Statistics as Figleaves

Recently, Jennifer Saul (“Racial Figleaves, the Shifting Boundaries of the Permissible, and the Rise of Donald Trump”, 2017; “Racist and Sexist Figleaves”, 2021) has explored the use of what she calls “figleaves” in the discourse on race and gender. Following Saul, a figleaf is an utterance that, for some portion of the audience, blocks the conclusion that some other utter-ance, R , or the person who uttered R is racist or sexist. Such racial and gender figleaves are pernicious, says Saul, because, among other things, they can shift the boundaries of what is deemed acceptable to think or say. This paper expands on Saul’s picture in a threefold way: It is first argued that appeals to statistics can function as figleaves. It is then argued that—as far as figleaves go—there is reason to believe that statistics-as-figleaves are especially pernicious. Finally, the paper explores some strategies for counterspeech.


Figleaves
Arguably, norms against racism and sexism are operative in many societies (cf.Mendelberg 2001, 2008a, b; Saul 2017a,  2021).Given that these norms will typically be implicit and given that they will likely vary substantially between different societies and cultures, Jennifer Saul takes it that they are best formulated in very general terms; as "Don't be racist" and "Don't be sexist ", respectively (cf.2017a, p.100, 2021,  p. 163).As these norms don't specify what it means to be racist or sexist, they are bound to be interpreted very differently by different people, even by members of the same society or the same culture.For instance, a statement that will strike some as quite definitely racist or sexist might be deemed benign by others. 1 That being said, most people want to conform to these norms in some form or another: They genuinely do not want to be racist or sexist.Or they might just not want to think of themselves as racist or sexist.Moreover, it might be that they don't want others to think that they are racist or sexist.And they might not want to think of those they feel close to-be it family, friends, or politicians they vote for-as racist or sexist (cf.Saul 2021,  pp.162-163).
Yet, says Saul, despite these norms and the roles they play in everyday life, many people harbour racist and sexist attitudes (cf.2021, pp.162-163). 2Put differently, when it comes to racism and sexism, there is a tension between (1) how people want to be or, at least, want to see themselves, want to be seen by others, and want to see others, and (2) how people actually are.It is this tension that provides fertile ground for what Saul calls "racial figleaves" and "gender figleaves".
According to Saul, "[a] racial figleaf is an utterance which (for some portion of the audience) blocks the conclusion that (a) some other utterance, R, is racist; or (b) the person who uttered R is racist" (Saul 2021, p. 161), while "[a] gender figleaf is an utterance which (for some portion of the audience) blocks the conclusion that (a) some other utterance, R, is sexist; or (b) the person who uttered R is sexist" (Saul 2021, p. 163). 3To see how such figleaves might work, let's look at an example for each one-starting with a racial figleaf.Imagine a speaker says something racist, e.g., "Black men are prone to criminal behaviour", only to follow up on their statement with "But don't get me wrong, some of my best friends are black". 4Now, upon listening to the combination of these two statements, someone who, say, harbours racist attitudes but doesn't want to think of themselves as racist-i.e., who wants to conform to the "Don't be racist"norm-might (implicitly) reason as follows: 1. Someone who has black friends can't simultaneously be racist.2. The speaker says they have black friends (and I can/  should/am going to take their statement at face value).3.If the speaker says that they have black friends, then the speaker can't be racist.4. The speaker isn't racist.5.If the speaker isn't racist, then their statement about black men isn't racist.5 6.If their statement isn't racist, then I am not racist for believing, accepting, repeating… it.Therefore, 7. It's ok for me to believe, accept, repeat… it.
Mutatis mutandis, the speaker themselves might appeal to this line of reasoning.
As we shall presently see, we can say something similar about gender figleaves.Imagine a speaker makes a sexist remark-like, "Women are no good at math"6 -and then follows up on this remark by adding "In saying this, I want to make it clear that I have great respect for women".Here, someone who, say, harbours sexist attitudes but who doesn't want to think of themselves as sexist might (implicitly) reason as follows: 8. The speaker isn't sexist (after all, they have great respect for women).7 9.If the speaker isn't sexist, then their statement about women isn't sexist.10.If that statement isn't sexist, then I am not sexist for believing, accepting, repeating… it.
Therefore, 11.It's ok for me to believe, accept, repeat… it.
Again, mutatis mutandis, the speaker themselves might reason in this vein.
