PEPTalk, University of Amsterdam

1. CONNECTING THE ETHICS
AND EPISTEMOLOGY OF AI
FEDERICA RUSSO, ERIC SCHLIESSER, JEAN WAGEMANS
UNIVERSITY OF AMSTERDAM
@FEDERICARUSSO | @NESCIO13 | @JEANWAGEMANS

2. OUTLINE
● From Ethics aut Epistemology to Ethics cum Epistemology
○ Disconnected projects, ethics as a post-hoc assessment
○ Shifting focus from output to process
○ Ethics as continuous assessment, from design to use
● What can XAI learn from argumentation theory?
● A crash course on arguments from expert’s opinion
● 4 simplified scenarios
● A normative stance for real scenarios
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3. FROM ETHICS AUT EPISTEMOLOGY
TO ETHICS CUM EPISTEMOLOGY
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4. DISCONNECTED PROJECTS,
ETHICS AS POST HOC ASSESSMENT
• Disconnected projects:
• [Ethics] Questions of how to make AI ethically compliant, ensuring that algorithms are
as fair as possible and as unbiased as possible.
• [Epistemology] Questions of transparency / opacity of AI, i.e. , AI as a glass or opaque
box.
• AI raises important ethical concerns, therefore we need to produce suitable
mechanisms
• To audit ethics compliance
• To verify responsibility and accountability
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Post-hoc assessment

5. ‘STAND ALONE’ EPISTEMOLOGY
• A vast and rich debate on transparency
• What is it?
• Can we trust outcomes of opaque AI?
• But the whole debate is orthogonal to ethics concerns
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6. SHIFTING FOCUS: FOR OUTCOME TO PROCESS
• Typical question: Can we trust output X of AI system Y?
• Our proposal: look at the process, before the outcome
• The whole process: design, implementation, use
• At each and every point of the process we can (should) make considerations
about
• Epistemology > transparency, explainability, validation/verification, …
• Ethics > which values are operationalized? How?
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Builds on ‘Computational
Reliabilism’ and on Creel’s
3 types of transparency
Unlike Kearns & Roth, it is
not a trade-off, but a
design choice, proper

7. ETHICS AS CONTINUOUS ASSESSMENT
• Ethical considerations have to be raised
• Already at the design stage
• Throughout the whole process
• And in combination with epistemological / technical considerations
• Epistemology-cum-Ethics: the way forward for XAI
• We care about the role of designers, programmers, engineers
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Complements
‘ethics auditing’,
e.g. Mokander &
Floridi
On how to train
ethical designers
and engineers, see
Bezuidenhout&Ratti

8. XAI AND
ARGUMENTS FROM EXPERT OPINION
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9. LEARNING FROM ARGUMENTATION THEORY
ARGUMENT FROM EXPERT OPINION
p is true, because p is said by expert E
POSSIBLE CRITICAL QUESTIONS
• Is E really an expert about p?
• Is p true or not?
• Do other experts agree?
• Are there other interests at play?
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• Check which form of
institutionalization guarantees
trusting the source of expertise
• Check contents p said by E
• Confront p with other expert opinions
• Check other institutional guarantees

10. DIMENSIONS OF INQUIRY
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Epistemological
Queries
Normative
Queries
Expert
Non-expert

11. SIMPLIFIED SCENARIO 1: EPISTEMIC SYMMETRY OF
EXPERTS
• Expert A: “How did you get to result
X?”
• Expert B: “Because the system is
designed such-and-such”
• Expert A: “Is your AI system fair and
transparent?”
• B: “Yes, I operationalized concepts
XYZ in such-and-such way”
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A question about
epistemology of AI
Expert B gives technical
details about AI system
A question about ethics
of AI
Expert B gives technical
details about how AI
system is ethical
In case of epistemic symmetry between experts, both
epistemological and ethical questions can be answered
with technical details of AI

12. SIMPLIFIED SCENARIO 2: EPISTEMIC ASYMMETRY
• Non-expert: “I am diagnosed with
disease X, why?”
• Expert: “Because AI said you are in
reference class XYZ”
• Non-expert: “Is your AI fair and
unbiased?”
• B: “Yes, I operationalized XYZ in
such-and-such way”
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A question about
epistemology AI
Expert’s technical answer
is meaningless to non-
expert
As non-expert, if you
can’t grasp
epistemology, you
inquiry about axiology
Expert’s technical answer
is meaningless to non-
expert
In case of epistemic Asymmetry between experts, both
epistemological and ethical questions cannot be
answered with technical details of AI

