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Human(e) AI: CORE (PROFESSIONAL VALUES (equal treatment (structural…
Human(e) AI: CORE
BIAS
data
structural inequality becomes clear in AI data
OR reinforce and strengthen biases
"datafication"
machine learning algorithms
inadmissible evidence research
REDUCE human bias
PROFESSIONAL VALUES
efficiency
transparancy
right to appeal
equal treatment
structural inequality becomes clear in AI data
OR reinforce and strengthen biases
uniformity? lack of bias - impartiality?
AI
machine-learning
most dominant
dependent on data to function
knowledge representation + rules-based AI
involves modeling some real-world process/activity
expert systems
DM PROCESS
risk-assesment
effective
efficient
uniformity
GOAL
support
supplement
replace
ETHICS
who is making the expert systems?
who is the DM?
what is the impact of the decision?
can we go against AI DM?
JUSTICE
equitable justice
v. incomprehensibility
might affect the public accountibility of prof jud DM + legitimacy/fairness issues
codified justice
AI + CHANGE
changes values
changes rules
HUMAN FACTOR
human algorithm/codified justice