Extrapolating from these examples, we can say that racial figleaves and gender figleaves allow people to perform a kind of cognitive "magic trick" on themselves: they allow people to indulge their racism and sexism without thereby having to think of themselves (or others) as racist or sexist.Put differently, figleaves allow people to be racist or sexist while at the same time deluding themselves into thinking that they are conforming to the societal norms prohibiting against racism and sexism.
Figleaves are far from harmless.For one, they might well have a negative effect on the personal level: by allowing people to harbour racist or sexist attitudes without having to think of themselves as racist or sexist, figleaves can stand in the way of people realizing that they do in fact harbour such attitudes, which of course would be a first step towards addressing and maybe ultimately overcoming these attitudes (cf.Saul 2017a, pp.110-111).
In addition, says Saul, figleaves pose dangers on the societal level.To make the case for this claim, she draws on Rae Langton's (2012) and especially Mary Kate McGowan's  (2012) work on how racist and sexist utterances can change the "conversational score" (cf.Lewis 1979) in a given context.The idea here is, roughly, that by introducing racist or sexist utterances in some discursive context, provided these utterances go uncontested, speakers can make such utterances and the attitudes expressed by them more acceptable than they were before in that context.And if such racist or sexist utterances are frequent enough and go uncontested enough times, then, over time, it might become more acceptable in general to make such utterances, or to hold the attitudes expressed by them.In this way, the boundaries of what is deemed acceptable to say or think in a given society can shift.
However, says Saul, this picture is incomplete.For, according to her, the conversational score doesn't automatically change when racist or sexist utterances go uncontested.After all, it might be that hearers who want to conform to the "Don't be racist"/"Don't be sexist"-norm don't accept these utterances,8 even if they don't contest them.Such hearers might simply be too timid or too embarrassed to speak up.This is where racial and gender figleaves can come in.For, thanks to the 'cover' provided by figleaves, hearers might deem the accompanying utterances non-racist/non-sexist and accept those utterances as a result.In this way, figleaves can help along the boundary-shift alluded to above (cf.Saul  2017a, pp.109-110, 113; 2021, p. 172).
Based on these remarks on figleaves, their workings, and the negative effects they likely bring in their wake, I will use the rest of the paper to examine what strikes me as an especially pernicious figleaf: a certain kind of reference to statistics (or empirical data more broadly).In what follows, I will first argue that, plausibly, appeals to statistics (or data) can function as both racial and gender figleaves in the sense described in this section.To do so, I will rely on both constructed and real-life examples ( §2).Assuming that this is on the right track, I will then look at some reasons why "statistics-as-figleaves" might be an especially pernicious kind of figleaf.And I will say something about why appeals to statistics might be pernicious in the discourse on race and gender, even when these appeals don't function as figleaves ( §3).Finally, switching to a more positive key, I will tentatively explore some strategies for counterspeech ( §4).
One clarification before we start: I don't want to deny that statistics play an important role in the social sciences and in science more broadly.Thus, the aim of this paper is not to argue that we shouldn't collect statistical data or that we shouldn't appeal to such data.Rather, the aim of this paper is only to highlight the dangers that certain appeals to statistics might carry with regard to the discourse on race and gender (I will return to this issue at the end of §2).

Statistics as Figleaves
To explore how appeals to statistics might function as racial figleaves or gender figleaves, let's return to the racist and sexist statements from the last section-"Black men are prone to criminal behaviour"/"Women are no good at math".And let's consider how a speaker might follow up on these statements by appealing to statistics: As already pointed out, viewed on their own, (I-a) and (IIa) might well be perceived as racist and sexist, respectively.However, due to adding (I-b) and (II-b), the racist/sexist inference-i.e., the inference to the conclusion that the speaker or their statement is racist or sexist-might well get blocked for some portion of the audience.9One way this inference might get blocked is if an interlocutor were to follow a similar line of reasoning as that introduced in §1.For instance: 12. The speaker isn't racist/sexist (after all, they are just reporting facts).10 13.If the speaker isn't racist/sexist, then their statement about black men/women isn't racist/sexist.14.If that statement isn't racist/sexist, then I am not racist/ sexist for believing, accepting, repeating… it.
Therefore, 15.It's ok for me to believe, accept, repeat… it.
Thus, in both cases, the appeal to statistics might function as a figleaf according to Saul's characterization of the term.
