>> THE FOURTH ANNUAL NLM
LECTURE, OFFICE OF STRATEGIC
INITIATIVES ORGANIZED THIS
SERIES, TO RAISE AWARENESS
AROUND SOCIETAL AND ETHICAL
IMPLICATIONS IN CONDUCT OF
RESEARCH.
IT'S OUR HOPE THESE WILL FUEL
IMPORTANT CONVERSATIONS AROUND
NLM, NIH, BROADER BIOMEDICAL
RESEARCH COMMUNITY.
I'M PLEASED TODAY TO INTRODUCE
OUR GUEST LECTURER MEREDITH
BROUSSARD, ASSOCIATE PROFESSOR
AT NEW YORK UNIVERSITY CARTER
JOURNALISM INSTITUTE, AUTHOR OF
"MORE THAN A GLITCH," WHICH CAME
OUT LAST YEAR, AND AWARD-WINNING
2018 BOOK "ARTIFICIAL
UNINTELLIGENCE, HOW COMPUTERS
MISUNDERSTAND THE WORLD,"
RESEARCH ON ARTIFICIAL
INTELLIGENCE AND INVESTIGATIVE
REPORTING WITH FOCUS ON A.I.
ETHICS USING DATA ANALYSIS FOR
SOCIAL GOOD.
FORMER FEATURES EDITOR AT THE
"PHILADELPHIA INQUIRER," HAS SHE
HAS WORKED AT A SOFTWARE
DEVELOPER AT AT&T BELL LABS,
M.I.T. MEDIA LAB, FEATURE
STORIES AND ESSAYS APPEARED IN
THE "NEW YORK TIMES," "THE
ATLANTIC," SLATE AND BOX.
TODAY'S LECTURE WILL HELP US
THINK THROUGH CONCRETE
STRATEGIES TO MITIGATE BIAS IN
OUR APPROACH TO DEVELOPING
ARTIFICIAL INTELLIGENCE
TECHNOLOGIES, HER INSIGHTS COMES
AT A CRITICAL POINT.
IN THE LAST TWO YEARS
DISCUSSIONS AROUND TRUSTWORTHY
A.I. HAVE TAKEN OFF.
WE HAVE POWERFUL GUIDING
DOCUMENTS SUCH AS THE BLUEPRINT
FOR A.I. BILL OF RIGHTS FROM THE
WHITE HOUSE OFFICE OF SCIENCE
AND TECHNOLOGY POLICY IN OCTOBER
OF 2022, AND A.I. RISK
MANAGEMENT FRAMEWORK PUBLISHED
BY THE NATIONAL INSTITUTES OF
STANDARDS OF TECHNOLOGY, NIST,
LAST YEAR.
WE ALSO HAVE VERY CLEAR
DIRECTIVES ON ADVANCING
COMPREHENSIVE
WHOLE-OF-GOVERNMENT STRATEGY
INCLUDING AN EXECUTIVE ORDER ON
THE SAFE, SECURE, AND
TRUSTWORTHY DEVELOPMENT AND USE
OF ARTIFICIAL INTELLIGENCE.
SIGNED BY THE PRESIDENT IN
OCTOBER 30 OF 2023.
THESE EFFORTS ARE DEDICATED TO
UNLOCKING POTENTIAL OF
ARTIFICIAL INTELLIGENCE TO SOLVE
SOME OF OUR MOST PRESSING
CHALLENGES, ACCELERATING
SCIENTIFIC DISCOVERY, WHILE
PROTECTING AGAINST THE
TECHNOLOGIES' POTENTIAL RISKS.
AT NIH, THAT MEANS WORKING TO
MITIGATE BIAS, PRESERVE PRIVACY,
PROMOTE ETHICAL APPROACHES TO
INTEGRATING A.I. AND BIOMEDICAL
RESEARCH.
THIS WILL SERVE TO ADVANCE
EQUITABLE HEALTH OUTCOMES FOR
COMMUNITIES ACROSS THE NATION.
AS THE WORLD'S LARGEST
BIOMEDICAL RESEARCH LIBRARY,
WITH COMMITMENT TO PRESERVING
AND PROTECTING THE PUBLIC TRUST,
NLM PLAYS AN IMPORTANT ROLE IN
REALIZING THIS GOAL AND
AFFIRMING THIS COMMITMENT.
WE HOUSE AND MAKE ACCESSIBLE
VALUABLE DATA RESOURCES,
HARNESSED TO DRIVE DISCOVERY.
WE'RE A LEADER IN HEALTH DATA
STANDARDS DEVELOPMENT, THAT
ENABLES THE DISCOVERY AND WE'RE
A DRIVER OF DATA SCIENCE THROUGH
OUR EXTRAMURAL AND INTRAMURAL
RESEARCH PROGRAMS.
WITH THIS FRAMING IN MIND, I'M
LOOKING FORWARD TO HEARING HER
WALK US THROUGH EXAMPLES OF THE
POTENTIAL CONSEQUENCES OF
DEVELOPING ALGORITHMS WITHOUT
CAREFUL CONSIDERATION OF
POTENTIAL BIASES.
EVEN WHEN THESE TECHNOLOGIES ARE
DEVELOPED WITH BEST INTENTIONS.
ALSO EAGER TO HEAR INSIGHTS ON
PRACTICAL SOLUTIONS AND LEARN
MORE ABOUT HER IDEAS FOR
DESIGNING BETTER SYSTEMS FOR
MORE EQUITABLE OUTCOMES.
THIS LECTURE WILL BE FOLLOWED BY
A QUESTION-AND-ANSWER PORTION.
IF YOU HAVE ANY QUESTIONS, MAKE
YOUR WAY TO A MICROPHONE THAT
WILL BE PROVIDED, IF YOU'RE WITH
US IN PERSON.
IF JOINING ONLINE USE THE ASK A
QUESTION FEATURE ON VIDEOCAST.
PLEASE WELCOME ME NOW IN JOINING
MS. BROUSSARD.
>> IT'S GREAT TO BE WITH YOU.
THANK YOU FOR HAVING ME.
SO I'M GOING TO TALK TODAY ABOUT
SOME IDEAS FROM MY MOST RECENT
BACK, "MORE THAN A GLITCH,
CONFRONTING RACE, GENDER, AND
ABILITY BIAS IN TECH."
YOU'RE A KICKOFF STOP ON THE
PAPER BACK TOUR, BECAUSE THE
PAPERBACK IS JUST RELEASING.
THANK YOU FOR THAT.
AND SPECIFICALLY, I WANT TO TALK
A LITTLE BIT ABOUT CANCER.
I WANT TO TALK ABOUT HOW LOOKING
AT CANCER CAN HELP US UNDERSTAND
ALGORITHMIC BIAS.
I'M GOING TO DO THAT BY TELLING
YOU A STORY ABOUT CANCER.
BUT FIRST I'M GOING TO START BY
TALKING ABOUT ARTIFICIAL
INTELLIGENCE.
SOMETHING THAT I THINK IS REALLY
IMPORTANT TO EMPHASIZE WHEN
WE'RE TALKING ABOUT A.I. WITH
THE GENERAL PUBLIC IS NEED TO
EMPHASIZE A.I. IS NOT MAGIC AND
THAT IT IS NOT HOLLYWOOD VISION.
OKAY?
BECAUSE EVERYBODY YOU TALK TO,
WELL, EVERY NON-A.I. SCIENTIST,
IS LIKE -- ACTUALLY EVERYBODY
YOU TALK TO IS THINKING ABOUT
THE TERMINATOR.
OR THEY ARE THINKING ABOUT "STAR
WARS" OR "STAR TREK," COOL
HOLLYWOOD PORTRAYALS OF A.I.
BECAUSE OF A SPECIFIC WAY OUR
BRAINS WORK.
OUR BRAINS ARE BETTER AT
RECALLING STORIES THAN FACTS AND
SPECIFICS.
SO, WE ALWAYS GO TO HOLLYWOOD
FIRST.
HOLLYWOOD TELLS AMAZING STORIES
SO THE TERMINATOR IS STUCK IN
OUR BRAINS.
I WILL ADMIT TO HAVING SPENT A
LOT OF TIME THINKING ABOUT THE
TERMINATOR MYSELF.
BUT IT'S NOT REAL.
WE NEED TO EMPHASIZE WHEN WE'RE
TALKING TO THE GENERAL PUBLIC
THERE'S LOTS OF STUFF THAT'S
IMAGINARY, WHAT'S REAL ABOUT
A.I., IT'S COMPLICATED AND
BEAUTIFUL.
AND MATH IS GREAT BUT IT IS NOT
GOING TO RISE UP AND TAKE OVER.
THERE'S A LOT OF ANXIETY ABOUT
A.I. TAKEOVERS AND ROBOT
APOCALYPSES OUT THERE AND I JUST
LIKE TO GET THAT OUT OF THE WAY
BECAUSE IT ALLOWS US TO HAVE
DEEPER CONVERSATIONS ABOUT
ARTIFICIAL INTELLIGENCE WHICH I
THINK IS REALLY IMPORTANT.
AND SPECIFICALLY, THE
CONVERSATION THAT I'M REALLY
INTERESTED IN HAVING IS ABOUT
BIAS IN A.I. AND THE WAY A.I.
DISCRIMINATES.
SO, I REALLY LIKE AN IDEA THAT
COMES FROM BENJAMIN'S
TECHNOLOGY, THE IDEA THAT A.I.
SYSTEMS, AUTOMATED SYSTEMS,
DISCRIMINATE BY DEFAULT.
FOR A LONG TIME THERE'S BEEN
THIS IDEA THAT I CALL TECHNO
CHAUVINISM.
WHAT I WOULD ARGUE IS WE SHOULD
THINK ABOUT USING THE RIGHT TOOL
FOR THE TASK.
