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MindMap Gallery Data privacy, monopoly and fairness in the era of artificial intelligence

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.

Edited at 2023-08-01 16:37:45

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Data privacy, monopoly and fairness in the era of artificial intelligence

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Outline

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

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