## 2 Materials and Methods
### Data Collection  
Data were collected through semi-structured interviews performed from November 2021 to March 2022. Semi-structured interviewing was chosen as a data collection method to create a balance between naturalism and control. The choice of greater control, such as restricting eligible participants to those who only have a high school education could influence the data in ways that could compromise the representativity of the subsequent analysis. In essence, implementing strict controls could inhibit the qualitative discovery of critical information, and add another layer of complexity in accessing an already hidden population. The planned duration of each interview was 60 min but ranged between 50 and 70 min in time and was conducted using the cloud-based, secure video platform Zoom. Upon accepting the interview request, participants were given verbal and written information about the study, told that they would receive a $60.00 USD gift card for their time, and once informed consent was obtained, an appointment was scheduled for an interview. The goal of providing a small monetary value incentive was to reduce the rate of recruitment failure based on hidden population dynamics with an already challenging study population [20] and to ensure that participants incur no costs and to provide a revenue-neutral experience. The interviewer had prior background experience working with law enforcement professionals and AI technologies, facilitating the execution of an interview protocol process to help to guide each interview and create efficient use of the allotted time. The participants were informed at the beginning of the interview that they could opt out at any time without repercussions. Additionally, the interviewer briefed all 20 participants that the interview data was confidential, and all participants consented to be audio-recorded. The interviews were transcribed using an intelligent verbatim transcription technique, reviewed for de-identification of any personal data, and coded using the qualitative analysis software MAXQDA. A data analysis protocol was developed to manage the results of the 20 studies. Initially, numerous dimensions were considered for data analysis, but similar to prior work from our group (e.g., [21]), data was coded based on the abductive inference approach to qualitative research. First-cycle coding consisted of establishing structural codes based on the interview questions and open (initial) coding. Second-cycle coding consisted of axial coding to facilitate the development of a thematic framework and to identify and map the overarching themes in the 20 interviews (see Figure 1). Two researchers thoroughly reviewed a coding pilot subset of four transcripts to establish intercoder reliability. Percent agreement between coders was 91.89%, with a Kappa (RK) of 0.93, well within the acceptable rates of consensus. Coders discussed coding conflicts and refined the definitions of codes, and then the remaining transcripts were analyzed independently by one researcher. Since the subject pool was drafted from participants attending university-based executive training programs, respondents report longer years of service (85% over ten years) and senior ranks (55% supervise other personnel) compared to a random distribution of sworn law enforcement officers. In addition, female officers and military veterans are slightly overrepresented in the sample (20% and 30%, respectively). Nationally in the United States, 12.3% of state and local law enforcement officers are female [22], and 19% of law enforcement officers are military veterans [23].