For the sake of clarity, I have used two constructed examples to introduce the idea that appeals to statistics might function as figleaves in the discourse on race and gender.The plausibility of this idea depends on how plausible it seems that, at least some, hearers would (implicitly) embrace something like (12).To show that the latter seems indeed plausible, let's now turn to two real-life examples, both drawn from the social media and discussion website/platform Reddit.In a post on the sub-reddit "r/changemyview", a user writes: Let's take a moment to examine this post in some detail.The reddit-user starts off by explicitly saying that it would be racist to assume that a black male walking towards you will mug you.One might take this, together with what he says near the end of the post, to suggest that the user wants to avoid being perceived as racist.In other words, one might take some of what he says to suggest that he wants to conform to the "Don't be racist"-norm alluded to in the previous section.But he then goes on to ask whether the assumption in question would also be racist if it then gets confirmed-if the black male walking towards you were to actually rob you.And he seems to take it that this would no longer to be the case, saying "I think if you make an assumption about a person that is not in their favor on no grounds other than race, you're a racist.But only if you are wrong".In connection to this, he also asks in a more general vein whether facts can be racist.The dialectics in play here are interesting in so far as they point to a juxtaposition that might hold sway over people's thinking and talking about racism: On the one hand, racism is a nefarious ideology where you make unfavourable assumptions about a person on no other grounds than their race.On the other hand, reflecting on facts and talking about them are value-neutral activities, free from ideology.That is, by reflecting on them and talking about them, we can, respectively, see and communicate how the world really is.This juxtaposition in turn might lead some hearers to think that a speaker can't be racist if they are referring to "facts".Relatedly, some hearers might think that views and the statements expressing them can't be racist if they are based on "facts" or get confirmed by them.
For the purpose of this paper, the reference the reddituser makes to statistics is especially worth emphasising.He moves quite straightforwardly from talking about "facts" to talking about "accurate prison demographics", presumably referring to statistics about incarceration rates among black males.What seems to be suggested here is that statistics are an appropriate purveyor of "facts".That is, by consulting statistics we can learn something about how things really are and by talking about them we can communicate something about how things really are.Using this way of thinking as our guide, we can somewhat specify the above juxtaposition: On the one hand, racism is a nefarious ideology.On the other hand, reflecting on facts and talking about them are value-neutral activities, free from ideology.And we can learn about those facts and communicate them with the help of statistics.In accordance with this specified juxtaposition, some hearers might in turn think that a speaker can't be racist if they are referring to "statistical facts".Relatedly, some hearers might think that views and the statements expressing them can't be racist if they are based on "statistical facts" or get confirmed by them. 12f this is the case, then, at least some, hearers who harbour racist views but who still want to conform to the "Don't be racist"-norm might well (implicitly) embrace something like (12)-The speaker isn't racist/sexist (after all, they are just reporting facts)-when encountering a racist statement that is coupled with an appeal to statistics.And their (implicitly) embracing something like (12) might furthermore well block an inference on their part to the conclusion that the statement in question or the speaker who uttered it is racist.Consequently, these hearers might now deem it (at least more) morally permissible to make or endorse the statement in question.Perhaps something along these lines is happening when the reddit-user starts out by calling some assumptions racist but then ends up asking "If you are right, then aren't you slightly absolved of your malicious assumptions?".In light of these considerations, it thus seems plausible that appeals to statistics can function as figleaves in the discourse on race.
In a similar spirit, let's now turn to a real-life example that will help us see how appeals to statistics can sometimes function as gender figleaves.On the sub-reddit "r/ MansRights", a user starts his post by writing: "The 'incredibly sexist notion' that women generally like action-packed games less and relaxing or puzzle-based games more is not a sexist stereotype, but statistical fact [emphasis added]." He then provides a screenshot of the statistic in question and elaborates on his opening remark as follows: I once had a conversation with a female friend of mine, where I said about Ghost of Tsushima-an Action Adventure samurai game-that I wasn't sure if she'd like it as much as I did, because women typically like these sorts of games a lot less.She's a very civil person and we still have a very good relationship, but it was clear she thought I just said something very sexist.
And he adds near the end of his post: Whoever has a sober mind, clearly wouldn't think these statistics pose any problems.After all, we don't have a feminist ideology we need to preserve at all costs!As a Christian, the difference between men and women is a wonderful thing; they are equal -both being made in the image of God -but not the same.[emphasises in the original] 13 In this reddit post, the user expresses, and apparently endorses, what might be taken to be a blatantly sexist stereotype; he assumes that his friend won't like an action game in virtue of her gender.More specifically, he reports saying to a female friend that she, because she is female, might not like "Ghost of Tsushima" as much as he does.14At the same time, he denies that what he has said is sexist.How?
