Оглавление
Jörg Dräger. We Humans and the Intelligent Machines
We Humans and the Intelligent Machines
Contents
The algorithmic society – a preface
1Always everywhere
In the service of safety
In the service of justice
In the service of efficiency
Setting the course
2Understanding algorithms
German ignorance, indecision and discomfort
A simple recipe
When algorithms become political
Distorted images of a superintelligence
3People make mistakes
Information overload: Drowning in the flood of data
Flawed reasoning: Making mistakes and discriminating
Inconsistency: Rating the same things differently
Complexity: Overwhelmed by too many options
No glorification
4Algorithms make mistakes
System error: Algorithms fail to do the job they are assigned to
Wrong conclusions: Algorithms misinterpret data
Discriminatory data: Algorithms amplify inequalities
One-sided learning: Algorithms as self-fulfilling prophecies
Normative blindness: Algorithms also pursue wrong objectives
Lack of diversity: Algorithmic monopolies jeopardize participation
No blind trust
What algorithms can do for us
An algorithm for algorithms
Creating order in the jungle of algorithms
5Personalization: Suitable for everyone
Math does not have to horrify
Teach to One
Predatory advertising
The algorithm as magnifying glass
Not defenseless
6Access: Open doors, blocked paths
Loans for the non-creditworthy
Playing computer games for your dream job
Inadvertent discrimination
Black box
The temptation of misuse
7Empowerment: The optimized self
Prostheses for the brain
Algorithmic arms race
The new upgrade culture
The limits of self-optimization
8Leeway: More time for the essential
Leaving annoying routines behind
The efficiency trap
Beyond profit and productivity
Fear of unemployment
Political struggle as corrective
9Control: The regulated society
Searching for origin
Searching for social scammers
A fine line
Controlling the controller
10Distribution: Sufficiently scarce
Fighting fire with data
Enrolled at school by an algorithm
When the tablecloth is too short
11Prevention: A certain future
The old dream of foretelling the future
Algorithmic life savers
On the list of bad guys
The spiral of bad data
Combating symptoms 4.0
Future guilt
12Justice: Fair is not necessarily fair
Fighting prejudice
Positive discrimination
Jail or freedom, black or white
Ethical dilemmas
13Connection: Automated interaction
The opposite of random
Disinformation amplifier
Algorithmic world view
Double-edged swords
What we must do now
Magic and everyday life
Analogue analogies
14Algorithms concern all of us: How we conduct a societal debate
Like a nuclear non-proliferation treaty
A strong lobby for the common good
Transparent consequences and risks
There is no way around participation
15Well meant is not well done: How we control algorithms
Control is a must
Making the black box transparent
Keeping an eye on the professionals
Upholding rights
16Fighting the monopolies: How we ensure algorithmic diversity
Algorithmic monopolies
More free data
More players and goals
More diversity in the tech sector
17Knowledge works wonders: How we build algorithmic competency
Incompetence at all levels
As important as reading or writing
Professional ethics for programmers
Responsibility as a trademark
Exiting the engine room
Machines serving people – an outlook
Orwellian nightmare
AI superpowers
The European way: Values and competition
Humans and machines: A shared destiny
Acknowledgments
Endnotes. 1Always everywhere
2Understanding algorithms
3People make mistakes
4Algorithms make mistakes
5Personalization: Suitable for everyone
6Access: Open doors, blocked paths
7Empowerment: The optimized self
8Leeway: More time for the essential
9Control: The regulated society
10Distribution: Sufficiently scarce
11Prevention: A certain future
12Justice: Fair is not necessarily fair
13Connection: Automated interaction
14Algorithms concern us all: How we conduct a societal debate
15Well meant is not well done: How we control algorithms
16Fighting the monopolies: How we ensure algorithmic diversity
17Knowledge works wonders: How we build algorithmic competency
Machines serving people – an outlook
Bibliography
The Authors