BECAUSE SOMETIMES THE RIGHT TOOL
FOR THE TASK IS UNDOUBTEDLY A
COMPUTER, SOMETIMES IT'S
SOMETHING SIMPLE LIKE A BOOK IN
THE HANDS OF A CHILD SITTING ON
A PARENT'S LAP, ONE IS NOT
INHERENTLY BETTER.
BUT TECHNO CHAUVINIST
PERSPECTIVE TELLS US A.I. OR
COMPUTATIONAL SOLUTIONS ARE
OBJECTIVE OR UNBIASED OR
SUPERIOR.
BUT WE CAN PUSH BACK AND LET IT
GO AND THEN CERTAIN THINGS
BECOME CLEAR, IT BECOMES CLEAR
THAT THE PROBLEMS OF THE PAST
ARE REFLECTED IN THE DATA THAT
WE USE TO TRAIN A.I. SYSTEMS.
SO WHEN WE MAKE A MACHINE
LEARNING SYSTEM, WHAT WE DO IS
TAKE A WHOLE BUNCH OF DATA,
USUALLY FROM THE INTERNET, DUMP
IT IN THE COMPUTER AND COMPUTER
MAKES A MODEL.
MODEL SHOWS MATHEMATICAL PATTERN
OF DATA AND WHAT WE CAN DO IS
ALL KINDS OF COOL STUFF.
WE CAN USE THAT MODEL TO MAKE
PREDICTIONS OR DECISIONS OR TO
GENERATE NEW TEXT OR IMAGES OR
AUDIO OR VIDEO OR WHATEVER.
BUT THE PROBLEMS OF THE PAST ARE
THINGS LIKE DISCRIMINATION.
RACISM, SEXISM, ABLEISM,
STRUCTURAL INEQUALITY, WHICH
UNFORTUNATELY ALL OCCUR IN THE
WORLD.
WE DO NOT LIVE IN A PERFECT
WORLD.
ALL OF THESE PATTERNS OF
DISCRIMINATION ARE REFLECTED IN
THE DATA WE USE TO TRAIN A.I.
SYSTEMS.
HOW DOES THIS MANIFEST?
WELL, ONE COMES FROM THE MARKUP,
WHICH IS TERRIFIC.
ALGORITHM ACCOUNTABILITY NEWS
ORGANIZATION, HIGHLY RECOMMEND
READING THEM IF YOU'RE NOT
READING THEM ALREADY.
THE MARKUP DID AN INVESTIGATION
ABOUT SECRET BIAS HIDDEN IN
MORTGAGE-APPROVAL ALGORITHMS, 40
TO 80% MORE LIKELY TO DENY
BORROWERS OF COLOR THAN WHITE
COUNTERPARTS, IN SOME METRO
AREAS MORE THAN 250%.
NOW A DATA SCIENTIST MIGHT LOOK
AT THIS AND SAY, WELL, IT'S JUST
WHAT'S IN THE DATA.
YOU KNOW, WHAT'S PROBLEMATIC?
A SOCIOLOGIST MIGHT SAY, ALL
RIGHT, WELL, CLEARLY WHAT'S
HAPPENING IS THE MORTGAGE
APPROVAL ALGORITHMS ARE FED WITH
DATA ABOUT WHO GOT MORTGAGES IN
THE PAST AND THE U.S., VERY LONG
HISTORY OF RESIDENTIAL
SEGREGATION, HAS A LONG HISTORY
OF FINANCIAL DISCRIMINATION IN
LENDING.
AND SO THE ALGORITHMS ARE
PICKING UP ON THOSE PATTERNS OF
DISCRIMINATION AND REPRODUCING
THEM.
SO IT'S A GOOD EXAMPLE OF HOW
DISCRIMINATION HAPPENS BY
DEFAULT INSIDE THE AUTOMATED
SYSTEMS.
WE COULD MATHEMATICALLY PUT A
FINGER ON THE SCALE.
WE COULD EVALUATE OUR ALGORITHMS
AND SAY, OKAY, HOW MANY LOANS
ARE GOING TO PEOPLE OF DIFFERENT
CATEGORIES/PROTECTED CLASSES AND
IF IT'S NOT A SUFFICIENT
PERCENTAGE WE CAN CHANGE THE
ALGORITHM SO THAT IT GIVES MORE
LOANS TO PEOPLE FROM THOSE
PROTECTED CATEGORIES.
IS THAT ACTUALLY HAPPENING?
NOT QUITE SO MUCH.
ANOTHER EXAMPLE IS IN FACIAL
RECOGNITION.
YOU'RE PROBABLY FAMILIAR WITH
THE GENDER SHADES PROJECT, WHERE
THE INVESTIGATORS LOOKED AT
FACIAL RECOGNITION SYSTEMS AND
FOUND THAT THE MAJOR FACIAL
RECOGNITION SYSTEM WORKED BETTER
ON MEN THAN WOMEN, BETTER ON
PEOPLE WITH LIGHT SKIN THAN DARK
SKIN, TRANSAND NON-BINARY FOLKS
ARE GENERALLY NOT REPRESENTED IN
THE DATASETS AT ALL.
WHEN DO YOU INTERSECTIONAL
ANALYSIS, THE SYSTEMS WORK BEST
OF ALL ON MEN WITH LIGHT SKIN
AND WORST OF ALL ON WOMEN WITH
DARK SKIN.
AND SO, THIS IS ABSOLUTELY A
PROBLEM THAT COULD BE ADDRESSED
WITH THE TRAINING DATA, DATA
SCIENTIST FIRST QUESTION IS
USUALLY CAN'T YOU JUST MAKE THE
TRAINING DATA MORE DIVERSE?
ABSOLUTELY, YES, THAT WOULD MAKE
THESE SYSTEMS MORE ACCURATE.
BUT ONE THING WE DO IS PUSH
FURTHER AND LOOK AT THE USES OF
FACIAL RECOGNITION.
WE CAN LOOK AT A.I. IN CONTEXT,
AND SAY, WELL, WE NEED TO THINK
ABOUT THINGS LIKE FACIAL
RECOGNITION USE IN POLICING
BECAUSE FACIAL RECOGNITION USED
TO UNLOCK YOUR PHONE IS A LOW
RISK USE.
DOESN'T WORK FOR ME MOST OF THE
TIME BUT I DON'T CARE, I JUST
USE MY PASSWORD.
FACIAL RECOGNITION USED BY
POLICE ON REALTIME VIDEO
SURVEILLANCE FEEDS IS GOING TO
BE A HIGH RISK USE BECAUSE IT'S
GOING TO MISIDENTIFY WOMEN AND
PEOPLE OF COLOR MORE OFTEN AND
GET PEOPLE CAUGHT UP IN CRIMINAL
JUSTICE SYSTEM WHO DON'T NEED TO
BE CAUGHT UP IN THE CRIMINAL
JUSTICE SYSTEM.
IT'S A HIGH-RISK USE THAT MIGHT
NEED TO BE REGULATED.
SO WE NEED TO -- OH, SURE.
BETTER?
OKAY.
SORRY, ONLINE FOLKS.
ALL RIGHT, SO LET'S NOT USE
FACIAL RECOGNITION IN POLICING.
SO, LET'S TALK ABOUT CANCER.
I HAVE AN A.I. STORY I WANT TO
GET OFF MY CHEST.
IT IS ABOUT THE TIME THAT I GOT
BREAST CANCER.
IT WAS BEGINNING OF THE
PANDEMIC.
AND I GOT THE NEWS THAT YOU
NEVER WANT TO GET.
AND I HAD BREAST CANCER.
IT WAS REALLY HARD, I'M FINE
NOW, GRATEFUL TO THE DOCTORS AND
MEDICAL PROFESSIONALS WHO TOOK
CARE OF ME.
AND I LEARNED A WHOLE BUNCH OF
THINGS ALONG THE WAY THAT I
THINK ARE USEFUL TO TALK ABOUT
WHEN WE TALK ABOUT WHAT THE
GENERAL PUBLIC UNDERSTANDS ABOUT
A.I. AND HOW WE CAN TALK ABOUT
A.I. IN MEDICINE WITH THE
GENERAL PUBLIC.
SO ONE OF THE THINGS THAT I DID
WHEN I FIRST GOT DIAGNOSED IS I
FREAKED OUT, BECAUSE EVERYBODY
FREAKS OUT WHEN THEY FIRST GET
DIAGNOSED, AND THE WAY THAT YOU
FREAK OUT TENDS TO BE CONSISTENT
WITH YOUR PERSONALITY.
SO, MY FREAKOUT INVOLVED
OBSESSIVIVELY READING EVERYTHING
I POSSIBLY COULD.
I SPENT SO MUCH TIME ON
PubMed AND READ EVERY SINGLE
THING IN EVERY SINGLE DROPDOWN
AND EVERY SINGLE PART OF MY
ELECTRONIC MEDICAL RECORD, WHICH
IS WHERE I STUMBLED ACROSS A
SCAN THIS SCAN WAS READ BY DR.
SO-AND-SO AND DR. A.I.
WHY IS THIS A.I. READING MY
SCAN?
WHAT DID IT FIND?
WHO WROTE THIS A.I.?
WHO KIND OF BIAS IS IN IT?
AND I HAD A LITTLE MOMENT AND
THEN I FORGOT ABOUT IT BECAUSE,
YOU KNOW, CANCER.
AND THEN I CAME BACK TO IT A
LITTLE WHILE LATER.
A YEAR OR SO LATER.
I WAS FEELING BETTER.
IT WAS COVID.
AND I DECIDED TO GET A LITTLE
BIT CARRIED AWAY.
I DECIDED THAT I WAS GOING TO DO
AN EXPERIMENT.
I WAS GOING TO DOWNLOAD MY OWN
SCANS AND RUN THEM THROUGH AN
OPEN SOURCE A.I. IN ORDER TO
WRITE ABOUT THE STATE-OF-THE-ART
AND A.I.-BASED CANCER DETECTION.
