This morgenbooster will share how to find the positive impact of AI and how to integrate it into your own digital process.

1. Shi
f
t
in
g
Focus on AI:
How i
t
Can Make a Posi
t
ive
Di
f
f
erence
- balancing promise and critique

3. In 1508
t
here’s room
f
or di
f
f
eren
t
opinions:
Skep
t
icism, c
a
u
t
ion, en
t
husi
a
sm….

4. Ch
a
n
g
e o
f
a
ppro
a
ch
Siri Beerends
Cultural sociologist, PhD researcher
+ colleges in 1508 and random talks with
people at conferences etc.
Kasper Bjørkskov
Founder, No Objectives
Michael Zwicky Hauschild
Leader, Centre for Absolute Sustainability
Morten Saxnæs
Strategy consultant, independent
Birgitte Kofod Olsen
Legal expert and advisor, Human Rights Advice

5. What is AI?

6. What is AI?
for good?
“Anything that helps people” “Anything that helps us solve
the big problems”
“The term is a contradiction”

7. Aspec
t
s o
f
‘
G
oodness’
Depth of problem solved
Level of responsibility

8. Dep
t
h o
f
prob
l
em so
l
ved
Wi
t
h
t
he AI so
l
u
t
ion
Symptom treatment
An AI that helps young people get a
job
Addressing the Root Causes
An AI that tackles why young people
become unemployed, and works to
eliminate it as a systemic problem
Depth of
problem
solved

9. AI
t
oo
l
s c
a
n produce bi
a
sed resu
l
t
s
t
h
a
t
h
a
rm m
a
r
g
in
a
l
ized communi
t
ies
AI
l
a
cks
a
nd er
a
ses cu
l
t
ur
a
l
a
nd
l
in
g
uis
t
ic diversi
t
y
due
t
o Wes
t
ern-cen
t
ric
d
a
t
a
se
t
s
AI comp
a
nies use
l
ow-p
a
id
l
a
bor in
t
he
G
l
ob
a
l
Sou
t
h
Comp
a
nies
a
re exp
l
oi
t
in
g
we
a
k re
g
u
l
a
t
ion in under-resourced coun
t
ries
t
o
t
es
t
AI
AI h
a
s
a
n enormous environmen
t
a
l
imp
a
c
t
AI depends on unsus
t
a
in
a
b
l
e in
f
r
a
s
t
ruc
t
ure
E
f
f
icienc
y
g
a
ins
f
rom AI o
f
t
en
l
e
a
d
t
o incre
a
sed consump
t
ion r
a
t
her
t
h
a
n
s
a
vin
g
s (The Rebound E
f
f
ec
t
)
AI con
t
ribu
t
es
t
o
g
rowin
g
g
eopo
l
i
t
ic
a
l
compe
t
i
t
ion over resources, power,
a
nd con
t
ro
l
We risk
l
ockin
g
ourse
l
ves in
t
o Bi
g
Tech ecos
y
s
t
ems we c
a
n’
t
con
t
ro
l
AI produc
t
s
g
ive users no re
a
l
choice
t
o op
t
ou
t
or con
t
ro
l
how
t
he AI
beh
a
ves
AI s
y
s
t
ems m
a
ke i
t
h
a
rd
t
o know when we’re bein
g
m
a
nipu
l
a
t
ed
AI
t
echno
l
o
g
ies c
a
n be used
t
o en
a
b
l
e m
a
ss survei
l
l
a
nce wi
t
hou
t
in
f
ormed
consen
t
or oversi
g
h
t
Bec
a
use we perceive AI
a
s ob
j
ec
t
ive, we m
a
y
t
rus
t
i
t
t
oo much — even when
i
t
h
a
l
l
ucin
a
t
es.
Tr
a
inin
g
d
a
t
a
is o
f
t
en scr
a
ped wi
t
hou
t
permission, r
a
isin
g
ques
t
ions
a
bou
t
in
t
e
l
l
ec
t
u
a
l
proper
t
y
AI-
g
ener
a
t
ed con
t
en
t
is
f
l
oodin
g
t
he in
t
erne
t
, m
a
kin
g
i
t
h
a
rder
t
o
f
ind
hum
a
n-m
a
de con
t
en
t
AI reduce
t
he per
f
orm
a
nce o
f
t
op innov
a
t
ors,
f
l
a
t
t
enin
g
r
a
dic
a
l
bre
a
k
t
hrou
g
hs
Overre
l
i
a
nce on AI
t
oo
l
s m
a
y
erode cri
t
ic
a
l
t
hinkin
g
Au
t
om
a
t
in
g
hum
a
n processes c
a
n remove emo
t
ion
a
l
in
t
e
l
l
i
g
ence, c
a
re,
a
nd
j
ud
g
men
t
t
h
a
t
m
a
chines c
a
n’
t
rep
l
ic
a
t
e
AI re
f
l
ec
t
s
a
nd sc
a
l
es
t
he
l
o
g
ic o
f
ex
t
r
a
c
t
ive, pro
f
i
t
-driven s
y
s
t
ems
t
h
a
t
o
f
t
en
priori
t
ize
g
row
t
h over e
t
hics
Hum
a
n experience, me
a
nin
g
,
a
nd e
t
hics c
a
nno
t
be
f
u
l
l
y
mode
l
ed or reduced
t
o d
a
t
a
poin
t
s
Bi
g
Tech comp
a
nies sh
a
pe
t
he domin
a
n
t
n
a
rr
a
t
ive
a
round AI, promo
t
in
g
a
g
row
t
h-
a
nd pro
f
i
t
-driven
a
g
end
a
AI is o
f
t
en
f
r
a
med
a
s
a
‘new
f
ron
t
ier,’ echoin
g
co
l
oni
a
l
l
o
g
ics o
f
domin
a
t
ion
a
nd exp
a
nsion
A
I
Concerns
Level of
responsi-
bility

