Response To The Comments On The Sexual Assault Model

Content warning: this post discusses sexual assault in some detail.  

A few weeks ago I created a statistical model which implied that someone who has been accused of sexual assault by multiple people is much less likely to be innocent than someone who has been accused of assault once. This ended up on the front page of Hacker News, which Wikipedia describes as a “social news website that caters to programmers and entrepreneurs”, and got as many views in two hours as this blog usually gets in two months. (Granted, that’s not really that much traffic, but we math nerds take what social interaction we can get.)

While many of the comments were critical, almost all of them were civil, thoughtful, and free from the kind of trolling that women worry about when discussing controversial gender issues. All this was tremendously welcome and we need more of it on the internet, so I thought I’d take the time to respond to the most interesting criticisms.

To briefly recap (skip to the next paragraph if you remember all this) the model computes the probability that someone who has been accused of sexual assault by k people has never actually committed assault. The model assumes that there are serial predators, who have a high probability of assaulting people, and non-serial predators, who have a low probability of assaulting people. If someone has been assaulted, they accuse the other person of assault with probability pag; if they have not, they accuse with lower probability pai. You can play with the model here (move the sliders).

As I note in the post, the model is a simplification of the real world, as all models are. That said, it is possible to build a model which is too simple. For example, after Thomas Duncan’s nurse became infected with Ebola, it would’ve been irresponsible to say, “Assuming the current rate of weekly doubling of the Ebola epidemic in the United States continues, we expect every US citizen to be infected 28 weeks from now.” So I will discuss three assumptions the model makes and you can judge their validity and usefulness for yourself. Overall my view is that, while the model simplifies out of necessity, its basic conclusions probably hold even under more complicated assumptions. But I also see why there is room for disagreement because of the uncertainty about basic facts about assault, and trying to build a model has definitely reinforced my belief that we need better data. There also are clear tradeoffs between reducing the number of assaults and not punishing innocent people; your background and life experiences differ from mine, so you may worry more about victims of assault and less about the wrongfully accused, or vice versa.

1. Objection 1: the model assumes that each accuser comes forward independently, but in fact each accuser may be influenced by previous accusers. This was the main concern people on Hacker News raised [1], and I think it is definitely important in high-profile cases like Bill Cosby’s, where accusers may well know about and be influenced by previous accusations. But I do not think cases like Cosby’s are the most representative or important to focus on, because the vast majority of accused and actual rapists are not famous. In these more representative cases, I do not think we should worry too much about accusers influencing each other for several reasons.

1. In my post I discuss a tool which allows accusers to file accusations with a third party who will keep the accusations confidential unless multiple accusations are levied at the same person. If this tool were used, it seems quite likely that accusations would be essentially independent because they would be kept secret. (I guess it is possible that accusers might collude and all submit accusations together, but this John Tucker Must Die scenario stretches the bounds of plausibility.)
2. Even if this tool is not used, accusers have to know about previous accusations to be influenced. This is by no means certain: how many people do you know who are currently being accused of assault? How often do you compare notes with someone who has had a sexual encounter with the same person?
3. Even if accusers are aware of previous accusations, either i) they were actually assaulted, in which case it’s a good thing if they’re more likely to come forward or ii) they were not actually assaulted, in which case it’s a little unclear why they’re now more likely to come forward. The thought process might go something like, “Well, I’m not sure exactly what happened...I thought it was consensual at the time, but if they attacked other people, I guess they attacked me too.” This seems a bit unlikely to me, but feel free to suggest more plausible reasoning.
4. Even if you disagree with all of the above speculations about probabilities, I built you another model that allows you to adjust them (you might have to zoom out to see all the sliders) and account for accusers who influence each other. I describe the mathematical details here [2]. Even if accusers can influence each other quite strongly, this model yields the same conclusion as before: someone accused of sexual assault multiple times is much less likely to be innocent. For example, I still see the same effect if I make an accuser ten times more likely to accuse an innocent person of assault if they learn they’ve been previously accused.

2. Objection 2: the model assumes that everyone is equally likely to be accused of assault. Several people pointed out that there are factors that influence how likely a person is to be accused of assault besides whether they actually assaulted someone. To take one example, someone who has sex with their partner of ten years may be less likely to be accused than someone who has sex with a person they met for the first time at a frat party, even if both encounters are consensual. More generally, we can imagine two groups of people, A and B, who have different sorts of sexual encounters, and thus different probabilities of being innocent:

I think this only alters our basic conclusion, though, if we think of a group who is a) reasonably large and b) a lot more likely to be accused of assault even when they are innocent. (Merely identifying group differences is not enough: you would have to identify a group for whom we can set the parameters of the original model such that the innocence curve no longer plunges sharply.) None of the people I spoke to about this advanced plausible candidates for such a group, but let me know if you have thoughts.

3. Objection 3: the model computes the probability that the accused is guilty of at least one assault, but that is not the probability we are interested in. In no court would a judge say, “Well, I don’t know which of these six alleged assaults the accused is guilty of, but there’s a very high probability he’s guilty of at least one, so I’m throwing him in jail.”

Still, when we’re not throwing someone in jail, we often do care about the probability the model computes. Maybe I’m in charge of student housing, someone’s been accused of assault by two of their dorm mates, and I’m trying to decide whether to move them to another house (or out of housing altogether). We frequently apply statistical reasoning that isn’t allowed in a courtroom.

Also, it turns out we can use a similar model to compute the probability that someone is guilty in a particular case (rather than in any case) given that they have k previous accusations of sexual assault, and it produces similar curves [3].

If you’ve made it this far, especially if you disagreed with me at the outset, thanks so much for reading, because I wrote this mainly for you. I imagine we still have disagreements and would welcome your comments below or via email.

Notes:

[1] Some made comparisons to witchcraft trials, where a “mob mentality” takes hold and many people accuse one person of witchcraft. I will say, first, that I think witch trials are so different as to make this comparison pretty useless. A large number of people are truly assaulted; no one is a true victim of witchcraft. I do not believe there are mobs of potential assault accusers who, having all had sex with the same person, are suddenly all possessed by groupthink and decide that their sexual encounters constituted assault.

[2] The model assumes that if someone’s been previously accused of assault, a potential accuser knows about it with probability pk; if they know about the previous accusations, they accuse with probability pag1  if the person assaulted them and pai1  if the person did not; if the potential accuser does not know about the previous accusations, they accuse with probability pag0  if the person assaulted them and pai0  if the person did not. As pk  goes to zero -- no accuser knows about the others -- we recover the old model, where every accuser acts independently. On the other hand, if we increase pk  and pai1  to 1 -- accusers are certain to know about the first accusation, and certain to accuse if they know about it -- every accusation after the first becomes uninformative, as we would expect. We see the same effect as in the old model even for quite high settings of pk  and pai1 -- which can’t be all that high by simple virtue of the fact that very few people are accused of assault -- someone who is accused multiple times is much less likely to be innocent.

[3] The lawyers I discussed this with are still uncomfortable with this kind of reasoning, where past accusations can influence present guilt. My general sense is that the legal system disallows a lot of reasoning that statisticians deem kosher -- which is probably, given the weight we place on not convicting the innocent, a good thing.