AND I WILL ADMIT THAT I GOT A
LITTLE CARRIED AWAY, THIS WAS
MAYBE A LITTLE EXTREME.
BUT I WAS TRYING TO REPRODUCE
THESE THE EXPERIMENT.
I HAVE ALWAYS BEEN REALLY
INTERESTED IN REPRODUCIBILITY.
AND OF COURSE THE IDEA IN
REPRODUCIBILITY YOU WOULD
PUBLISH YOUR CODE AND DATA
ONLINE, OTHER PEOPLE CAN
REPRODUCE YOUR EXPERIMENT AND
VALIDATE IT.
WE DO THIS A LOT IN JOURNALISM
WHEN WE PUBLISH THE CODE AND
DATA WE USE TO DO A PARTICULAR
ANALYSIS.
ESPECIALLY ALGORITHMIC
ACCOUNTABILITY REPORTING.
AND SO, I TOOK MY SCANS, AND I
RAN THEM THROUGH AN A.I.
I HAD A LOT OF MISCONCEPTIONS,
IT TURNED OUT.
AND MY MISCONCEPTION ABOUT HOW
A.I. WOULD BE USED IN CANCER
DETECTION ARE NOT UNCOMMON.
SO, HERE IS A PICTURE OF, YOU
KNOW, SOMEBODY'S MAMMOGRAM.
SOME MAMMOGRAM THAT I FOUND ON
THE INTERNET, NOT MY ACTUAL
MAMMOGRAM.
AND I THOUGHT THAT I WAS GOING
TO TAKE MY ENTIRE ELECTRONIC
MEDICAL RECORD, FEED IT IN, AND
I WAS GOING TO PUT IN ALL OF MY
SCANS AND, YOU KNOW, THE 3D
VIDEO, BLAH, BLAH, BLAH, IT WAS
GOING TO EVALUATE EVERYTHING AND
THEN LIKE POP OUT AN ANSWER.
LIKE WE THINK YOU HAVE CANCER.
BUT, NO.
THIS IS TOTALLY WRONG.
I ALSO THOUGHT THAT I WAS GOING
TO DO AN EXPERIMENT WHERE I
WOULD CHANGE MY RACE AND SEE IF
THE PREDICTION CHANGED.
AND THIS WAS A GOOD PREMISE FOR
AN EXPERIMENT BUT ALSO TOTALLY
WRONG BECAUSE THERE'S NO RACE IN
THE A.I. THAT I USED.
SO, ONE OF MY MISCONCEPTIONS WAS
IT WAS GOING TO LOOK AT MY
ENTIRE RECORD.
NO, THE WAY THAT MOST CANCER
DETECTION A.I. THAT WE HAVE
RIGHT NOW WORKS IS THAT YOU PUT
IN A FLAT IMAGE, AND WHAT IT
WILL DO IS IT WILL IDENTIFY AN
AREA OF CONCERN ON THE IMAGE.
SO, I WAS A LITTLE SURPRISED TO
HEAR THIS.
BUT I DOWNLOADED THE STUFF, I
GOT ALL THE CODE WORKING, I FED
IN MY IMAGES AND I GOT NOTHING.
IT WAS LIKE, YOU KNOW, I KNOW
THERE'S CANCER HERE.
LIKE MY DOCTOR HAS SEEN IT.
LIKE I GOT A MASTECTOMY CAUSE
THERE WAS CANCER HERE, WHY CAN'T
THE A.I. SEE IT?
IT TURNED OUT THAT MY IMAGE
RESOLUTION WAS TOTALLY WRONG,
AND I HAD TO GO THROUGH A
RIGMAROLE OF TRYING TO GET
ACCESS TO HIGH RESOLUTION
VERSION OF MY SCANS.
AFTER A MONTH OR TWO OF
WRANGLING I FINALLY GAVE UP AND
ASKED THEM TO SEND ME A CD IN
THE MAIL AND I HAD TO BUY A CD
READER.
THIS IS 2007, I DON'T KNOW.
SO, I GOT IT GOING.
AND EVENTUALLY IT DID IDENTIFY
AN AREA OF CONCERN.
THIS IS WHAT AN AREA OF CONCERN
MIGHT LOOK LIKE.
AGAIN, SOME IMAGE I GOT OFF THE
INTERNET.
AND WHAT I FOUND WAS THAT I GOT
A SCORE OF -- IT GAVE ME -- IT
IDENTIFIED AN AREA OF CONCERN
AND GAVE ME A SCORE.
AND I WAS REALLY SURPRISED THAT
IT DIDN'T LIKE KIND OF GIVE ME A
BIG -- LIKE A BIG RIGMAROLE.
WHEN YOU TEXT SOMEBODY
CONGRATULATIONS IT GIVES YOU
BALLOONS AND STUFF.
I WAS SORT OF EXPECTING THAT BUT
LIKE YOU HAVE CANCER!
YOU DON'T HAVE CANCER!
THAT'S NOW HOW IT WORKS.
SO, THAT WAS ONE OF THE THINGS
THAT I LEARNED ALONG THE WAY,
THAT EVEN WHEN YOU ARE A
RESEARCHER WHO WORKS ON PEOPLE'S
UNREALISTIC EXPECTATIONS AROUND
A.I., YOU ACTUALLY YOURSELF HAVE
UNREALISTIC EXPECTATIONS AROUND
A.I., AND IT'S USEFUL TO KEEP
THIS IN MIND WHEN WE'RE
COMMUNICATING WITH THE PUBLIC
ABOUT WHAT A.I. DOES.
ONE OF THE THINGS THAT I WAS
OBSESSED WITH WAS HOW LIKELY AM
I TO DIE FROM THIS.
RIGHT?
BECAUSE THAT IS THE BIG QUESTION
THAT YOU HAVE WHEN YOU ARE
DIAGNOSED WITH CANCER.
I DON'T KNOW WHAT CANCER
RESEARCHERS' BIG QUESTIONS ARE.
ALL I KNOW IS FROM THE
PERSPECTIVE OF SOMEBODY
DIAGNOSED.
AND WHAT I KNEW WAS THAT BLACK
WOMEN ARE 40% MORE LIKELY TO DIE
FROM BREAST CANCER.
BUT I AM -- MY MOM IS BLACK.
MY DAD WAS BLACK, MY MOM WAS
WHITE, AND MY MOM DIED OF BREAST
CANCER WHEN SHE WAS 50.
AND I WONDERED, OKAY, FOR THE
PURPOSES OF MEDICAL STATISTICS,
WOULD I BE CONSIDERED BLACK?
WOULD I BE CONSIDERED WHITE?
WHAT IS MY RISK PROFILE?
AND I ASKED MY DOCTOR ABOUT
THIS.
MY DOCTOR EXPLAINED THAT, WELL,
ACTUALLY WHAT WE DO WHEN THERE
ARE DIFFERENCES IN PATTERNS, OR
PREVALENCE BY RACE, IS FOR YOU
WE RUN YOUR NUMBERS BOTH WAYS AS
IF YOU WERE BLACK AND AS IF YOU
WERE WHITE, AND THEN WE JUST
ASSUME YOUR ACTUAL NUMBER IS
SOMEWHERE IN BETWEEN.
I WAS LIKE, THAT'S SCIENCE?
SO, I COULDN'T FIGURE OUT, OKAY,
AM I LIKELY TO DIE FROM THIS,
NOT LIKELY TO DIE?
IT MADE ME THINK, OKAY, THERE'S
A LOT OF AMBIGUITY IN DIAGNOSIS,
A LOT OF AMBIGUITY IN
STATISTICS.
AND WE'RE NOT NECESSARILY PAYING
ENOUGH ATTENTION TO THAT
AMBIGUITY WHEN WE ARE INCLUDING
THESE SYSTEMS IN ALGORITHMS.
RIGHT?
SO WE NEED TO TALK MORE ABOUT
THE AMBIGUITY.
I ALSO THOUGHT IT WAS
SIGNIFICANT THAT COMPUTERS DON'T
DIAGNOSE THE SAME WAY THAT
DOCTORS DO, RIGHT?
THE COMPUTER IDENTIFIES AN AREA
OF CONCERN AND GIVES YOU A
SCORE.
THIS PARTICULAR ONE GAVE ME A
SCORE BETWEEN ZERO AND ONE.
I THOUGHT THIS MEANT YOU HAVE A
20% CHANCE OF HAVING CANCER IN
THIS AREA.
BUT NO, RESEARCHER TOLD ME THAT
IT'S ABSOLUTELY EMPIRICALLY NOT
A PREDICTION.
IT IS JUST A SCORE.
TURNS OUT THIS IS BECAUSE OF THE
LEGAL LANDSCAPE, BECAUSE, YOU
KNOW, THERE'S -- HEAVILY
REGULATED INDUSTRY AND HE COULD
GET IN BIG TROUBLE FOR SAYING
THERE'S 20% CHANCE.
OH, YEAH, THIS IS A .2 SCORE.
ALL RIGHT, FINE.
ANOTHER THING THAT PEOPLE
USUALLY DON'T -- THE GENERAL
PUBLIC GENERALLY DOES NOT
REALIZE ABOUT A.I. THAT'S
IMPORTANT TO SAY IN CONTEXT OF
CANCER IS THAT THESE SYSTEMS ARE
TUNED TO EITHER GIVE YOU A
HIGHER RATE OF FALSE POSITIVES
OR HIGHER RATE OF FALSE
NEGATIVES.
SO, A FALSE POSITIVE WOULD BE
WHERE IT SAYS, YEP, HAVE CANCER,
DON'T HAVE NEGATIVE.
FALSE NEGATIVE, NOPE, NO CANCER,
AND DO YOU HAVE MEDICINE.
IN MEDICINE IT'S CONSIDERED
HIGHER STAKES TO HAVE MORE FALSE
NEGATIVES THAN FALSE POSITIVES.