Let us try to develop the idea that what goes on here can be likened to what plausibly happens in the post on racism.To do so, first note that this reddit-user sees the notion he expresses not as "a sexist stereotype", but as a "statistical fact".This suggests a juxtaposition similar to the one presented above, one which might hold sway over people's thinking and talking about sexism: On the one hand, sexism is a nefarious ideology.On the other hand, thinking and talking about facts is value-neutral, free from ideology.And we can learn about those facts and communicate them with the help of statistics.In accordance with this sexism-related juxtaposition, hearers might in turn think that a speaker can't be sexist if they are referring to "statistical facts".Relatedly, some hearers might think that views and the statements expressing them can't be sexist if they are based on "statistical facts" or get confirmed by them.Something in this vein strikes me as a plausible diagnosis of what goes on when the reddit-user claims that the remarks he made towards his female friend aren't sexist.
Assuming the above is more or less correct, the following should seem plausible, too: At least some hearers who harbour sexist views but who still want to conform to the "Don't be sexist"-norm might well (implicitly) embrace something like (12) when encountering a sexist statement that is coupled with an appeal to statistics.And their (implicitly) embracing something like (12) might furthermore well block an inference on their part to the conclusion that the statement in question or the speaker who uttered it is sexist.Consequently, these hearers might now deem it (at least more) morally permissible to make or endorse the statement in question.In light of these considerations, it thus seems plausible that appeals to statistics can function as figleaves in the discourse on gender as well.
So far, I have used both constructed and real-life examples to make plausible that appeals to statistics can function as racial figleaves and gender figleaves. 15However, as pointed out at the end of §1, it can be perfectly legitimate to appeal to statistical data.For instance, it seems perfectly legitimate to appeal to statistical data on the correlation between drunk driving and the causing of traffic accidents to speak in support of laws that prohibit driving under the influence. 16This raises the question whether we have a way of distinguishing between legitimate appeals to statistics and, say, statisticsas-figleaves. 17 Since whether an appeal to statistics in legitimate or functions as a racial or gender figleaf will likely depend on quite specific contextual factors, I fear that we won't be able to establish hard and fast rules here.Still, the following might be of help: As, roughly speaking, statisticsas-figleaves-like figleaves more broadly-are utterances that can provide 'cover' for racist or sexist statements, it functions as a figleaf if said utterance blocks the conclusion that (a) some other utterance, R, or (b) the person who uttered R is racist/sexist for some portion of the audience.This leaves open the possibility that an utterance might function as a figleaf for some portion of the audience, while having the opposite effect on another portion of the audience.Saul herself discusses such an example.Commenting on Paul Ryan's response to Donald Trump's infamous remarks on the "Access Hollywood"-tape, she writes: "Ryan condemned [Trump's] utterances, saying that 'women are to be championed and revered' […].Ryan received extremely widespread praise in the media for this utterance.As many feminist critics noted, however, Ryan's utterance was a clear expression of the sort of Benevolent Sexism that holds women back from being taken seriously as agents, and that indeed can serve to support rape culture.(2021: 165).Here, plausibly, Ryan's utterance functioned as a gender figleaf for one portion of the audience, while having the opposite effect on another portion of the audience.Likewise, I want to allow that an appeal to statistics might function as a figleaf for one portion of the audience, while having the opposite effect on another portion of the audience. 16Thanks to an anonymous Referee for suggesting this example. 17To be clear, I don't mean to suggest here that an appeal to statistics it automatically legitimate if it doesn't function as a racial or gender figleaf.I will say more on this in § § 3 and 4. might help to perform a kind of "subtraction test".That is, when an appeal to statistics is coupled with another statement, one might try to mentally subtract the former and look at the latter in isolation, asking "Does this statement appear racist or sexist"?If the answer is "Yes", then chances are you are dealing with an appeal to statistics that functions (or can function) as a racial or gender figleaf.Conversely, if the answer is "No", then chances are you are dealing with an appeal to statistics that doesn't function as a racial or gender figleaf, and that is thus legitimate in this regard.For instance, viewed on its own, a statement like "People driving drunk are prone to cause accidents" doesn't appear racist or sexist.After all, it doesn't even contain a reference to a specific group that might be especially prone to drunk driving, or that might be especially prone to cause traffic accidents when driving drunk.Hence, it seems unlikely that a related appeal to statistics functions as a figleaf.In contrast, viewed on its own, a statement like "Black men are prone to criminal behaviour" does seem racist indeed.After all, it contains a racist stereotype.Hence, it seems likely that a related appeal to statistics functions (or can function) as a figleaf.

