harm. And yeah: that might provoke a bit of anxiety. That’ll be the topic of this post.

In future posts, I’ll talk about the theory behind our biases [Part 2], how bias impacts the workplace [Part 3], a dozen debiasing strategies from the research [Part 4], and a few tips for giving (and receiving) feedback about our biases [Part 5].

Related post: The Bias Fallacy (what it is and how to avoid it).

Continue reading Implicit Bias | Part 1: Bias Anxiety

Exercise, Neuroscience, and the Network Theory of Well-being


Michael Bishop outlines a network theory of well-being in which well-being is constituted by positive causal networks and their fragments (2012, 2015). ‘Positive’ refers to — among other things — experiences that have positive hedonic tones, the affirmation or fulfilment of one’s values, and success in achieving goals. So according to Bishop’s view, we flourish when certain positive causal networks are robust and self-reinforcing. For example, something good happens to us and that improves our motivation and mood, which then helps us achieve more, which improves our motivation and mood even more, and so on.

Bishop’s network account musters philosophical rigor by providing a systematic and coherent account of wellbeing that satisfies many common sense judgments about well-being. But lots of philosophical accounts can do that. So Bishop’s account does even more. It unifies and makes sense of a huge swath of the science. This provides some reason to think that Bishop’s account is superior to its competition.

So what’s this got to do with exercise and neuroscience?

1.  Neuroscience

I am largely persuaded by Bishop’s arguments for the network account of well-being, so I will skip my criticism of the project. Rather, I will add to it. Specifically, I will show how well is makes sense of the neuroscience. While I will not be able to review all of neuroscience, I can accomplish a more modest goal. I can review one part of neuroscience: the effect of exercise on the brain.

2.  Exercise

There is a wealth of evidence suggesting that regular physical activity and exercise forms an important part of one’s positive causal network of well-being by, among other things, increasing positive affect (Harte, Eifert, and Smith 1995), increasing confidence (Klem, Wing, McGuire, Seagle, and Hill 1997), reducing stress, relieving depression (Blumenthal et al 1999; Motl et al 2005) and preventing more than a dozen chronic diseases (Booth, Gordon, Carlson and Hamilton 2000; see also Biddle, Fox and Boutcher 2000 for a review of relationships between exercise and well-being). The mechanisms for all of these results are not entirely clear. But neuroscience is providing, in broad strokes at least, some clues about the mechanisms that can explain, in part, why exercise produces a series of positive effects in a well-being network (e.g., Meeusen 1995Farooqui 2014).

The Positive Effects in the Brain

Let’s start with how exercise produces direct positive effects in the brain. Firstly, exercise and regular physical activity directly improve the brain’s synaptic structure by improving potentiating synaptic strength (Cotman, Berchtold, Christie 2007). Secondly, exercise and regular physical activity strengthen systems that underlie neural plasticity—e.g., neurogenesis, the growth of new neural tissue (ibid., Praag et al 2014). These changes in the brain cause “growth factor cascades” which improve overall “brain health and function” (ibid.; Kramer and Erickson 2007).

Now consider how exercise has indirect positive effects in the brain by producing ancillary positive circumstances. Generally speaking, “exercise reduces peripheral risk factors for cognitive decline” by preventing—among other things—neurodegeneration, neurotrophic resistance, hypertension, and insulin resistance (ibid.; see also Mattson 2014). By preventing these threats to neural and cognitive health, exercise is, indirectly, promoting brain health and function.

Positive Causal Networks

It requires no stretch of the imagination to see how these positive effects will reinforce positive causal networks and thereby increase well-being. Even so, I will do you a favor by trying to demonstrate a connection between exercise, the brain, and the larger network of well-being.

We have already seen how exercise results in, among other things, increased plasticity. And increased plasticity results in improved learning (Geinisman 2000; Rampon and Tsien 2000). Also, the increased plasticity that results in improved learning can produce other positive outcomes: increased motivation, increased opportunities for personal relationships in learning environments, etc. (Zelazo and Carlson 2012, 358). Further, increased motivation and social capital can — coming full circle — result in further motivation (Wing and Jeffery 1999).