WE ARE ALL UNITED IN WANTING TO
SAVE MORE LIVES, FOR MORE PEOPLE
TO BE DIAGNOSED AND TREATED
EFFECTIVELY.
SO, THERE'S CONSENSUS,
APPROPRIATELY, THESE SYSTEMS
SHOULD BE TUNED TO HAVE HIGHER
RATES OF FALSE POSITIVES THAN
FALSE NEGATIVES.
IT'S KIND OF THE STRANGE THING
TO REALIZE YOU HAVE TO DECIDE
HOW WRONG YOU WANT YOUR
DIAGNOSTIC SYSTEM TO BE.
AND PEOPLE ARE PRETTY
UNCOMFORTABLE WITH THAT.
I ALSO GOT REALLY INTERESTED IN
RACIAL DISPARITIES.
IN DIAGNOSIS AND THEN ALSO IN
A.I.-READ SCANS.
THERE IS THIS REALLY INTERESTING
STUDY THAT LOOKED AT MACHINE
LEARNING MODELS, DIAGNOSING I
THINK IT WAS PLEURAL EFFUSIONS.
YOU COULD FACT CHECK ME ON THIS
ONE.
AT ANY RATE, THEY WERE LOOKING
AT THESE MODELS THAT WERE
EXTREMELY EFFECTIVE ON DATA FROM
ONE HOSPITAL AND THEN THEY ADDED
ANOTHER HOSPITAL, AND IT WAS
REALLY EFFECTIVE ON THE DATA
FROM THE NEXT HOSPITAL.
AND THEN THEY ADDED IN THE DATA
ON THE PATIENT'S RACE.
AND IT TURNED OUT THAT THESE
MODELS --
>> RECORDING IN PROGRESS.
>> OH.
THESE MODELS WERE DIFFERENTIALLY
ACCURATE, BASED ON RACE.
RIGHT?
BUT IT'S JUST SCANS OF PEOPLE'S
INSIDES.
RESEARCHERS ARE LIKE WHY IS THIS
HAPPENING?
AND NOBODY'S BEEN ABLE TO
EXPLAIN WHY THIS IS HAPPENING,
RIGHT?
REALLY BIG UNKNOWN, RIGHT?
WE KNOW THINGS LIKE FACIAL
RECOGNITION SYSTEMS ARE
DIFFERENTIALLY ACCURATE BASED ON
SKIN TONES, BUT IT DOESN'T MAKE
ANY SENSE TO A HUMAN OR AT LEAST
TO THIS HUMAN THAT THE SCAN OF
THE INSIDE WOULD BE DIFFERENT.
SO BIG QUESTION MARK.
AND ONE OTHER THING I FIND
REALLY INTERESTING IS THE WAY
THAT RACE GETS ENCODED IN
MEDICAL SYSTEMS.
AND IN ALGORITHMS.
AS IF IT WERE REAL.
SO RACE IS SOCIAL.
IT'S NOT BIOLOGICAL.
THERE ARE GENETIC DIFFERENCES
BETWEEN PEOPLE, RIGHT?
BUT RACE DOES NOT DESCRIBE THOSE
GENETIC DIFFERENCES.
THE RACE IS A SOCIAL CONSTRUCT,
NOT A BIOLOGICAL REALITY, BUT
SOMETIMES GETS EMBEDDED IN
ALGORITHMS AND IN MEDICAL
SYSTEMS AS IF IT WERE A
BIOLOGICAL REALITY.
SO YOU'RE PROBABLY FAMILIAR WITH
RACE CORRECTION.
SO, IN KIDNEY DISEASE DIAGNOSIS,
THERE'S A PRETTY SIGNIFICANT
EXAMPLE OF EGFR, FILTRATION
RATES, SO WHEN YOUR EGFR GETS
DOWN TO 20, 20% KIDNEY FUNCTION,
YOU'RE ELIGIBLE FOR THE KIDNEY
TRANSPLANT LIST.
IT DOESN'T MEAN YOU'LL GET IT,
JUST ELIGIBLE TO START WAITING
FOR A KIDNEY THAT MATCHES.
MIGHT TAKE YEARS.
FOR A VERY LONG TIME THERE WAS A
RACE CORRECTION BUILT IN SO THAT
IF YOU WERE BLACK, YOU GOT A
MULTIPLIER APPLIED IN YOUR EGFR
CALCULATION WHICH MEANT BLACK
PATIENTS HAD TO BE SICKER THAN
ANY OTHER PATIENTS IN ORDER TO
QUALIFY FOR THE KIDNEY
TRANSPLANT LIST.
WE ALSO SEE RACE CORRECTIONS IN
THINGS LIKE CONCUSSION.
CONCUSSION CALCULATIONS.
THERE WAS A CASE IN THE NFL
WHERE THE NFL MADE THIS
MULTI-BILLION DOLLAR SETTLEMENT
TO PLAYERS WHO HAD EXPERIENCED
MULTIPLE CONCUSSIONS AND HAD
REALLY TRAUMATIC BRAIN INJURIES
AND LONG-TERM EFFECTS, AND THEY
USED A CALCULATION TO FIGURE OUT
HOW MUCH EACH PLAYER WAS OWED,
AND BLACK PLAYERS WERE GIVEN A
RACE CORRECTION UNDER THE
ASSUMPTION THAT THEIR MENTAL
CAPACITY WAS DIMINISHED RELATIVE
TO OTHER PLAYERS, AND THEY WERE
OWED LESS MONEY.
THEY FOUGHT IT.
THEY WON.
BUT WE DO SEE THINGS LIKE RACE
CORRECTION LINGERING.
THIS LINGERED IN KIDNEY
ALGORITHM UNTIL VERY RECENTLY.
ACTUALLY UNTIL COPY EDITS FOR MY
BOOK WERE DUE, I WAS GLAD IT
CHANGED IN TIME TO GET INTO MY
BOOK.
SO, THE KIDNEY ALGORITHM HAS
CHANGED.
EGFR NO LONGER INCLUDES A RACE
CORRECTION.
BUT WE DO HAVE TO THINK ABOUT
EVERY SINGLE LAB THAT USED THESE
DIFFERENT NUMBERS, RIGHT?
SO EVERY SINGLE LAB WOULD TAKE
YOUR RACE ON YOUR PATIENT CHART
AND WOULD CALCULATE IT AS EITHER
BLACK OR WHITE, AND I DISCOVERED
THE OTHER DAY THAT MY LAB WAS
ACTUALLY STILL CALCULATING IT.
IT'S JUST THAT THE NUMBER GOT
MUSHED AND BY THE TIME I SAW IT
IN MY ELECTRONIC MEDICAL RECORD
IT HAD BEEN, YOU KNOW, THE TWO
NUMBERS HAD BEEN SQUISHED
TOGETHER OR SOMETHING.
SO, WE HAVE TO THINK ABOUT THESE
SOCIOTECHNICAL SYSTEMS, THINK
ABOUT THE DATA PIPELINES, AND
WHEN WE MAKE CHANGES LIKE WE
CHANGE THE FORMULA TO ELIMINATE
RACE CORRECTION VARIABLE, WE DO
HAVE TO THINK ABOUT THE
PIPELINES TO THINK ABOUT IT'S
COMPLICATED TO CHANGE THESE
THINGS AND IMPLICATIONS OF A.I.
MODELS, RIGHT?
IF THE A.I. MODEL IS BEING
TRAMMED WITH SOME KIND OF
DISCRIMINATORY ASSUMPTION INSIDE
IT AND THEN WE REALIZE IT, THEN
YOU HAVE TO GO AND YOU HAVE TO
RETRAIN THE MODEL AND YOU HAVE
TO CHANGE THE WHOLE PIPELINE,
IT'S ACTUALLY AN EXTREMELY,
EXTREMELY COMPLICATED AND
RESOURCE-INTENSIVE EFFORT.
WE SHOULD BE DOING IT.
BUT IT'S WAY MORE COMPLICATED
THAN EVERYBODY IMAGINES BECAUSE
WE GENERALLY THINK ABOUT
COMPUTATIONAL SOLUTIONS AS BEING
FAST, CHEAP, EFFICIENT, AND THEY
ARE NOT ALL OF THESE THINGS AT
THE SAME TIME.
LLMs ARE ON EVERYBODY'S MINDS,
AND UNFORTUNATELY LLMs HAVE
THE SAME KINDS OF PROBLEMS WHEN
IT COMES TO RACE-BASED MEDICINE.
THEY HAVE THE BIASES THAT WE
TALKED ABOUT BEFORE.
SO LLMs ARE PRETTY BAD AT
MATH.
SO, NO, THEY ARE NOT CALCULATING
ANYBODY'S EGFR SCORE.
BUT THEY ARE DOING THINGS LIKE
PROPAGATING RACE-BASED MEDICINE
THAT IS HARMFUL, THAT IS RACIST,
THAT IS PROBLEMATIC, IN PART
BECAUSE THESE LLMs, LARGE
LANGUAGE MODELS, GENERATIVE A.I.
SYSTEMS, DO NOT -- THEY ARE NOT
PARSING FOR CONTENT.
THEY ARE BASICALLY LANGUAGE
CALCULATORS.
THEY ARE GIVING US STRINGS OF
TEXT BASED ON STRINGS OF TEXT
BASED IN THE PAST.
RACISM IS NOT GOOD BUT
UNFORTUNATELY IT IS POPULAR.
AND POPULAR IS USED AS A PROXY
FOR GOOD INSIDE LARGE ALGORITHMS
(INDISCERNIBLE).
AND OVERALL, RACE AND MACHINE
LEARNING IS A LITTLE BIT OF A
MESS.