Why Statistics Might Make for Especially Pernicious Figleaves
After thus arguing that appeals to statistics can function as racial figleaves and gender figleaves, I will now argue that there is reason to believe that appeals to statistics might make for especially pernicious figleaves.To make case for this claim, we first need to recall why, according to Saul, figleaves are pernicious in general.Saul points out that hearers who want to conform to the Don't be racist"/"Don't be sexist"-norm might not accept racist or sexist statements they encounter, even if they don't contest them.Rather, says she, such hearers might accept such statements only if, for some reason or other, they don't perceive them as racist or sexist.And here figleaves might play a crucial role.Due to the 'cover' provided by a figleaf, hearers who want to adhere to the norms in question might deem an utterance non-racist/ non-sexist-that they would have deemed racist/sexist otherwise-and accept said utterance as a consequence.Hence, over time, figleaves might help shift the boundaries of what is deemed acceptable to think or say in a given society (cf.§1).
Based on these general remarks on the perniciousness of figleaves, it seems plausible that the perniciousness of a given kind of figleaf depends on how robust a cover for racism and sexism it provides for how many people.The "worse" some figleaf does in this regard, the less likely it is that someone who want to conform to the above norms will accept the racist or sexist statement that said figleaf accompanies.Hence, the less likely it is that said figleaf will contribute to the boundary-shift Saul warns against in any significant way.Conversely, the "better" some figleaf does in this regard, the more likely it is that someone who want to conform to the above norms will nevertheless accept the racist or sexist statement that said figleaf accompanies.Hence, the more likely it is that said figleaf will make a more significant contribution to shifting the boundaries.
On this picture, for instance, the so-called "denial figleaf" might be a relatively "harmless" kind of figleaf.The classic example for a denial figleaf is "I'm not a racist, but…", where the "but" is followed by a statement that would likely be perceived as racist when made on its own (cf.2017a, p.  103, 2021, p. 165).Now, as Saul points out, this well-worn phrase will do little for most audiences to block the inference to the conclusion that some other utterance or the speaker is racist (cf.2021, p. 165).Rather, it will likely do quite the opposite-it will put people on racism-alert."I am not a racist, but…" has become something of a meme, frequently made fun of by comedians and even members of the general public.It seems that common wisdom has it that this phrase typically functions as "a figleaf" for racism (though, of course, most people would not use the term "figleaf" to refer to this phrase).After all, why would one otherwise introduce an utterance by explicitly denying to be a racist?For example, why would one say something like "I am not a racist, but I need to buy new socks"?In fact, there is a challenge where one has to produce a meaningful sentence containing the phrase in question, and where what follows after the "but" is genuinely non-racist. 18Hence, "I am not a racist, but…" and the like will likely not lead to many hearers accepting a statement that they wouldn't have accepted otherwise.This in turn means that the denial figleaf will likely do little when it comes to shifting the boundaries of what is deemed acceptable to think or say.
What about statistics-as-figleaves? Recall, it was argued in the last section that appeals to statistics can function as racial and gender figleaves because some hearers might take it that views and the statements expressing them can't be racist or sexist if these views and statements are supported by what they take to be "statistical facts" or "statistical evidence".Put differently, some hearers might take it that a statement is either racist/sexist or backed by (what they perceive as) statistical evidence.If this is on the right track, then, by this token, an appeal to (what they perceive as) statistical evidence would likely be quite a robust cover for racism or sexism for these hearers-probably a more robust cover than, say, a denial figleaf.Consequently, these hearers might be quite likely to accept a racist or sexist statement-a statement that they probably wouldn't have accepted otherwise-if it is accompanied by an appeal to (what they perceive) as statistical evidence.
However, the following needs to be stressed at this point: for all that was said so far, people who take it that a statement is either racist/sexist or backed by (what they perceive as) statistical evidence might be quite rare (for instance, the two reddit posts discussed in the last section might point to quite unusual, rather than common, patterns of thought).If this were the case, then, probably, appeals to statistics wouldn't make for very effective figleaves, because such appeals would only work for a rather limited number of people.