13. A SIMPLIFIED SCENARIO 3: EPISTEMIC ASYMMETRY
• Non-expert: “I am diagnosed with
disease X, why?”
• Expert: “Because the system said you
are in reference class XYZ”
• Non-expert: “Is your AI system fair and
transparent?”
• Expert: “Yes, our research and
algorithms comply with standards and
codes of conduct XYZ”
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A question about
epistemology AI
Expert’s technical answer
is meaningless to non-
expert
As non-expert, if you
can’t grasp
epistemology, you
inquiry about axiology
Expert’s answer appeals
to axiology +
institutionalization
In case of epistemic Asymmetry, both epistemological and
ethical questions are answered appealing to axiology and
institutionalization: the non-experts trusts that the process
complies with institutionalized standards

14. A SIMPLIFIED SCENARIO: EPISTEMIC SYMMETRY OF
NON-EXPERTS
• Non-expert A: “My request for a loan
was rejected, why?”
• Non-expert B: “Because AI said you
don’t comply with XYZ”
• Non-expert A: “Is your AI system fair
and unbiased?”
• Non-expert B: “Yes, our bank is part
of the EU Federation of Ethical
Banks”
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A question about
epistemology of AI
Non-expert cannot give
details about process,
only output
A question about ethics
of AI
Non experts answers
epistemological and
ethical questions with
instititutionalization
In case of epistemic symmetry between non-experts
epistemological and ethical questions are answered
appealing to axiology and institutionalization: the non-
experts trusts that the process complies with
institutionalized standards

15. FROM SIMPLIFIED SCENARIOS TO REAL SCENARIOS
• Requests of ethical compliance have to be anticipated with clear and accessible
coding documentation
• Making nested algorithms more transparent is not a compromise on e.g. on
efficiency but a positive stance about e.g. faireness and transparency
• Kearns & Roth: a trade-off
• Russo-Schliesser-Wagemans: value-promoting
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16. Thanks for your attention
FEDERICA RUSSO, ERIC SCHLIESSER, JEAN WAGEMANS
UNIVERSITY OF AMSTERDAM
@FEDERICARUSSO | @NESCIO13 | @JEANWAGEMANS

17. CONNECTING THE ETHICS
AND EPISTEMOLOGY OF AI
FEDERICA RUSSO, ERIC SCHLIESSER, JEAN WAGEMANS
UNIVERSITY OF AMSTERDAM
@FEDERICARUSSO | @NESCIO13 | @JEANWAGEMANS
Thanks for your attention

18. EPISTEMIC SYMMETRY
EPISTEMOLOGICAL QUERIES
• Expert A: Can I trust output of
algorithm G?
• Expert B: Yes. Look at technical
features XYZ.
NORMATIVE QUERIES
• Expert A: Is the algorithm G fair?
• Expert B: Yes. Look at technical
features XYZ.
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This follows from
epistemology: can trust
outcome if can trust process
This follows from
epistemology-cum-ethics:
can trust G is fair because of
features of process

19. EPISTEMIC ASYMMETRY
EPISTEMOLOGICAL QUERIES
• Non-expert: Can I trust output of
algorithm G?
• Expert: Yes. You can trust my
expertise in designing and
implementing technical features XYZ.
NORMATIVE QUERIES
• Non-expert: Is algorithm G fair?
• Expert: Yes. You can trust I comply
with ethics requirements, as
mandated by institution Y.
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Axiological component
to address normative
question
Axiology +
institutionalization to
address normative
question

20. TO SUM UP AND CONCLUDE
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21. EPISTEMOLOGICAL AND NORMATIVE
• It is high time that epistemological and normative questions are considered
together, rather than separately
• To develop an ethics-cum-epistemology, we shift focus from the outcome to the
whole process
• At each stage of the whole process, normative and epistemic questions have to
be considered
• Ethics is continuous assessment, rather than post-hoc
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Epistemological
Queries
Normative
Queries

22. ARGUMENTS FROM EXPERT OPINION AND AI
• With an ethics-cum-epistemology, and with the aid of argumentation theory, we
account for situations of epistemic symmetry and asymmetry
• In epistemic symmetry, both epistemological and normative questions can be
answered at technical level
• In epistemic asymmetry, axiology and institutionalization help address both
epistemological and normative questions
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Expert
Non-expert