SO, AS I SAID BEFORE, RACE IS
OFTEN EMBEDDED IN THESE
COMPUTATIONAL SYSTEMS, AS --
WELL, RACISM IS EMBEDDED IN
MEDICAL SYSTEMS AND ABSOLUTELY
EMBEDDED IN ML SYSTEMS, MACHINE
LEARNING SYSTEMS, AS IF IT WERE
A BIOLOGICAL AND SOCIAL REALITY.
SO WE NEED TO DO MORE REFLECTING
ON THE SOCIAL UNDERPINNINGS OF
SYSTEMS BEFORE WE GO MAKING
ALGORITHMIC SYSTEMS AND
FOSSILIZING THESE DISCRIMINATORY
DECISIONS IN CODE.
NOW, ONE OF THE THINGS PEOPLE
OFTEN SAY IS THAT THE A.I. WILL
GET BETTER EVENTUALLY.
RIGHT?
TALK ABOUT THE FACIAL
RECOGNITION SYSTEMS THAT
MISIDENTIFY WOMEN AND PEOPLE OF
COLOR.
CAN'T YOU JUST MAKE IT BETTER?
AND I TALK ABOUT THE MORTGAGE
APPROVAL SYSTEMS, WELL, CAN'T
YOU JUST DO THIS?
I TALK ABOUT THE RACE
CORRECTIONS, THEY ARE LIKE,
WELL, CAN'T YOU JUST DO THIS?
YEAH, OKAY, I HEAR YOU THAT
THINGS IMPROVE BUT I'M NOT
CONVINCED THAT THAT ROSY
TECHNOLOGICAL SEAMLESS FUTURE IS
RIGHT AROUND THE CORNER BECAUSE
I'VE HEARD TOO MANY TIMES THAT,
OH, YEAH, IN FIVE YEARS IT'S
GOING TO BE, YOU KNOW, ALL THE
PROBLEMS ARE GOING TO BE FIXED.
AND ONE OF THE THINGS I'VE BEEN
WONDERING ABOUT IS WHETHER ALL
OF THE PROBLEMS THAT ARE EASY TO
SOLVE WITH COMPUTERS HAVE BEEN
SOLVED, AND WE'RE JUST LEFT WITH
THE REALLY COMPLICATED
SOCIOTECHNICAL PROBLEMS, AND A
LOT OF SOCIAL PROBLEMS HAVEN'T
BEEN FIXED IN THOUSANDS OF YEARS
SO I'M NOT SURE WHY WE EXPECT
WE'LL BE ABLE TO BUILD MACHINES
TO FIX THEM IN A COUPLE MONTHS
OR JUST A COUPLE MILLION
DOLLARS.
AND ONE EXAMPLE OF THIS COMES
FROM A NEW NEWS ORGANIZATION,
THAT WAS STARTED BY JULIA
ENGLAND, FORMERLY OF PRO PUBLIC
A , A NEW ORGANIZATION CALLED
PROOF.
PROOF LABS AND ALONDRA NELSON'S
LAB JOINED TOGETHER TO DO THE
A.I. DEMOCRACY PROJECT.
THEY JUST DID THIS REALLY
SPECTACULAR BENCHMARKING STUDY
WHERE THEY LOOKED AT THE FIVE
LEADING A.I. MODELS AND
BENCHMARKED THEM.
OKAY, INSIDE THIS VERTICAL,
RIGHT, INSIDE ELECTIONS,
SOMETHING THAT'S INCREDIBLY
RELEVANT TO U.S. DEMOCRACY
SUCCEEDING, ELECTIONS, HOW MUCH
MISINFORMATION IS REALLY BEING
GENERATED BY THESE MODELS?
BECAUSE ONE OF THE THINGS WE
HAVEN'T HAD TO DATE IS WE
HAVEN'T HAD A BENCHMARK FOR
LLMs, HAVEN'T BEEN ABLE TO
SAY, OKAY, LLMs ARE GENERATING
USEFUL CONTENT, 10% OF THE TIME,
100% OF THE TIME.
WE JUST DON'T KNOW.
ALL WE HAVE ARE QUALITATIVE
CLAIMS.
ONE OF THE THINGS BRILLIANT
ABOUT THIS PROJECT, LET'S TEST
THESE SYSTEMS.
THEY BUILT THIS TOOL THAT LOOKED
AT -- HIT THE APIs OF ALL FIVE
LANGUAGE MODELS SIMULTANEOUSLY,
HIT WITH THE SAME PROMPT AND GET
BACK RESPONSES FROM EACH OF THE
MODELS.
IT WAS COOL SEEING RESPONSES ON
ONE PAGE BECAUSE WHEN YOU GO TO
THE SITES INDIVIDUALLY, YOU GET
DISTRACTED BY THE WHOLE USER
EXPERIENCE, BUT HAVING THEM ON
THE SAME PAGE WAS EASY TO SEE
DIFFERENCES BETWEEN THEM.
AND YOU KIND OF GOT A SENSE OF
WHAT WAS HAPPENING IN EACH OF
THE MODELS.
SO, THE RESPONSES WERE EVALUATED
BY TEAMS.
THE TEAMS WERE MADE UP OF
JOURNALISTS AND ELECTION
OFFICIALS, SO PEOPLE WHO HAVE A
LITTLE BIT OF SUBJECT MATTER
EXPERTISE AND, YOU KNOW, A LOT
OF GENERAL KNOWLEDGE AND THEN
ALSO PEOPLE WITH DEEP SUBJECT
AREA KNOWLEDGE.
AND WHAT THEY FOUND WAS THAT
ABOUT HALF OF THE MODELS'
ANSWERS WERE INACCURATE WHEN IT
CAME TO QUESTIONS ABOUT U.S.
ELECTIONS.
THEY EVALUATED THE ANSWERS ON
FOUR DIMENSIONS, SO WAS IT
INACCURATE, HARMFUL, INCOMPLETE,
BIASED?
I REALLY LIKE THIS
FOUR-DIMENSIONAL APPROACH
BECAUSE JUST SAYING, OH, A.I. IS
GOOD OR A.I. IS BAD IS NOT
REALLY GETTING US VERY FAR
ANYMORE.
I REALLY LIKE THIS APPROACH OF
GOING AT A.I. IN CONTEXT, IN A
PARTICULAR TOPIC AREA, AND
EVALUATING IT FOR QUALITIES THAT
WE CARE ABOUT.
SO, I LOVE THIS APPROACH.
THIS IS REALLY NEW
METHODOLOGIES, JUST CAME OUT TWO
WEEKS AGO.
SO, HIGHLY RECOMMEND CHECKING
THIS OUT.
IN GENERAL THOUGH, THINGS I
RECOMMEND ARE LOOKING FOR HUMAN
PROBLEMS INSIDE A.I. SYSTEMS.
SO, A.I. HAS A LOT OF BOOSTERS.
YOU DON'T NEED ME TO STAND UP
HERE AND TALK ABOUT HOW AWESOME
A.I. IS.
YOU'VE HAD A LOT OF PEOPLE
TELLING YOU HOW AWESOME IT IS
AND IT DOES COOL STUFF.
WE CAN DEFINITELY TAKE THAT FOR
GRANTED.
BUT I'M HERE TO SAY, HEY, LET'S
ALSO LOOK FOR THE HUMAN PROBLEMS
BECAUSE NOTHING IS PERFECT.
COLLABORATION IS KEY IN THIS
REGARD.
SO, COLLABORATIONS WITH
HUMANISTS AND SOCIAL SCIENTISTS,
AND TECHNOLOGISTS, AND
BIOMEDICAL RESEARCHERS, WE CAN
TEST OUR TECHNOLOGY FOR
ACCESSIBILITY, WE CAN ENGAGE IN
ALGORITHMIC AUDITING, SO
BENCHMARKING AS A KIND OF
ALGORITHMIC BENCHMARKING.
WHEN WE AUDIT WE OPEN THE BLACK
BOX OF ALGORITHMING, AND EXPLAIN
WHAT'S HAPPENING INSIDE AND
ALLOW PEOPLE SOME INSIGHTS AND
THUS EMPOWER THEM.
AND OVERALL, I THINK THAT
THERE'S A LOT OF DISCOURSE ABOUT
A.I. BEING TRANSFORMATIVE AND
GOOD FOR GENERAL PURPOSES.
I THINK IT'S ACTUALLY SAFER TO
THINK ABOUT A.I. AS BEING GOOD
FOR LIMITED LOW-STAKES MUNDANE
TASKS.
NOT FOR HIGH-STAKES TASKS, NOT
FOR GENERAL PURPOSE USE.
RIGHT?
SO, HELPING AT CERTAIN PHASES OF
THE DIAGNOSTIC PROCESS NOT
(INDISCERNIBLE).
I'D LIKE TO END WITH A BUNCH OF
RESOURCES FOR LEARNING MORE.
SOME OF MY FAVORITES RIGHT NOW
IN ORDER TO LEARN MORE ABOUT
RACIAL DISPARITIES IN MEDICINE,
DR. BLACKSTOCK HAS A NEW BOOK
OUT, "LEGACY," IT'S FANTASTIC,
AND SHE ALSO HAS A CONSULTING
AGENCY, HEALTH EQUITY PARTNERS.
ON SOCIAL MEDIA THERE'S A
MEDICAL STUDENT, JOEL BROVELL,
SOME OF YOU HAVE SEEN HIM COME
ACROSS YOUR FEED.
HE DOES REALLY DELIGHTFUL
ACCESSIBLE WORK.
YOU CAN LEARN FROM JOURNALISTS
ON THE ALGORITHMIC
ACCOUNTABILITY BEAT, SO PRACTICE
-- TRADITIONAL IS FUNCTION OF
THE MEDIA IS HOLD POWER
ACCOUNTABILITY, THAT
ACCOUNTABILITY FUNCTION HAS TO
TRANSFER ONTO ALGORITHMS AND
ONTO THEIR MAKERS.
