Appleton Police Dept. shows public its training for 'fair and impartial policing'
APPLETON, Wis. (WLUK) - Fox Valley police departments have been training on fair and impartial policing.
Now, the Appleton Police Department is showing the public what it learned and how everyone can use these tools, in uniform, or out of it.
Capt. Larry Potter say officers have to deal with implicit biases every day.
"Sometimes it can get us into trouble," he said. "It's a reminder to realize that we do that, all of us do, and then apply that to our jobs in policing."
Potter and Officer Jack Taschner set out to learn about these biases and teach other officers how to manage them.
"It's a reminder for the officers to take a step back, look in the mirror, and ask yourself, do I do some of those implicit reactions? Because your body wants to," Potter said.
Potter and Taschner took this training and brought it back to the Fox Valley, training officers at the Appleton Police Department as well as the Fox Valley Metro and Grand Chute police departments.
Monday, they held a learning session to show the public what the officers have been learning.
Janet Cardin, a resident of Kaukauna, attended the session.
"It's really interesting to note and see how the police are reacting, and want to make sure everything goes fair and goes well," she said.
The officers in charge say this type of training not only benefits law enforcement but also any member of the public.
Potter says all walks of life should have this conversation, for all of us to look in the mirror.
"People want to be aware of what their own thoughts are and what their own feelings are, and how that interacts with the environment," Cardin said.
So far, Potter says the trainings have been going well.
"The tone of the curriculum is to spur conversation, have some active thought and ask some tough questions and have some difficult conversations, and that's happened," he said.
Difficult conversations which can lead to a more open mind.
Kaukauna is the next department set to be trained on implicit biases.