Now, how common it actually is for people to think that a statement is either racist/sexist or backed by (what they perceive as) statistical evidence isn't something that can be decided from the philosopher's armchair.Only empirical research might decide that.Yet, let me point out that there is some more general research that indicates that quantitative data tends to be perceived as carrying what we might call "an air of scientific objectivity" and, relatedly, that such data might have quite some purchase on people's thought and behaviour (cf.e.g., Porter 1995 (quoted in fn.12); Merry  2016; Nguyen 2021a, b).In light of such research, it doesn't seem unreasonable to assume that there might be relatively many people that are in the grip of the above dichotomy.And if this assumption turns out to be correct, then relatively many people might be quite likely to accept a racist or sexist statement-a statement that they probably wouldn't have accepted otherwise-if it is accompanied by an appeal to (what they perceive as) statistical evidence.Following this line of thought, provided they are used often enough, statistics-as-figleaves might play some non-negligible part when it comes to shifting the boundaries of what is deemed permissible to think or say.
I think that the considerations presented in this section lend plausibility to the claim that appeals to statistics might make for an especially pernicious kind of figleaf: given that (1) the perniciousness of some kind of figleaf depends on how robust a cover for racism and sexism it provides for how many people, and assuming that (2) statistics-as-figleaves provide a rather robust cover for racism and sexism for a relatively large number of people, then, plausibly, statisticsas-figleaves turn out to be an especially pernicious kind of figleaf. 19fter thus arguing that, plausibly, appeals to statistics make for an especially pernicious kind of figleaf, let me end this section by suggesting some reasons for why certain appeals to statistics might be more generally pernicious when it comes to the discourse on race and gender, even when these appeals don't function as figleaves.Again, consider a speaker uttering "Black men are prone to criminal behaviour.Just look at the incarceration statistics".While this appeal to statistics will likely function as a figleaf for some portion of the audience, it might have a somewhat different effect for another portion of the audience.That is, for a portion of the audience that already explicitly endorses racist views, e.g., that already has explicitly disrespectful attitudes towards black men qua black men (cf.Glasgow, 2009), this appeal to statistics will probably not function as a figleaf for racism (there would be no need for this here).Instead, it might help reinforce and perhaps even strengthen the consciously endorsed racist views of these hearers."Told you so", they might say to fellow racists or to their more moderate friends, should they have any.In connection to this, it might also be noteworthy that the search term "statistics" yields 9,140 results on the notoriously racist outlet Breitbart News, leading to articles such as "Almost Half of Crimes in Berlin Committed by Migrants"20 or "Sweden Blocks Request for Data on Link Between Crime and Immigration". 21For this might be taken to suggest that this outlet, to a degree, exploits the figleaf potential of statistics-perhaps trying to lure readers in and make them more susceptible to the discriminatory views Breitbart promotes.While I have focused on racism here, similar considerations might apply to sexism as well.That is, for someone who explicitly endorses sexist views, appeals to certain gender-related statistics might also serve to reinforce and perhaps even strengthen their sexist views.If this is on the right track, then certain appeals to race-and gender-related statistics might be pernicious when it comes to the discourse on race and gender, not just because such appeals might function as figleaves for some portion of the audience, but also because they might serve to reinforce or strengthen explicitly endorsed racist or sexist views for another portion of the audience. 22n connection with these two possibilities, let me briefly explore the suggestion that certain appeals to race-and gender-related statistics might also be pernicious in so far as they might play a part in people's radicalization.Again, focusing on racism, imagine someone who holds a view like "Black men are prone to criminal behaviour", but who 19 A clarification is in order here.In arguing that statistics-asfigleaves might be an especially pernicious kind of figleaf, I don't mean to suggest that they are a uniquely pernicious kind of figleaf.I take everything I have said in this section to be compatible with the claim that there are other kinds of figleaves that are equally pernicious or that are even more pernicious.doesn't want to think of themselves as racist.Such a person might look up incarceration statistics online to confirm their preconceived notions, all the while thinking "It's not racist if it's true". 23This search behaviour in turn might lead to them looking up related statistics, and eventually happening upon outlets such as Breitbart News, thereby encountering and over time explicitly embracing more and more radical positions.In short, in the beginning, they might consume statistics and appeal to them to indulge their racial resentments while also convincing themselves that they aren't racist.However, over time, statistics might tempt them further and further down the racist rabbit hole. 24Admittedly, I have only sketched a hypothetical scenario here.Still, such a scenario doesn't seem too unrealistic.Moreover, provided this scenario seems realistic enough, we might also think that similar scenarios could play out with regard to sexism.
In this section, I have tried to make plausible the idea that statistics-as-figleaves might be an especially pernicious kind of figleaf.And I have also suggested some additional reasons for why we might think that certain appeals to statistics are more generally pernicious when it comes to the discourse on race and gender.Assuming one broadly agrees with what has been said so far, one probably wonders by now how to respond when encountering an appeal to statistics that functions as a figleaf, or that might serve a more explicitly racist or sexist purpose.This is the question to which will now turn.However, before I do so, let me stress that I don't claim to have any definitive answers here.Rather, what follows is meant to be exploratory in nature, explicitly inviting the reader to come up with further suggestions.