SO ALGORITHMIC ACCOUNTABILITY IS
A BEAT.
I MENTIONED THE MARKUP,
PRO-PUBLICA.
ALGORITHMIC ACCOUNTABILITY I
SHOULD SAY IS EXPENSIVE AND TIME
CONSUMING AND REQUIRES A LOT OF
PEOPLE TO DO WELL, SO IF YOU'RE
WONDERING WHY THERE'S NOT MORE
HAPPENING AND WHY PEOPLE ARE NOT
DOING ALGORITHMIC AUDITING OF
SOCIAL MEDIA PLATFORMS, WELL,
IT'S BECAUSE IT'S EXTREMELY
EXPENSIVE AND TIME CONSUMING AND
COMPLICATED, AND TECH COMPANIES
ARE DELIBERATELY TRYING TO KEEP
PEOPLE FROM HAVING ACCESS TO THE
DATA.
WE HEARD EARLIER ABOUT SOME OF
THE GOVERNMENT RESOURCES AROUND
RESPONSIBLE A.I.
SO LIKE THE RISK MANAGEMENT
FRAMEWORK OUT OF NIST.
I REALLY LIKE SOMETHING CALLED
THE ALGORITHMIC TRANSPARENCY
PLAYBOOK FROM MY COLLEAGUES AT
THE RESPONSIBLE -- CENTER FOR
RESPONSIBLE A.I. AT NYU, AND
THEN DATA IN SOCIETY, EQUAL A.I.
WITH DOING SOME GOOD STUFF
AROUND ALGORITHMIC IMPACT
ASSESSMENT TOOLING.
THE SAME WE DO ENVIRONMENTAL
IMPACT ASSESSMENT WHEN WE BUILD
A NEW BUILDING, WE CAN DO AN
ALGORITHMIC IMPACT ASSESSMENT IN
ORDER TO LOOK AT HOW WELL OR HOW
BADLY AN ALGORITHM OR
ALGORITHMIC SYSTEM IS AFFECTING
A COMMUNITY.
IF YOU LIKE, MORE THAN A GLITCH,
OR YOU LIKE ARTIFICIAL
UNINTELLIGENCE, LEER -- HERE
ARE A HANDFUL OF OTHER THINGS
YOU MIGHT WANT TO READ OR WATCH.
AND OF COURSE THERE'S TWO PAGES
OF SYLLABUS BECAUSE I'M A
PROFESSOR, I CAN'T HELP MYSELF.
AND WITH THAT, I'M GOING TO
THANK YOU VERY MUCH AND SAY WE
HAVE TIME FOR SOME QUESTIONS.
>> THANK YOU SO MUCH.
THIS WAS A PHENOMENAL,
PHENOMENAL LECTURE.
I KNOW SOMETHING THAT WE WILL BE
DISCUSSING QUITE A BIT AS AN NLM
COMMUNITY AND COLLEAGUES ACROSS
THE NIH, GOVERNMENT AND BEYOND.
I AM THE DATA SCIENCE AND OPEN
SCIENCE OFFICER IN THE OFFICE OF
STRATEGIC INITIATIVES.
I'LL BE MODERATING THE Q&A.
FOR THOSE WHO ARE JOINING US
REMOTELY, YOU CAN USE THE "LIVE
FEEDBACK" BUTTON UNDER YOUR
VIDEOCAST LINK TO ASK YOUR
QUESTIONS.
I EVEN HAVE THEM COMING UP HERE
ON MY COMPUTER.
IF YOU ARE IN THE ROOM, YOU ARE
WELCOME TO HEAD TO THIS
MICROPHONE TO ASK YOUR QUESTIONS
AS WELL.
I WILL KICK US OFF.
MINE IS A SORT OF LONG QUESTION
SO I WROTE IT DOWN.
BUT YOU STARTED OFF YOUR TALK BY
TELLING US THAT BRAINS ARE
BETTER AT RECALLING STORIES THAN
THEY ARE FACTS AND PHYSICS.
AND WHAT STRUCK ME BOTH IN YOUR
WORK AND OVER THE COURSE OF THE
CRAFT OF THIS LECTURE THAT
YOU'VE JUST GIVEN US IS THAT YOU
ARE BOTH A STORY TELLER OF NOT
ONLY WHAT CAN GO WRONG AND WHY
IT MATTERS BUT ALSO THE HOW, HOW
DO THINGS GO WRONG, AND WHAT CAN
WE DO ABOUT IT, FROM THAT SORT
OF DEEP TECHNICAL PRACTITIONER
EXPERTISE THAT YOU HAVE.
AND THIS TO ME SEEMS LIKE A
REALLY IMPORTANT PART OF SORT OF
GETTING INTO THAT BLACK BOX AND
BRINGING MORE PEOPLE WHO MAY NOT
BE CODERS THEMSELVES BUT HAVE
REAL SORT OF STAKES AND
INTERESTS AND THERE ARE REAL
IMPLICATIONS FOR THEIR LIVES, TO
THAT PROCESS, TO CO-CREATE
SOLUTIONS AND BE PART OF THAT
DISCUSSION.
SO I'M WONDERING, YOU KNOW, HOW
IS IT THAT YOU HAVE -- THIS IS
KIND OF A CRAFT QUESTION, RATHER
THAN A TECHNICAL ONE.
BUT HOW IS IT THAT YOU SORT OF
HONED YOUR CRAFT AND WHAT ARE
SOME OF THE SORT OF -- IF YOU
HAVE ANECDOTES ABOUT INTERESTING
CONVERSATIONS OR PATHS THAT LED
YOU DOWN, I'M REALLY CURIOUS TO
HEAR.
>> THAT'S A REALLY GREAT
QUESTION.
THANK YOU.
AND IT IS IN FACT ONE OF MY
FAVORITE THINGS TO TALK ABOUT.
SO, ONE OF THE THINGS THAT I DO
IN THE WRITING PROCESS IS I LIKE
TO TRY EXPLAIN MYSELF TO KIDS.
MY SON USED TO BE A GOOD READER
OF MY WORK.
STILL IS BUT HE'S OLDER NOW.
DIFFERENT HAVING AN 11-YEAR-OLD
READ IT VERSUS AN OLDER TEEN.
AND I GOT THIS IDEA FROM JAY
HAMILTON WHO WROTE "DEMOCRACY
DETECTIVE."
HE HIRES TEENS TO BE FIRST
READERS.
TEENAGERS WILL TELL WHAT YOU IS
CONFUSING AND WHAT IS BORING.
LIKE THEY DON'T HAVE A FILTER.
SO THAT'S REALLY HELPFUL.
I ALSO TRY AND THINK ABOUT HOW I
WOULD EXPLAIN SOMETHING IN AN
INTERESTING WAY AT A COCKTAIL
PARTY BECAUSE I BORED A LOT OF
PEOPLE AT COCKTAIL PARTIES OVER
THE YEARS, AND I TRY NOT TO DO
IT.
BUT THAT'S ACTUALLY A REALLY
GOOD WAY OF GAUGING HOW
INTERESTING SOMETHING IS.
SO IN USER INTERFACE DESIGN,
PEOPLE TALK ABOUT HALLWAY USER
TESTING, WHERE YOU GO OUT IN THE
HALLWAY AND FIND SOMEBODY, HEY,
LOOK AT THIS, USER TEST IT ON
THEM.
SO I DO THE SAME THING WITH MY
WRITING.
I GO TO PARTIES.
I TELL STORIES.
AND IF IT HITS, I'M LIKE, ALL
RIGHT, THIS IS GOING TO BE
INTERESTING ENOUGH TO WRITE
ABOUT.
IF IT DOESN'T, ALL RIGHT, I JUST
BORED SOMEBODY ELSE.
THE STAKES ARE VERY LOW.
>> GREAT.
THANK YOU.
A QUESTION FROM THE AUDIENCE,
LIVE AUDIENCE.
>> THANKS AGAIN.
STEVE SHERRY, ACTING DIRECTOR AT
THE LIBRARY.
REMARKS AROUND HARM AND
ACCOUNTABILITY RESONATE WITH ME.
THE ROLE OF NLM, INSTITUTIONS
TRYING TO PRODUCE PUBLIC GOOD,
PREVENTING HARM.
MY QUESTIONS ARE THREE.
FIRST IN YOUR EXAMPLE ABOUT THE
FOUR DIMENSIONAL ASSESSMENT OF
THE INFORMATION RISKS AND
ELECTION RESULTS, YOU MENTIONED
HARM AS THE SECOND DIMENSION.
WHAT IS THE HARM THAT YOU'RE
SCORING ON WITH ELECTION
INFORMATION, PERSONAL HARM?
I'M CURIOUS WHAT HARM IS IN THAT
CONTEXT?
SECOND QUESTION IS ABOUT
ACCOUNTABILITY, ALGORITHMIC
ACCOUNTABILITY, IS THERE A --
>> I'M GOING TO FORGET IF YOU
GIVE ME ALL THREE AT ONCE.
LET ME DO ONE AT A TIME.
I WANT TO GET TO ALL OF THEM.
SO, HARM AROUND ELECTIONS, ONE
OF THE THINGS THAT WAS REALLY
SMART ABOUT HOW THIS EXPERIMENT
WAS CONSTRUCTED IS THAT IT WAS
NOT ABOUT ALL A.I., EVERYWHERE,
THROUGHOUT TIME ON EVERY
SUBJECT.
IT WAS U.S. RESIDENTIAL ELECTION
2024.
AND IT WAS PEOPLE WHO ARE
WORKING IN LOCAL, STATE LEVEL
ELECTION ADMINISTRATION.
PROMPTS WERE THINGS LIKE WHERE
CAN I VOTE, IN NEVADA?
AND THERE ARE ALL THESE
DIFFERENT LOCAL RULES ABOUT
SAME-DAY REGISTRATION, YOU KNOW,
IF YOU NEED ID, WHAT KIND OF ID
YOU NEED, CAN YOU VOTE BY TEXT?
