Looking more criminal: It’s not so black and white

Abstract

Prior research regarding the influence of face structure on character judgments and first impressions reveals that bias for certain face-types is ubiquitous, but these studies primarily used decontextualized White faces for stimuli. Given the disadvantages Black men face in the legal system, this study aimed to investigate whether the criminal face-type presented in the context of crime influenced different legal system-type judgments as a function of perpetrator race. In a mixed-model design, participants saw Black and White computer-generated faces that varied in criminality presented with either violent or nonviolent crime scenarios. At test, participants attempted to identify the original perpetrator from a photo array, along with providing penalty severity judgments for the crime committed. Results indicate that when crimes were violent, participants meted harsher penalties overall to Black faces or to high-criminality faces identified as the perpetrator. Furthermore, for violent crimes, participants were more likely to select a face from the photo array that was higher/equally as high in criminality rating relative to the actual perpetrator when memory failed or when the perpetrator was Black. Overall, the findings suggest that when people are making judgments that could influence another’s livelihood, they may rely heavily on facial cues to criminality and the nature of the crime; and this is especially the case for Black faces presented in the context of violent crime. The pattern of results provides further support for the pervasive stereotype of Black men as criminal, even in our racially diverse sample wherein 36% identified as Black.

Appendices

Appendix A

Face criminality pre-ratings

The overall model predicting criminality ratings was significant (χ²(2) = 501.72, p < 0.001; Table 6). As expected, low-criminality faces were judged as significantly less criminal than neutral criminality faces (b = -0.34, SE = 0.05, p < 0.001, 95% CI [-0.43, -0.25]), while high-criminality faces were judged as significantly more criminal than neutral criminality faces (b = 0.73, SE = 0.05, p < 0.001, 95% CI [0.63, 0.82]).

Table 6 Summary of the mixed effects linear regression analysis predicting criminality ratings among GSU participants (N = 70 participants)

Table 7 Mean criminality ratings for Black and White faces according to the criminality ID (N = 70 participants)

Appendix B

Sample vignettes for violent crimes

Stereotypically White crime – “Police got a call Tuesday night from a local woman about a house fire. The firefighters were on the scene before the house was too damaged, although the front door and garage were both severely burned. Luckily the woman got herself and her pets out of the house quickly, and no one was hurt. Home video surveillance showed the woman’s ex-boyfriend setting the fire. Police are currently looking for her ex-boyfriend as he seemed to have disappeared following the fire. They have a warrant out for his arrest on the charge of arson.”

Stereotypically Black crime – “Last night, a man walked into a convenience store with a gun in his pocket. After grabbing a six-pack of beer, the perpetrator walked to the cash register as if he was going to pay. While the cashier was ringing up the man for the beer, the man pulled out his gun and directed the cashier to empty the register into a bag. The cashier complied, and the man left the store. The police were promptly called and are looking for the assailant. The entire ordeal was caught on camera, and police are hopeful that the criminal will be found.”

Sample vignettes for nonviolent crimes

Stereotypically White crime – “Early this morning police arrested a local man forwarded appears to be a case of insurance fraud. The house mysteriously burned down a few days back with no apparent explanation. Authorities now believe it was done on purpose by the owner. Neighbors told police that the man had recently started to have severe money problems and could no longer afford to pay the mortgage on his home, which had caused his wife to take the children and leave him. Police believe that he thought that insurance would cover the cost of the damages, and he would no longer be liable to pay his mortgage.”

Stereotypically Black crime – “On the night of July 17th at approximately 9:45 pm police received a call regarding a car theft in a local mall parking lot. According to police, employees witnessed a man wearing a hoodie and pants using what they believe to be a metal wire to unlock the driver's side door. After gaining access to the vehicle the assailant then sped off almost striking one of the employees who happened to witness the apparent theft. Witnesses claim that the suspect may have been under the influence because of how erratically he was driving through the parking lot.”

Vignette violence pre-ratings

The overall model predicting vignette violence ratings was significant (χ²(2) = 521.47, p < 0.001; Table 8). Violent crimes were rated as significantly more violent than nonviolent crimes (b = 2.08, SE = 0.08, p < 0.001, 95% CI [1.92, 2.24]), while pre-determined, stereotypically Black crimes did not significantly differ from stereotypically White crimes in violence ratings.

Table 8 Summary of the mixed-effects linear regression analysis predicting vignette violence ratings (N = 39 participants)

Black perpetrator expectations pre-ratings

The overall model predicting expectations that a crime would be committed by a Black man was significant (χ²(1) = 76.74, p < 0.001; Table 9). Expectations that a crime would be committed by a Black man were significantly higher for pre-rated stereotypically Black, compared to White, crimes (b = 0.53, SE = 0.06, p < 0.001, 95% CI [0.41, 0.65]).

Table 9 Summary of the mixed effects linear regression analysis predicting expectations that a crime would be committed by a Black man (N = 39 participants)

White perpetrator expectations pre-ratings

The overall model predicting expectations that a crime would be committed by a White man was significant (χ²(1) = 76.74, p < 0.001; Table 10). Expectations that a crime would be committed by a White man were significantly higher for pre-rated stereotypically White, compared to Black, crimes (b = 0.57, SE = 0.05, p < 0.001, 95% CI [0.46, 0.68]).

Table 10 Summary of the mixed effects linear regression analysis predicting expectations that a crime would be committed by a White man (N = 39 participants)

Assigned penalty severity pre-ratings

The overall model predicting the severity of assigned penalties for a crime was significant (χ²(1) = 179.57, p < 0.001; Table 11). Violent crimes were given much harsher penalties than nonviolent crimes (b = 1.01, SE = 0.07, p < 0.001, 95% CI [0.87, 1.16]).

Table 11 Summary of the mixed effects linear regression analysis predicting the assigned penalty severity (N = 39 participants)

Appendix C

Identification in the nonviolent crime condition

The overall model was not significant (χ²(4) = 3.15, p > 0.05; Table 12), suggesting that our model predictors do not reliably influence the face-type level chosen as the perpetrator of nonviolent crimes. Hit and false alarm rates and associated confidence in identification for Black and White perpetrator trials in the nonviolent crime condition are presented in Table 13.

Table 12 Summary of the mixed effects binary logistic regression analysis predicting the criminal face-type level chosen as the perpetrator of nonviolent crimes (N = 191 participants/1,528 observations)

Table 13 Hit and false alarm rates and mean confidence ratings for nonviolent crimes in total and by perpetrator race (N = 191 participants)

Appendix D

Table 14 Hit and false alarm rates, mean confidence ratings, and mean penalty assigned for Black and White targets according to participant race (N = 385 participants)