.

1. Essential Epistemic Opacity
and AI-Driven Data Research
Conceptualizing
the Epistemology
of Data Science

2. General Outline
Posit the core
questions
Examine the
standard definition
Establish key terms and
concepts
01
Indentify the most
pressing issues
02
Apply the
definition
Analyze the causes of
EEO and the main
response strategies
03

3. —Paul Humphreys, 2009,
The Philosophical Novelty of
Computer Simulation
Methods
“A process is essentially epistemically opaque to
agent X iff it is impossible, given the nature of X,
for X to know all the epistemically relevant
elements of the process.”

4. Standard Definition
“A process is essentially
epistemically opaque to agent
X iff it is impossible, given the
nature of X, for X to know all
the epistemically relevant
elements of the process.”
KEY TERMS AND CONCEPTS
⬢ An epistemic (knowledge-
generating) procedure.
⬢ Human agents with their
(naturally) limited capabilities.
⬢ Circumstances and factors
that influence the generation
of knowledge.

5. Standard Definition
“A process is essentially
epistemically opaque to agent
X iff it is impossible, given the
nature of X, for X to know all
the epistemically relevant
elements of the process.”
SCENARIO #1
Steve is using his sight to check
whether it is currently raining
outside.
⬢ A simple procedure with a few
steps.
⬢ Steve, a human being.
⬢ Factors that influence Steve's
ability to accurately perceive
phenomena.

6. Standard Definition
“A process is essentially
epistemically opaque to agent
X iff it is impossible, given the
nature of X, for X to know all
the epistemically relevant
elements of the process.”
SCENARIO #2
Steve is utilizing computer
simulation methods in order to
generate scientific representations.
⬢ A complex procedure
consisting of “millions of
computational steps”.
⬢ Steve, a human being.
⬢ Correctness of every single
computational step.

7. Computational methods are EEO
“No human can examine and justify every element of the
computational processes that produce the ultimate output.”
01
Anthropocentric Predicament
“An exclusively anthropocentric
epistemology is no longer
appropriate.”
02
HUMPHERYS' CLAIMS

8. “AI-Driven Data Research is EEO”
Applying AI technologies to data research
produces highly complex and intricate epistemic
procedures that are (clearly!) EEO.

9. Software conditionality:
Unintended
patterns or
behaviors emerge
from simple
interactions.
“Historical” inheritance of hard-
to-understand features:
PATH
COMPLEXITY
COMPUTATIONAL
EMERGENCE
“FUZZY”
MODULARITY
CAUSES OF EEO
It is difficult to
define clean
interfaces
between the
components.
GENERATIVE
ENTRENCHMENT
Entrenched components are often
regarded as legacy artifacts.
Each conditional
exponentially augments
the lines of code that are
to be tested.

10. —John Symons & Ramón Alvarado, 2016,
Can we trust Big Data? Applying philosophy
of science to software
“(Epistemic opacity) is induced by the sheer
volume of people, number of processes, (...) the
number of lines of code involved in projects of
such magnitude.”

11. DENY
Responding to EEO claims
ACCEPT
Deny the possibility of
knowledge
AI-driven data reseach
can't generate knowledge.
EEO
CLAIM
Adopt some of the less-
conventional epistemic
approaches
Focus on groups, tools, machines,
communities, etc.
Dissolve the opacity
“Mr. Humphreys does not understand
empirical software design.”
- Julian Newman, 2016
01
02
03
Issue
Issue
Issue
No procedure can
generate knowledge!
Underdeveloped
and nonintuitive.
Their applicability is
context-dependent.
Error mitigation techniques can
dissolve the EEO.

12. CORE
QUESTIONS
Can “empirical
software design”
dissolve the opacity?
02
What is the most
appropriate unit of
epistemic analysis?
01
⬢ How do we
distribute knowledge
when it comes to
data science?
⬢ What are the
appropriate
standards of
epistemic relevancy?
⬢ Can error mitigation
strategies eliminate
the causes of opacity?
⬢ How do data scientists
deal with ever-
increasing levels of
complexity?

13. Sources:
Symons, John & Alvarado, Ramón (2016), “Can we trust Big Data? Applying
philosophy of science to software “, Big Data & Society July–December 2016: 1–17.
Humphreys, Paul (2009), “The philosophical novelty of computer simulation
methods”, Synthese 169: 615–626.
Newman, John (2016), “Epistemic Opacity, Confirmation Holism and Technical
Debt: Computer Simulation in the Light of Empirical Software Engineering”, In
Gadducci, F & Tavosanis, M (eds) History and Philosophy of Computing – Third
International Conference, HaPoC 2015.
THANK YOU FOR YOUR
ATTENTION!
milunovicbojan707@gmail.com
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