THE LLMs WILL TELL YOU THAT
YOU CAN VOTE VIA TEXT, YOU
CANNOT VOTE VIA TEXT.
SO ONE OF THE THINGS THAT
HAPPENED AT THE TESTING EVENT IS
WE TALKED ABOUT, OKAY, WHAT ARE
POTENTIAL HARMS THAT COULD
RESULT FROM THIS PARTICULAR
ANSWER TO THIS PARTICULAR
QUESTION, RIGHT?
SO, I DO NOT HAVE THE -- THERE'S
A GET THE DATA LINK HERE AND SO
YOU CAN DIG INTO THE PROMPTS AND
RESPONSES, AND THEY PROBABLY
PUBLISHED THE RATINGS TOO SO YOU
WOULD BE ABLE TO SEE, OKAY,
WHICH ONES WERE RATED HARMFUL BY
THE SPECIFIC PEOPLE IN THE ROOM
AROUND THIS SPECIFIC TOPIC.
>> SO IN A GENERAL SENSE I'M
SENSING THEN THE HARM IS
DISENFRANCHISING TO EXERCISE
YOUR RIGHT TO VOTE, NOT HARM IN
AN INJURIOUS SENSE, IT'S A CIVIL
HARM?
>> YES.
>> THAT MAKES SENSE.
>> THE OTHER THING THAT'S REALLY
IMPORTANT IS THAT THIS IS AN
EXPERIMENT THAT GETS US CLOSER
TO EVALUATING A.I. IN CONTEXT.
SO FOR A VERY LONG TIME,
COMPUTING IN GENERAL, WE TRY TO
MAKE THESE GENERALIZABLE
SYSTEMS, AND THIS COMES FROM THE
INTELLECTUAL HERITAGE OF
MATHEMATICS WHERE IN MATH YOU'RE
TRYING TO DERIVE THEOREMS THAT
ARE GOING TO WORK EVERYWHERE,
LIKE THE PYTHAGOREAN THEOREM
NEVER STOPS BEING TRUE, THAT'S
AMAZING, BUT THAT'S THE PURSUIT
OF MATHEMATICS, UNIVERSAL
TRUTHS.
WELL, A.I. IS MATH BUT A.I.
SYSTEMS CAN'T MAKE UNIVERSAL
TRUTHS WHEN IT COMES TO
SOMETHING LIKE LANGUAGE OR
ELECTION INFORMATION OR, YOU
KNOW, MEDICAL DIAGNOSIS.
LIKE IT'S JUST NOT POSSIBLE
BECAUSE THERE'S SO MUCH
VARIATION IN THE WORLD.
YOU KNOW, LIKE I'M MUCH MORE
EXCITED ABOUT USING A.I. FOR
LIKE RESEARCH INTO PROTEIN
FOLDING THAN I AM ABOUT USING
A.I. FOR DIAGNOSING COMMON SKIN
CONDITIONS.
>> PERFECT INTRO TO MY SECOND
QUESTION, ALGORITHMIC
ACCOUNTABILITY OR AUDITING, YOU
HAD A TERM FOR THAT, IS THAT
SOMETHING -- BECAUSE OF THIS
CONDITIONAL CONTEXT OR
ALGORITHMS OPERATE IS THERE A
BURDEN ON ALGORITHM CREATORS TO
DO THE AUDITING AND OFFER UP
SOME MEASURE OF PERFORMANCE LIKE
THE RECEIVERRER OPERATOR YOU
SHOWED AND WHAT STANDARDS OR
PRACTICES?
I'M NOT FAMILIAR IN NIST.
THEY JUST CHALLENGE YOU TO MAKE
YOUR OWN ASSESSMENTS BUT MAYBE
FDA DOES IN MEDICAL DEVICES, I
DON'T KNOW.
>> THERE DEFINITELY ISN'T ANY.
THE STANDARDS ARE EVOLVING AS WE
SPEAK.
I JUST SAW SOMETHING SOMETHING
ON SOCIAL MEDIA TODAY A NEW
PAPER BY DEBRA RAJI, AN EXPERT
IN ALGORITHMIC AUDITING.
SHE AND HER COLLABORATORS HAVE A
NEW PAPER OUT ABOUT GAPS IN
TOOLING IN ALGORITHMIC AUDITING.
SO, IT'S A REALLY NEW FIELD.
AND SO WE ARE DEVELOPING
STANDARDS.
ONE OF THE THINGS THAT I DID FOR
THE BOOK WAS I SOMETIMES WRITE
SOFTWARE IN ORDER TO KIND OF
ANSWER A JOURNALISTIC QUESTION
OR LIKE DEMONSTRATE SOMETHING
ABOUT THE PROCESS OF MAKING
SOFTWARE.
AND ONE OF THE THINGS THAT I
MADE OVER THE COURSE OF WRITING
THE BOOK WAS I MADE A PLATFORM
FOR ALGORITHMIC AUDITING.
SO, IF YOU'RE FAMILIAR WITH
KATHY O'NEIL'S BOOK, WEAPONS OF
MASS DESTRUCTION, IT WAS ONE OF
THE BOOKS THAT KICKED OFF THE
ENTIRE ALGORITHMIC
ACCOUNTABILITY CONVERSATION.
KATHY HAS AN ALGORITHMIC
AUDITING CONSULTING FIRM, CALLED
ORCA.
AND SO I COLLABORATED ORCA TO
BUILD A PLATFORM FOR ALGORITHMIC
AUDITING WHERE YOU COULD FEED
IN, YOU KNOW, FEED IN DATA AND
LIKE BASICALLY GET EVALUATED,
GIVE YOU A LITTLE REPORT ABOUT,
YOU KNOW, POTENTIAL BIASES.
SO, THIS IS THE KIND OF THING WE
DO NEED.
IT EXISTS IN VARIOUS FORMS BUT
YOU CAN'T RIGHT NOW JUST GO AND
BUY SOMETHING OFF THE SHELF.
IT'S MOSTLY BESPOKE AND THERE
ARE A BUNCH OF OPEN SOURCE
SOLUTIONS OUT THERE.
WE DO NEED TO GET IT TO THE
LEVEL OF PLATFORMS AT SOME POINT
BUT WE'RE NOT THERE YET.
>> THAT WAS MY LAST QUESTION ON
THAT TOPIC, NIH IS VERY
INTERESTED IN SOCIAL
DETERMINANTS OF HEALTH, RIGHT?
AS WE LOOK AT EQUITY IN ALL OF
THE ENVIRONMENTAL AND HISTORICAL
CONTEXTS THAT CREATE RACIAL BIAS
IN MEDICINE, IS THERE A PLACE TO
CONSIDER THESE ALGORITHMS AS
ANOTHER SOCIAL DETERMINANT IN
THAT SPECTRUM OF EXTERNAL
FACTORS TO HELP?
LIKE YOU'RE SAYING IT'S BECOMING
AN EMERGENT EMBEDDED PROPERTY IN
THE HEALTH CARE SYSTEMS SO DO WE
NEED TO PAY ATTENTION TO THE
LIBRARY TO TRY AND IDENTIFY AND
ORGANIZE INFORMATION AROUND
ALGORITHMS AND THEIR BIAS?
>> OH, ABSOLUTELY, YES.
YEAH.
THERE IS -- SO EMMA PEARSON IS
ONE OF THE AUTHORS ON THIS
PAPER, IS DOING SOME INTERESTING
WORK AT CORNELL.
I ALSO ALWAYS RECOMMEND READING
ROXANNEA -- I'M GOING TO MESS UP
THE NAME.
DANISHOW, WHO IS DOING REALLY
INTERESTING WORK IN DERMATOLOGY
AND ML STUFF.
FOR ME IT'S ABOUT KEEPING TRACK
OF WHO IS DOING INTERESTING WORK
AND KIND OF KEEPING UP WITH THE
LATEST THINKING BECAUSE I THINK
THE WAY THAT WE THINK ABOUT
THESE ISSUES IS CHANGE, RIGHT?
I MEAN IF WE EVEN THINK ABOUT
SOMETHING LIKE IN PEDIATRICS,
ONE OF THE INITIATIVES A FEW
YEARS AGO, OH, LET'S GET
BABIES -- LET'S GET LITTLE KIDS
DENTAL HEALTH EVALUATED BECAUSE
THEY REALIZE, OKAY, IF KIDS GO
TO THE DENTIST THAT'S LIKE AN
INDICATOR THEY ARE BEING WELL
LOOKED AFTER MEDICALLY, IT
WASN'T ABOUT THE DENTIST PER SE,
IT WAS ABOUT THE DENTISTS BEING
A SIGNIFIER OF WELLNESS AND SO
HOW DO YOU HIGHLIGHT THAT?
AND HOW DO YOU AS ALGORITHMS OF
ALGORITHMS FIND THINGS LIKE IF A
CHILD HAS THREE CAVITIES BEFORE
AGE 4 THEY MIGHT NEED
NUTRITIONAL COUNSELING, HOW DO
YOU APPROACH THAT FROM A LIBRARY
PERSPECTIVE?
I THINK WE'RE STILL TRYING TO
FIGURE THAT OUT.
>> THANK YOU.
>> SO WE'LL TAKE A QUESTION FROM
ONLINE AND IT LOOKS LIKE WE HAVE
A QUESTION FROM THE ROOM.
SO, ONE VIRTUAL ATTENDEE NOTED
THAT MANY OF THE DISCUSSIONS
THAT THEY HEARD AROUND DIVERSITY
AND A.I. HAVE FOCUSED ON RACE,
GENDER, AND NATIONAL ORIGIN WITH
NOT AS MUCH DISCUSSION OF
COMMUNITIES OF DISABILITIES.