10. Three
t
y
pes o
f
concerns
Human Environmental Structural
Impacts on individuals and groups Physical impact on the planet and
ecosystems
Reinforces dominant political, economic,
and ideological systems

11. Hum
a
n concerns
Bi
a
sed resu
l
t
s
Too much
t
rus
t
in AI
Loss o
f
con
t
ro
l

12. Environmen
t
a
l
concerns
E
l
ec
t
rici
t
y
us
a
g
e
W
a
t
er
t
o coo
l
M
a
t
eri
a
l
s
a
nd process
t
o bui
l
d h
a
rdw
a
re
“Scien
t
is
t
s h
a
ve es
t
im
a
t
ed
t
h
a
t
t
he power requiremen
t
s o
f
d
a
t
a
cen
t
ers in Nor
t
h Americ
a
incre
a
sed
f
rom 2,688
me
g
a
w
a
t
t
s
a
t
t
he end o
f
2022
t
o 5,341 me
g
a
w
a
t
t
s
a
t
t
he end
o
f
2023, p
a
r
t
l
y
driven b
y
t
he dem
a
nds o
f
g
ener
a
t
ive AI.”
-MIT

13. S
t
ruc
t
ur
a
l
concerns
The Rebound E
f
f
ec
t
G
reen
g
row
t
h

14. Human Environmental Structural
Impacts on individuals and groups Physical impact on the planet and
ecosystems
Reinforces dominant political, economic,
and ideological systems
Three
t
y
pes o
f
concerns

15. Human Environmental
Structural
Triangle of
concerns

16. Re
a
l
wor
l
d
ex
a
mp
l
es

17. Source: Be m
y
E
y
es
AI-powered
visua
l
assis
t
ance
f
or
t
he b
l
ind

18. Prob
l
em so
l
vin
g
imp
a
c
t
Symptom
treatment
Addressing the
Root Causes

19. Hi
g
h environmen
t
a
l
cos
t
due
t
o mu
l
t
imod
a
l
LLM use
Sounds con
f
iden
t
– bu
t
m
a
y
be wron
g
- Use low-energy model options?
- Communicate limits? Human Environmental
Structural
Possib
l
e concerns
The AI cre
a
t
es bi
a
ses resu
l
t
s
- O
ff
er multiple/neutral views?
Risk o
f
m
a
nipu
l
a
t
ion
- Neutral interpretations

20. Reducin
g
CO₂
emissions in
cemen
t
produc
t
ion
wi
t
h AI
Source: C
a
rbon Re

21. Prob
l
em so
l
vin
g
imp
a
c
t
Symptom
treatment
Addressing the
Root Causes

22. Possib
l
e concerns
Human Environmental
Structural
Rebound e
f
f
ec
t
: E
f
f
icienc
y
-> s
a
ve mone
y
->
incre
a
sed produc
t
ion -> No reduc
t
ion in
emissions
- ?
G
reen
g
row
t
h
- Avoid narrative + ?

23. Usin
g
AI
t
o revi
t
a
l
ize
an endan
g
ered
l
an
g
ua
g
e
Source: AI
f
or
G
ood

24. Possib
l
e concerns
Human Environmental
Structural
Hardware from big Tech
- ?

25. Wh
a
t
t
he
y
did ri
g
h
t
Indi
g
enous ownership
a
nd con
t
ro
l
Bui
l
t
a
mode
l
b
a
sed on M
ā
ori
t
r
a
inin
g
m
a
t
eri
a
l
s
Sm
a
l
l
mode
l
, resource-conscious in
f
r
a
s
t
ruc
t
ure
Avoided Bi
g
Tech
l
ock-in (minus chips)
Chose no
t
t
o
f
ocus on
g
row
t
h or pro
f
i
t
, bu
t
ins
t
e
a
d used AI
t
o pro
t
ec
t
t
heir
l
a
n
g
u
a
g
e
a
nd cu
l
t
ure
Source: AI
f
or
G
ood

26. Prob
l
em so
l
vin
g
imp
a
c
t
Symptom
treatment
Addressing the
Root Causes

27. The pro
j
ec
t
is p
a
r
t
o
f
some
t
hin
g
bi
g
g
er

28. Some
g
ood
a
dvice

29. 1.
S
t
a
r
t
wi
t
h
t
he
prob
l
em

30. 2.
Ac
t
responsib
l
y
People:
Make the technology understandable
Give users the control
Avoid manipulation and bias
Planet:
Think small
Choose your partners with care
Keep monitoring
Structural:
…
…
…

31. 3.
I
f
i
t
’s b
a
sic
a
l
l
y
a
bou
t
e
f
f
icienc
y
or
g
row
t
h –
t
hink
t
wice

32. 4.
Invi
t
e o
t
hers in
a
nd
be
t
r
a
nsp
a
ren
t

33. 5.
S
y
mp
t
oms
t
re
a
t
men
t
?
Tr
y
t
o suppor
t
ch
a
n
g
e
e
l
sewhere

34. Morgenbooster in
October:
S
y
s
t
emic
view on AI

35. Case:
Kasper Bjørkskov
No Objectives

42. Th
a
nk
y
ou