MindMap Gallery Data privacy, monopoly and fairness in the era of artificial intelligence
Research on fairness in machine learning. With the advent of the artificial intelligence era, the value contained in big data has been continuously developed. Let’s take a look at data privacy, monopoly and fair knowledge in the artificial intelligence era.
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This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
Data privacy, monopoly and fairness in the era of artificial intelligence
Summary
background
The arrival of the artificial intelligence era
The value contained in big data is continuously developed
The problem becomes more prominent
privacy leakage issue
Data monopoly problem
Fairness Issues in Algorithmic Decision-Making
Discussing privacy, monopoly and fairness
From the perspective of data development
generate environment
unique
Artificial Intelligence Era
Analyze one by one
Problem status
problem challenge
get conclusion
opacity
data collection
Data usage
decision making
Solutions
Establish a data transparency mechanism
introduction
people
Big data decision-making
create deep dependence
own data
lose control
Ethical issues in data ecology
Misuse of user data
privacy leakage issue
Data monopoly problem
decision-making fairness issues
problem caused
causing users to suffer
privacy threats
unfair treatment
The crisis of trust between users and enterprises
Making it difficult to implement technology
Autopilot
Medical health forecast
limited
Development of artificial intelligence technology
Mining the value of data is not inconsistent with respecting human ethics
affect each other
Mutual restraint
eventually achieve dynamic equilibrium
There are essentially two types of problems
Data ethics issues
Ethical issues arising during data collection and use
main performance
Private issues
monopoly problem
Algorithmic ethical issues
Ethical issues arising from algorithmic decision-making processes
main performance
fairness issue
Explore the nature of current ethical issues
From the perspective of data development
Explore the data environment in which the problem arises
Analyze uniqueness
Analyze the connections between different issues
Discuss in further detail
analyze
its current situation
its challenge
Raise the essence of the problem
opacity
data collection
Data usage
decision making
Solutions
Establish a data transparency mechanism
Great privacy concept
not only
through technology
disturbance
anonymous
difference
achieve protection
ensure
In data collection usage scenarios
Correct application of data
Algorithms make correct decisions
Covers a wider range of content
The front is longer
Looking at ethical issues from the perspective of data development
Data privacy issues
Data monopoly problem
decision-making fairness issues
Related events
Automated recruitment system
There is sexism
Pattern recognition software
Combine keyboard, mouse, etc. with men
Integrate kitchen shopping and more with women
Big data familiarity
price discrimination
Algorithmic decision-making
provided
More efficient results
Not necessarily correct results
exist
unfair
Not trustworthy
theoretical analysis
Machine learning model
Correctness is extremely dependent on
training data
training data
Generated by human annotation
People are biased
unconscious
inject bias into
training and test data
conscious
inject bias into
training process
Train based on these data
get
Unreasonable decision-making model
bias
Passed from person to data
Then pass the data to the model
Not only will it not be contained
will also be amplified
Clarify the meaning of fairness
is a multidimensional concept
reflect
People's pursuit of equality
sociology
Equal pay for equal work
psychology
with others
social comparison
with myself
historical comparison
philosophy
veil of ignorance
Everyone is unclear about the role they will play in society
Only rules made jointly can be fair
Ideal Machine Learning Fairness
reflect objective reality
Should be able to correct subjective biases caused by people
Meet two requirements
similar data sets
Can be obtained through training
similar model
Information about similar individuals
can be obtained
similar output
try solution
Thinking from a data perspective
Reasonable auditing of decision-making data
make decision-making process
Transparency
understandability
bias in data
be found
be held accountable
avoid introducing
Consideration from an algorithmic perspective
Current work has limitations
Introducing fairness metrics
Improve the model itself
for a specific algorithm
A broader understanding of decision-making
Automated decision-making
human decision-making
Consider both decisions together
Get more comprehensive decision-making results
Improve decision-making fairness
Solution: Establish a data transparency mechanism
The basis of traditional decision-making
Acquisition of "data-information-knowledge"
Existing big data decision-making
Directly driven by data
Big data transparency
On data privacy issues
On the issue of data monopoly
On the issue of fairness in decision-making
the outcome of the decision
through data transparency
Conduct an audit
Discover
unfair result
discriminate
bias
Based on the audit results
Algorithm engineers further improved
decision algorithm
decision input data
improve
Fairness in Data Decisions
There are many problems with big data transparency
Conclusion
Machine learning and other algorithms
produced
huge social value
think
Realize data value
Address ethical issues simultaneously
Great privacy concept
Face data in its life cycle
Reasonable collection
Reasonable storage
fair use
The emergence of ethical issues
result of interaction
Data ecological environment
machine learning technology
data driven
Its ownership cannot be defined simply by
Solve ethical issues
Establish a data transparency mechanism
Key to Solving Ethical Issues