That right there is what we call a self-reinforcing positive causal network or positive feedback loop. And that, according to Bishop, is how we increase well-being (see figure 1).

Figure 1. Positive Causal Well-being Network. Exercise promotes outcomes in the brain that promote other positive outcomes outside the brain. Similarly, exercise reduces negatives outcomes that would reduce certain positive outcomes. This is adapted from causal network models found in Cotman, Berchtold, and Christie 2007.
Figure 1. Positive Causal Well-being Network. Exercise promotes outcomes in the brain that promote other positive outcomes outside the brain. Similarly, exercise reduces negatives outcomes that would reduce certain positive outcomes. This is adapted from causal network models found in Cotman, Berchtold, and Christie 2007.
This causal model shows how the neuroscience we just discussed implies a causal network. The nodes and causal connections in this model show how well-being is a matter of positive causal networks.

3.  What about Ill-being?

Obviously, I’ve only mentioned the neuroscience of well-being. But if we want to promote well-being, then we also have to decrease ill-being, right? Right. And once again, the network theory of well-being will fit nicely with the research on ill-being. For example, the research on emotion regulation (see Livingston et al 2015) implies some causal networks that can inhibit ill-being. The same can be said of the research about using deep brain stimulation in treatment-resistance depression (Bewernick et al 2010; Lozano et al 2008; Mayberg et al 2005; Neuner et al 2010).

4.  A Concern: Fitness

You might object by positing that Bishop’s theory of well-being will not fit neuroscience as well as it fits positive psychology. This objection can be dismissed in a few ways. Here are two ways.

First, we can safely accept that Bishop’s network theory of well-being will not fit neuroscience as well as it fits positive psychology. After all, Bishop’s network theory was designed to fit positive psychology, not neuroscience. It’s hardly a fault for a theory to not do what is was not intended to do.

Second, neuroscience is a larger domain than positive psychology. So of course it is harder for a theory to fit it. Allow me to explain. As the domain of discourse increases in scope, it becomes increasingly difficult for us to find a theory that fits all of it. So, because neuroscience is a larger domain than positive psychology, the challenge of providing a theory that fits neuroscience is always more difficult than providing a theory that fits positive psychology. So the fitness objection doesn’t necessarily reflect badly on Bishop’s theory. It might only reflect a difference between positive psychology and neuroscience.

Conclusion

Let me summarize. I mentioned a few cases in which Bishop’s theory of well-being can unifies and makes sense of neuroscience. Then I proposed a few more cases in which Bishop’s theory might do the same. And then I addressed a skeptical worry about the project I propose. So Bishop’s theory of well-being can accomplish even more than Bishop intended.

 

Image credit: “Blues Race Thru Belhaven” from Monumenteer2014, CC BY 2.0

Where Does “Bottom-up” Bottom Out?

(Image credit: “Microglia and Neurons” by GerryShaw licensed under CC BY 3.0)

‘Bottom-up’ and ‘top-down’ are staple concepts in cognitive science. These terms refer to more than one set of concepts, depending on the context. In this post, I want to talk about one version of ‘bottom-up’ and try to pin down what is at the “bottom” of cognition.

First, I should single out the meaning of ‘bottom-up’ that I have in mind. It is the one in which ‘bottom’ refers to the deterministic hardware and pre-conscious processes from which “higher level” processes like meaning, affect, and perhaps conscious awareness emerge. Continue reading Where Does “Bottom-up” Bottom Out?

Goals & Desires

Randy O’Reilly gave a talk at CU Boulder yesterday entitled “Goal-driven Cognition in the Brain:….” It was an excellent look at how goals have emerged in cognitive science and psychology and how goal-based models have improved upon previous behaviorist models. He also told a story about how goal-driven cognitive models can be grounded in neurobiology.1 There are two reasons I mention this talk. First, Randy’s talk convinced me that “goals” have a valuable place in the ontology of mental states. Second, his talk helped me realize an example that shows how goals and desires are dissociable. In this post, I will talk about this second item. Continue reading Goals & Desires