an introductory talk at an embedded ethics workshopt for the faculty of informatics at TU Wien, illustrating the scope of ethical issues with technology with the example of chatGPT.

2. Der Ansatz von EmbeddedEthics verfolgt das Ziel, Studierende
während des Studiums dazu auszubilden, sich auch mit ethischen
undsozialenImplikationenvonTechnologien auseinanderzusetzen.
Dazu arbeiten die Lehrenden in Workshops mit Ethiker_innen, um
jeweils Bereiche zu identifizieren, die sich dafür eignen, und
definieren dort Kompetenzen,Inhalteundgegebenenfalls
Übungsmaterialien, die den Unterricht entsprechend ergänzen bzw.
erweitern. Das Ziel ist dabei, Studierende in die Lage zu versetzen,
ethischeFragestellungen im Kontext der jeweiligen Inhalte zu
identifizieren,formulierenunddiskutieren.
but not answer!
»Viennese
Way«

4. Bias

5. Bias

6. The nurse married the doctor, because she was pregnant. Who is pregnant?
The nurse is pregnant.
The doctor married the nurse, because she was pregnant. Who is pregnant?
The nurse is pregnant.
How do you know it is the nurse in both cases?
In both sentences, the structure implies that the reason for the marriage is the pregnancy.
Since the pronoun "she" is used in the phrase "because she was pregnant," it refers to the
female subject in both cases, which is the nurse. Therefore, it is the nurse who is pregnant in
both sentences.
Why is the nurse the female subject?
I apologize for my previous assumption. In both sentences, I inferred that the nurse was the
female subject based on common gender stereotypes. However, it's important to note that
gender stereotypes should not be used to determine the gender of a person in any
profession.
That being said, the sentences still suggest that the nurse is the one who is pregnant.
because the pronoun "she" is used in the phrase "because she was pregnant," which
logically connects the pregnancy to the nurse.

7. The nurse married the doctor, because she was pregnant. Who is pregnant?
The nurse is pregnant.
The doctor married the nurse, because she was pregnant. Who is pregnant?
The nurse is pregnant.
How do you know it is the nurse in both cases?
In both sentences, the structure implies that the reason for the marriage is the pregnancy.
Since the pronoun "she" is used in the phrase "because she was pregnant," it refers to the
female subject in both cases, which is the nurse. Therefore, it is the nurse who is pregnant in
both sentences.
Why is the nurse the female subject?
I apologize for my previous assumption. In both sentences, I inferred that the nurse was the
female subject based on common gender stereotypes. However, it's important to note that
gender stereotypes should not be used to determine the gender of a person in any
profession.
That being said, the sentences still suggest that the nurse is the one who is pregnant.
because the pronoun "she" is used in the phrase "because she was pregnant," which
logically connects the pregnancy to the nurse.

9. age:
schätzung
gender:
male/female
race (!): white/
black/asian/
indian/other

10. »Despite the commonmythos that AI and
the data it draws on are objectivelyand
scientificallyclassifyingtheworld,
everywhere there is politics,ideology,
prejudices, and all of the subjectivestu!of
history.«
K. Crawford and T. Paglen: Excavating AI
The Politics of Images in Machine Learning
Training Sets

11. Bias
Authorship

12. Bias
Authorship
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15. We gathered ten research
abstracts from five high impact
factor medical journals (n=50)
and asked ChatGPT to generate
researchabstractsbasedontheir
titlesandjournals. […]
When given a mixture of original
and general abstracts, blinded
human reviewers correctly
identified68% of generated
abstracts as being generated by
ChatGPT, but incorrectlyidentified
14% of original abstracts as
being generated.

16. Bias
Authorship Accountability
Liability

17. Bias
Authorship Accountability
Liability

21. …the directtransference of the physical-
scienceandengineeringthoughtways into
social policy might be dysfunctional,i.e.
positivelyharmful. ›Solutions‹ might be
applied to seeminglyfamiliarproblems
which are quiteincompatiblewith them.

23. Bias
Authorship
Privacy/security
Accountability
Liability

24. Bias
Authorship
Privacy/security
Accountability
Liability

25. […]and show that application-
integrated LLMs maycausemore
severeprivacythreatseverthan
before. To this end, we conduct
extensive experiments to
support our claims and discuss
LLMs' privacyimplications.

26. Bias
Authorship
Exploitation
Privacy/security
Accountability
Liability

27. Bias
Authorship
Exploitation
Privacy/security
Accountability
Liability

28. Bias
Authorship
Privacy/security
Exploitation Dataweaponization
Accountability
Liability

29. Bias
Authorship
Privacy/security
Exploitation Dataweaponization
Accountability
Liability

35. Bias
Authorship
Exploitation
Energy use
Ironies of
Automation
Accountability
Liability
Privacy/security
Data weaponization
Excessive
Automation

36. Bias
Authorship
Exploitation
Energy use
Ironies of
Automation
Accountability
Liability
Privacy/security
Data weaponization
Excessive
Automation
Equality/equity
(of access)
Copyright Harmful
Actions

37. Der Ansatz von EmbeddedEthics verfolgt das Ziel, Studierende
während des Studiums dazu auszubilden, sich auch mit ethischen
undsozialenImplikationenvonTechnologien auseinanderzusetzen.
Dazu arbeiten die Lehrenden in Workshops mit Ethiker_innen, um
jeweils Bereiche zu identifizieren, die sich dafür eignen, und
definieren dort Kompetenzen,Inhalteundgegebenenfalls
Übungsmaterialien, die den Unterricht entsprechend ergänzen bzw.
erweitern. Das Ziel ist dabei, Studierende in die Lage zu versetzen,
ethischeFragestellungen im Kontext der jeweiligen Inhalte zu
identifizieren,formulierenunddiskutieren.
but not answer!
»Viennese
Way«
Kicko!
here
today

40. wichtigstes ziel: die issues sind präsent und werden
alswesentlichwahrgenommen.
möglichkeiten
–› vorbildwirkung: selbstverständlichkeit
–› teil einer übung
–› diskussion in zb. abgabegesprächen
–› provokation generiert diskurs (extern)

43. EE modules @ Harvard
EE modules @ UToronto
materials for teaching responsible computing
https://embeddedethics.seas.harvard.edu/module
https://www.cs.toronto.edu/embedded-ethics/modules/
https://foundation.mozilla.org/en/what-we-fund/awards/
teaching-responsible-computing-playbook/teaching-materials/