Counterspeech
In this section, I will consider three strategies for counterspeech that one might employ when encountering an appeal to statistics that plausibly (i) functions as figleaf, or (ii) serves a more explicitly racist or sexist purpose, or that (iii), for different portions of the audience, serves both functions, respectively. 25The first thing one might do here is to simply ask "What statistic are you/they referring to?" or "Where can I find it?".Such questions are relevant as a speaker might simply make a claim about some statistic to back up their other statement(s) without actually citing the statistic in question or providing a source.For instance, the first reddit-user just uses the phrase "accurate prison demographics" without providing any source (also cf.fn.9).One should be vigilant here, because, generally speaking, it might not be clear whether there actually is a statistic to go along with the statement(s), or whether it is made up.A prominent example for such deceit is provided by German author and former politician Thilo Sarrazin.In 2010, Sarrazin published an anti-Muslim book titled Deutschland schafft sich ab ("Germany abolishes itself"), which was widely read, discussed, and decried as racist.The book, as well as interviews given and articles penned by him, were filled with talk of statistics.However, when pressed on his statistical data, Sarrazin had to admit that he had just fabricated some of it, saying that if one doesn't have a number, then one has to create one that points in the right direction.Adding that, if nobody can refute that number, he will prevail with his estimate. 26Now, if one asks for a source and the speaker is unable to provide it, or even has to admit that the statistic in question is made up, then this might already be sufficient to deter some hearers from accepting the racist/sexist statement that was made in conjunction with the reference to statistics.
However, cases where a speaker initially doesn't provide a source and is then unable to provide it, or where the speaker has to admit that the statistic in question was made up might be quite rare.Hence, the first strategy might be quite limited in its applicability.Another strategy might have wider application.To get to this strategy, a little stage-setting is required.
Pairs of statements such as "Black men are prone to criminal behaviour.Just look at the incarceration statistics" and "Women are no good at math.Just look at the statistics on university degree conferment" could be taken to suggest that there exists a causal connection between corresponding pairs of states of affairs.In the first case, the combination of the two statements can be taken to suggest that incarceration statistics are the way they are because black men are prone to criminal behaviour; that is, if black men weren't prone to criminal behaviour, then the respective incarceration statistics would be different. 27In other words, the combination of statements can be taken to suggest that it is black men's be at least somewhat effective for addressees who, in some way, want to conform to the "Don't be racist"/ "Don't be sexist"-norm.
Footnote 25 (continued)  proneness to criminal behaviour that explains why the incarceration statistics are the way they are.And in the second case, the combination of the two statements can be taken to suggest that the statistics on university degree conferment are the way they are because women are no good at math; that is, if women were better at math, then the statistics on university degree conferment would be different.In other words, the combination of statements can be taken to suggest that it is women's lack of aptitude for math that explains why the statistics on university degree conferment are the way they are.
However, statistics are about correlation, not causation. 28 Based on these remarks, we can now formulate a second strategy for counterspeech: Given that (1) statements like the above can be taken to suggest a causal connection between, say, proneness to criminal behaviour and incarceration statistics or aptitude for math and statistics on university degree conferment, and given that (2) statistics are about correlation not causation, an effective means of counterspeech might be to point out that statistics can't tell you what might be responsible for the phenomenon tracked by them.For example, one might point out that incarceration statistics, in and of themselves, can't tell you anything about a certain group's proneness to criminal behaviour.Or one might point out that statistics on university degree conferment, in and of themselves, can't tell you anything about a certain group's aptitude from math.Pointing such things out might also be sufficient to deter some hearers from accepting racist or sexist statements that are made in conjunction with an appeal to statistics.
Yet, there is a potential problem here.Even if a hearer accepts the general point that statistics are about correlation not causation, they might still think that, in a given case, the statistical data is best explained by an alleged "fact" about a certain group, or they might think that this "fact" provides the only explanation for the statistical data.For example, a hearer might still think that incarceration statistics are best explained by black men's proneness to criminal behaviour.Or they might still think that statistics on university degree conferment are best explained by women's low aptitude for math.Thus, they might still accept the racist or sexist statement in question, even after it has been pointed out to them that statistics are about correlation not causation.