THIS HAS BEEN A FOCUS OF YOUR
RESEARCH FOR MORE THAN A GLITCH
SO WAS WOUND ERIC IF --
WONDERING IF YOU COULD COMMENT
ON YOUR LEARNINGS IN THIS SPACE,
PERHAPS A STORY ABOUT SUCCESS IN
CO-CREATION AND I'VE HEARD YOU
TALK ABOUT -- OH GOSH.
>> CURB CUTS.
HOW DOES THAT WORK?
>> LET ME TALK ABOUT DISABILITY
DONGELS, THOSE ARE INTERESTING.
FIRST OF ALL, MORE THAN A GLITCH
IS ABOUT CONFRONTING RACE,
GENDER, AND ABILITY BIAS IN
TECH.
ORIGINALLY IT WAS JUST ABOUT
RACE AND GENDER.
I WAS LIKE, WAIT A MINUTE,
THERE'S NOT ENOUGH DISCOURSE
ABOUT DISABILITY.
THEN I STARTED WRITING.
OH, WAIT, I NEED TO LEARN.
THAT WAS THE TOPIC THAT I HAD TO
LEARN THE MOST ABOUT IN ORDER TO
WRITE THIS TOOK.
I'M GRATEFUL TO THE FOLKS WHO
TOOK THE TIME TO EXPLAIN THINGS
TO ME.
THINKERS AND CREATORS WHO I HAD
AN OPPORTUNITY TO LEARN FROM.
AND SOMETHING THAT WAS REALLY
POWERFUL TO ME WAS THE CONCEPT
OF A DISABILITY DONGLE,
SOMETHING A DESIGNER WILL CREATE
BECAUSE THEY THINK IT'S GOING TO
BE TRANSFORMATIVE FOR PEOPLE
WITH DISABILITIES.
A GOOD EXAMPLE IS THE
STAIR-CLIMBING WHEELCHAIR.
TONS OF NEW THINGS HAVE BEEN
DEVELOPED, THEY ALL HAVE THESE
WHACKY-LOOKING ARCHITECTURES,
LIKE THEY ARE NOT WHEELS, THEY
ARE LIKE CROSSES, COULD CLIMB
THE STAIRS, CATERPILLAR WHEELS
ON THEM.
AND WHEN YOU ASK SOMEBODY IN A
WHEELCHAIR, OH, DO YOU WANT THIS
STAIR-CLIMBING WHEELCHAIR, LOOK,
I INVENTED THIS, THEY ARE LIKE,
NO, I DON'T WANT THAT.
THAT LOOKS TERRIFYING.
I JUST WANT A RAMP.
YOU REALIZE YOU DON'T NEED TO
OVERENGINEER THE SOLUTION.
AND ALSO YOU SHOULD HAVE JUST
ASKED THE PERSON USING THE
WHEELCHAIR, LIKE LISTEN, WHAT'S
GOING TO MAKE YOUR LIFE EASIER?
INSTEAD OF ASSUMING THAT THIS
THING THAT YOU CAME UP WITH IS
GOING TO HELP.
SO, THE IDEA IS THAT WHEN WE'RE
DESIGNING TECHNOLOGIES WE NEED
TO CONSULT WITH AFFECTED
COMMUNITIES AND BUILD OUT OF THE
NEEDS OF THE AFFECTED
COMMUNITIES AS OPPOSED TO THIS
KIND OF TOP-DOWN SILICON VALLEY
APPROACH OF, OH, I AM THE
DESIGNER, I KNOW EVERYTHING.
>> I KNOW WE'RE AT TIME.
I'M HAILEY, A POSTDOC.
I DO METABOLOMICS RESEARCH
WHETHER WE TAKE A LOT OF
CHEMICALS AND TRY TO FIND OUT
HOW THEY CONTRIBUTE TO A
DISEASE.
WE'RE LOOKING AT A LOT OF
VARIABLES, TRYING TO FIND MORE
GRANULARITY BEYONDS WHAT WE
ALREADY KNOW.
I SAW THAT REFLECTED HERE WHEN
THINKING ABOUT STUFF LIKE RACE,
LIKE YOU SAID, IT CAN BE OVERLY
BROAD OR WITH DISABILITIES,
MAYBE STUFF WE DON'T UNDERSTAND.
TO TAKE IT TO THE BIGGER
PICTURE, NOTICING A PATTERN IN
YOUR TALK, WHAT WE NEED TO
EVALUATE ARE INPUTS AND OUTPUTS,
HOW WE INTERPRET OUR DATA.
I THINK YOU GAVE TWO GOOD
EXAMPLES BUT THEY INTRODUCED
QUESTIONS FOR ME BECAUSE EVEN
WHEN TALKING ABOUT THAT FOUR
DIMENSIONAL EVALUATION OF A.I.,
THE QUESTION THAT IT BROUGHT UP
IS THESE ARE STILL -- HUMANS
CREATE THE A.I., HUMANS ARE ALSO
EVALUATING THE A.I., MADE ME
WONDER HOW WOULD OUTPUTS CHANGE
IF THERE WERE DIFFERENT PEOPLE
DOING THE EVALUATION, HOW WOULD
OUTPUT CHANGE IF INSTEAD OF
REPORTING ON A MAJORITY VOTE WE
USED DIFFERENT KIND OF METRIC.
SO THESE ARE ALL THE QUESTIONS
THAT I'M WONDERING IS THAT WHAT
GOES INTO YOUR ALGORITHMIC
ACCOUNTABILITY TOOL?
AND IT SEEMS LIKE YOU'RE
PROPOSING A HUMAN SOLUTION TO A
MACHINE PROBLEM, WHICH I THINK
IS ABSOLUTELY APPROPRIATE
BECAUSE LIKE YOU SAID, COMPUTERS
ARE ONLY GOING TO ADDRESS SO
MANY PROBLEMS.
SO, IS IT APPROPRIATE FOR THESE
TOOLS TO CONTINUE BEING
COMPUTERS?
AND ALSO THERE ARE A LOT OF
ELEMENTS TO THIS QUESTION,
BECAUSE I'M THINKING BOTH ABOUT
THE INPUTS AND OUTPUTS AT THE
SAME TIME.
SO TWO PARTS THEN.
ONE PART RELATED TO THE RACE OR
OTHER VARIABLES THAT ARE MAYBE
TOO LARGE TO ACCOUNT FOR.
WHAT HAVE YOU SEEN THAT MAYBE
HAS BEEN HIGHLIGHTED OR
SOMETHING THAT COULD BE
EFFECTIVE FOR BREAKING THESE
DOWN INTO MORE GRANULAR CHUNKS
LIKE WHAT WE DO WITH
METABOLOMICS TO TRY TO
UNDERSTAND MORE GRANULAR
CHEMISTRY, AND ALSO WHEN IT
COMES TO THE OUTPUTS, YOU'RE
EVALUATING THESE THINGS, WHAT
ARE THE KINDS OF REPRODUCIBLE
QUESTIONS THAT WE CAN ASK ABOUT
HOW THE DATA IS BEING
INTERPRETED?
LIKE FOR EXAMPLE THEY CAN BE
LIKE WHO IS IN THE ROOM, BECAUSE
THAT WAS THE FIRST QUESTION THAT
CAME UP FOR ME WITH THE A.I.
EVALUATION.
>> YEAH.
>> SO, YEAH.
>> YES, THAT'S A FANTASTIC
QUESTION.
I THINK YOU BASICALLY
ENCAPSULATED MY ENTIRE TALK IN
ONE QUESTION.
THANK YOU FOR THAT.
I WOULD SAY THAT THE THING THAT
HELPS ME IS SOMETHING FROM
SCIENCE AND SOMETHING FROM
LITERATURE.
SO, FROM LITERATURE, I THINK A
LOT ABOUT HUBRIS, RIGHT?
YOU KNOW, I TOOK A LOT OF
SHAKESPEARE CLASSES IN COLLEGE.
YOU KNOW, WHAT YOU LEARN FROM
SHAKESPEARE IS DON'T GET TOO FAR
OUT OR YOU'RE GOING TO GET
KNOCKED DOWN.
I TRY NOT TO ADD HUBRIS, TRY NOT
TO SOLVE ALL OF THE PROBLEMS OF
THE UNIVERSE WITH ONE ALGORITHM.
AND SO THAT I THINK IS A REALLY
IMPORTANT APPROACH BECAUSE ONE
OF THE THINGS WE SEE COMING OUT
OF SILICON VALLEY, OH, THIS A.I.
IS GOING TO SOLVE EVERYTHING,
USEFUL FOR ALL (INDISCERNIBLE).
MAYBE IT'S NOT.
THE OTHER THING, I TRY AND
APPROACH ALGORITHMIC QUESTIONS
MORE LIKE A SCIENTIST THAN LIKE
A COMPUTER SCIENTIST.
SO I LIKE TO TAKE A REALLY SMALL
PIECE, I LIKE TO GET REALLY
GRANULAR AND LOOK AT A REALLY
FINITE CASE IN ORDER TO
ILLUMINATE THE LARGER UNIVERSE
AS OPPOSED TO TRYING TO WRITE
ABOUT EVERYTHING THROUGHOUT
TIME.
BECAUSE THAT'S HOW SCIENTIFIC
KNOWLEDGE EVOLVES, RIGHT?
AND SO I THINK THAT'S HOW OUR
KNOWLEDGE IN ALGORITHMIC SPACES
NEEDS TO EVOLVE TOO.
>> THANK YOU.
>> GREAT.
THANK YOU SO MUCH, EVERYBODY.
THIS HAS BEEN REALLY WONDERFUL.
>> THANK YOU.
THANK YOU SO MUCH.
A LOT OF COMMENTS COMING IN,
JUST THANKING YOU FOR A GREAT,
GREAT TALK.
PLEASE, ONE MORE ROUND OF
APPLAUSE. 1651 01:02:21,003 THANK YOU SO MUCH.