Falling Under the Lens of Suspicion: Identifying Behavioral Factors that Generate Suspicion

About This Grant

In every false conviction case, there came a point in the investigation when an innocent person became a suspect. Despite the countless accounts of how this presumed guilt can start a chain reaction of confirmation bias that taints all stages of the criminal justice process, little is known about what triggers suspicion about the innocent person in the first place. Drawing from existing and to-be-created databases, the overarching goal of this project is to identify and understand what demographic, behavioral, and linguistic factors initially spark suspicion and trigger dangerous confirmation bias processes leading to false convictions. We focus on one of the earliest moments in which a person might become a suspect: when they call 911. A key hypothesis is that when witnesses’ behaviors violate expectations, people “morally typecast” the witness as less capable of being a victim and more capable of being a perpetrator. Increasing basic understanding on how these expectations are formed, how they are violated, and how they interact with witnesses’ gender and race, is a critical step towards predicting whether callers are ultimately charged with the crime they are reporting. Moreover, understanding how these processes emerge will be critical in developing state-of-the-art curriculum to educate police about their expectations’ accuracy and consequences, and to educate attorneys on defending clients they believe to be innocent. Given the serious consequences that false convictions have on both innocent individuals and the public trust in the criminal justice system at large, the project’s focus has broad societal impact by systematically investigating the source of detectives’ misguided “hunches” that have anecdotally led to false convictions. This study employs a variety of data-driven methods to identify predictors of suspicion and being charged with the crime one is reporting. First, we will create a large corpus of real 911 calls to analyze linguistic and acoustic behavioral aspects of reporting a violent crime to see if laypeople and law enforcements’ expectations are accurate. We will recruit lay people, police officers, 911 operators, and trauma clinicians to listen to these calls and report their impressions of the caller to identify what aspects of reporting a violent crime generates suspicion and predicts actually becoming a suspect in the crime. We will identify psychological explanations for these effects as well as factors that might moderate these effects, such the caller’s race and gender. Across studies, we will use indirect, data-driven ways of assessing what makes people suspicious without imposing the researchers’ hypotheses on the design, such as using natural language processing machine-learning models. We will also test downstream consequences of witnesses’ emotion expression in a 911 call for the likelihood of being falsely convicted at trial for the crime they reported. Finally, we will quantitatively code cases of known innocence (exoneree case files) for what sparked suspicion about the innocent person, focusing on mentions of their behavior seeming “unusual”. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.