This brings me to a third counterspeech-strategy that might be used to complement the second strategy.When being faced with statements like the above, one might not just point out that statistics are about correlation not causation, but one might also point to alternative explanations of the statistical data that is referred to or quoted.More specifically, one might point to alternative explanations that don't appeal to some alleged trait of the group in question, but that take historical, sociological, and economic factors into consideration. 31For example, when encountering a statement about black men and incarceration statistics, one might point out that American society is fraught with "both structural and individual racism" (cf.Saul 2021, p. 175), which manifests itself, among other things, in black men being disproportionally affected by stop-and-frisk, receiving worse legal representation, and getting higher prison sentences… And one might further point out that these factors are well-documented and that they help explain incarceration rates without 31 Incidentally, these considerations also point to a test for implicit racism or implicit sexism one might perform on oneself.When encountering a relevant statistic, one might stop and reflect on what explanation of the data one feels drawn to -is it an interpretation that shines a negative light on the group in question?If the answer is "Yes", then one might well be committing "explanatory injustice" (see fn. 30), which in turn might indicate that one harbours racist or sexist attitudes. 28For a classic discussion of the difference between correlation and causation, see David Hume (2007: Book I, sec.6). 29https:// www.stati sta.com/ stati stics/ 12250 47/ ranki ng-of-happi estcount ries-world wide-by-score/ (last accessed: 2. November, 2022). 30Drawing on Miranda Fricker's (2003, 2007) work on epistemic injustice, Andrew Peet (2017) identifies a variant thereof, which he labels "interpretative injustice".According to Peet, "[i]nterpretative injustice is the phenomenon whereby a hearer's employment of prejudicial stereotypes results in the hearer attributing a message to the speaker when the speaker never intended to convey that message" (2017: 3423).An example would be a black man saying "They seem vulnerable to me" to convey that the person in question needs some help, and his white audience interpreting this as him saying that said person would make an easy victim (cf.2017: 3432-3433).-Now, drawing on what was said above, we can identify a phenomenon that seems in the vicinity of interpretative injustice, call it explanatory injustice, which might be characterized as follows: explanatory injustice is the phenomenon whereby one's employment of prejudicial stereotypes results in one explaining statistical data about a given group in a way that shines a negative light on that group, while discarding or not even entertaining alternative explanations.As should be obvious by now, an example for explanatory injustice would be automatically explaining incarceration statistics by appealing to an alleged proneness to criminal behaviour on the part of black men, while discarding or not even entertaining the notion that these statistics point to systemic racism.
referring to any special proneness to criminal behaviour on the part of black men. 32Similarly, when encountering a statement about statistics on university degree conferment and women's aptitude for math, one might point out that math and math-heavy professions, like engineering, were and continue to be seen as male domains, and that this stereotype might already dissuade women from enrolling in math programs.Moreover, one could remark that this stereotype might well foster a hostile environment for female math students and consequently make it less likely that they see their studies through to graduation. 33In short, one might point out that there are several factors that might well explain why comparatively few women get university degrees in math, and that these factors have nothing to do with any lack of mathematical aptitude on the part of women.
Pointing out that statistics are about correlation not causation and also pointing to such alternative explanations of the statistical data might deter some hearers from accepting racist or sexist statements that are made in conjunction with an appeal to statistics.In fact, doing these things might even deter some hearers that initially thought that the alleged "fact" about the members of the group in question was the best explanation of the statistical data.Thus, the combination of strategies two and three might be quite an effective means of counterspeech here.
In this section, I have outlined three strategies for counterspeech that one might employ when appeals to statistics are being made in conjunction with racist and sexist statements.Surely, these strategies won't be effective for all hearers, and they won't be effective in all situations (cf.fn.25).Nevertheless, these strategies might deter some hearers in some situations from accepting racist or sexist statements.Thus, employing these strategies might go some way towards preventing or counteracting problematic boundary-shifts in the public discourse on race and gender.

(
I) "(a) Black men are prone to criminal behaviour.(b) Just look at the incarceration statistics."(II) "(a) Women are no good at math.(b) Just look at the statistics on university degree conferment." But the statistic, in and of itself, can't tell you what accounts for this difference in happiness.Likewise, a statistic can tell you that a disproportionate number of black men are incarcerated in the United States right now, or that relatively few women get university degrees in math-but it can't tell you why.More generally, statistics can point to problems, but they can't identify what causes said problems.If black men get disproportionately incarcerated in the United States, then this surely constitutes a problem.And if relatively few women get university degrees in math, then this constitutes a problem, too.But statistics, by their nature, are entirely silent on where such problems stem from.