% STAY SAFE OUT THERE ON THE
% ROADWAYS.
% >> ALL RIGHT.
% THANK YOU.
% >>> TURNING TO EDUCATION,
% SOPHISTICATED ALGORITHM CALLED
% DROP OUT EARLY WARNING SYSTEMS
% DO'S USED BY WISCONSIN ONLY A
% FEW LIKE IT.
% SYSTEM TRIES TO PREDICT WHETHER
% A STUDENT EXITING MIDDLE SCHOOL
% IS LIKELY TO GRADUATE FROM HIGH
% SCHOOL.
% ACCORDING TO THE REPORT FROM
% MARK UP.%PUBLICATION THAT LOO
KS AT USE OF
% BIG TECH.
% NUMBER OF DATA POINTS, TEST
% SCORES, DISCIPLINARY RECORDS AND
% FAMILY INCOME.
% UNIQUE AND CONTROVERSIAL DATA
% POINT IS RACE.
% DPI SAYS DO'S USES WHAT HAS
% ALREADY HAPPENED IN THE PAST
% WHAT WILL HAPPEN IN THE FUTURE.
% ALL IN ORDER TO CHANGE COURSE
% AND HELP INSURE STUDENTS
% IDENTIFIED AS AT-RISK DOING IN
% FACT GRADUATE ON TIME BY
% DIRECTING RESOURCES, SUPPORT AND
% INTERVENTIONS TO STUDENTS THAT
% MAY NEED THEM.
% WE TURN TO REPORT'S AUTHOR TODD
% FEATHERS, THANKS FOR BEING HERE.
% >> THANK YOU SO MUCH FOR HAVING
% ME.% >> YOU SEEM TO HAVE UN
COVERED
% PREDICTION FOR HIGH SCHOOL
% GRADUATION MOST OF US HAVE NEVER
% HEARD OF.
% HOW DOES IT WORK?
% >> IT IS A SYSTEM BUILT IN 2012
% TO PREDICT WHETHER STUDENTS WILL
% GRADUATE ON TIME.
% GENERATES PREDICTION EVERY
% STUDENT 6-9TH, MILD, MODERATE OR
% LOW RISK OF DROPPING OUT.
% SUPPOSED TO HELP STOOLS AND
% DIRECT RESOURCES, SUPPORT FOR
% STUDENTS THAT NEED IT MOST.
% WHAT WE FOUND SYSTEM IS WRONG
% NEARLY THREE QUARTERS OF THE
% TIME A STUDENT WON'T GRADUATE.
% LACK OF HISPANIC, THAN WHITE
% STUDENTS.
% ON TOP OF THAT, ACADEMIC
% RESEARCH BASED OUT OF UNIVERSITY
% OF CALIFORNIA BERKELEY CONDUCTED
% STUDY LARGEST OF ITS KIND ON
% DO'S.%NOT ACHIEVED PRIMARY G
OAL.
% >> SO THIS QUESTION IF IT IS
% WRONG ACCORDING TO YOUR
% REPORTING, THREE QUARTERS OF THE
% TIME, WHY IS THAT?
% >> SO BY SOME STANDARDS, DO'S IS
% VERY ACCURATE.
% IT IS CORRECT 97% OF THE TIME A
% STUDENT WILL GRADUATE.
% WHAT DPI DID IS CALL --
% CALIBRATE IT.
% SO THEY ARE SAYING, WE ARE
% WILLING TO LABEL A LOT OF
% STUDENTS LIKELY NON GRADUATES IN
% ORDER TO CAPTURE ALL OF THE
% ONES.%>> COULD IT BE ERROR A
S A RESULT
% OF RESOURCE AND INPUTS GIVEN TO
% THE STUDENTS THAT MIGHT BE
% PREDICTED NOT TO GRADUATE?
% >> THAT IS ONE OF THE THINGS
% UNIVERSITY OF CALIFORNIA
% BERKELEY RESEARCHES TESTED FOR.
% THEY WERE GOING TO STUDENTS
% LABELED HIGH RISK, YOU WOULD
% EXPECT TEN YEARS OF DATA FOR
% IMPROVED GRADUATION RATES.
% DATA SHOWED NO IMPACT ON
% GRADUATION RATES AND IDENTIFIED.
% >> TURNING TO ANOTHER DATA POINT
% LOADED INTO THIS ALGORITHM, THAT
% IS RACE.
% SOMETHING YOU CALL IN YOUR
% ARTICLE QUOTE A RACIALLY
% INEQUITABLE ALGORITHM BUILT BY
% THE STATE OF WISCONSIN.
% WHAT DID YOU LEARN WHY RACE IS
% USED AS A PREDICTOR?
% >> IMPORTANT TO REMEMBER THIS
% ALGORITHM DESIGNED BACK IN 2012.
% WE HAVE COME A LONG WAY HOW WE
% UNDERSTAND ALGORITHMS AND BIASES
% IN THE WAY, THINGS LIKE RACE CAN
% FACTOR INTO THESE PREDICTIONS.
% THIS ALGORITHM HASN'T CHANGED
% MUCH.% BACK AT THAT TIME, E
VEN STILL
% CURRENTLY, RESEARCHERS IN THIS
% FIELD OF EARLY WARNING SYSTEMS,
% USING DATA POINTS LIKE RACE
% IMPROVES ACCURACY.
% BETTER AT PREDICTING WHO WILL
% AND WON'T GRADUATE HIGH SCHOOL.
% YOU CAN SEE WHY THAT WOULD BE
% THE CASE.
% WISCONSIN STARK RACIAL
% DISPARITIES, WHITE STUDENTS
% GRADUATING AT A MUCH HIGHER RATE
% THAN BLACK STUDENTS.
% GROWING BODY OF RESEARCH AND
% ACADEMIC ARGUMENT SAY A SMALL
% ACCURACY IS NOT ENCODING
% HISTORICAL BIASES.
% >> I WANT TO GET TO SOMETHING
% THE DPI TOLD US, YOU QUOTED A
% SPOKES PERSON SAID EDUCATION
% SYSTEM IS SYSTEMATICALLY RACIST.
% SHE TOLD US, WHEN IT COMES TO
% LABELLING STUDENTS, I AGREE WE
% SHOULD BE CONCERNED ABOUT THE
% IMPACT THE WORDS HAVE ON OUR
% LEARNERS.%YET INTENTION OF T
HE LABEL IS TO
% PROVIDE ACCESS TO THE
% INTERVENTIONS THESE STUDENTS
% NEED.% I WISH WE DIDN'T NEE
D A LABEL.
% IT IS AN ATTEMPT TO PROVIDE
% RESOURCES TO THE LEARNERS THAT
% NEED IT MOST AND SCHOOLS NEED TO
% IDENTIFY FOR THEM.
% HOW DID THE STUDENTS LIKE
% LABELED HIGH-RISK AND RACE WAS
% PUT INTO THE ALGORITHM?
% >> YOU KNOW, FIRST THING NONE OF
% THE STUDENTS SPOKE TO KNEW THE
% ALGORITHM EXISTED OR SCHOOL WAS
% LOOKING AT THIS INFORMATION AND
% MAKING DECISIONS HOW THEY TREAT
% THEM.
% SHOCKED A LOT OF THE STUDENTS.
% SHOCKED ME WHEN I FIRST HEARD.
% BLACK AND HISPANIC STUDENTS
% OFTEN FELT LIKE SECONDARY SCHOOL
% SYSTEMS WITHIN THEIR OWN
% SCHOOLS.
% THEY WERE TREATED DIFFERENTLY
% THAN WHITE STUDENTS.
% THEY FEARED HAVING THE BEHIND
% THE SCENES LABELS WOULD
% PERPETUATE KIND OF THE CONSCIOUS
% BIASES.
% YOU CAN IMAGINE FOR INCOMING 9TH
% GRADERS, FIRST THING YOU SEE
% ABOUT A STUDENT I AM MODERATELY
% LOW RISK.
% >> ALL RIGHT.
% TODD FEATHERS, REALLY
% COMPLICATED AND INTERESTING
% REPORTING.
% THANKS VERY MUCH.
% >> THANK YOU SO MUCH AGAIN.
% >> FOR MORE ON THIS AND OTHER
% ISSUES FACING WISCONSIN VISIT