Corpus ID: 225062154

Artificial Tikkun Olam: AI Can Be Our Best Friend in Building an Open Human-Computer Society

@article{Kasif2020ArtificialTO,
  title={Artificial Tikkun Olam: AI Can Be Our Best Friend in Building an Open Human-Computer Society},
  author={Simon Kasif},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.12015}
}
  • S. Kasif
  • Published 20 October 2020
  • Computer Science
  • ArXiv
Technological advances of virtually every kind pose risks to society including fairness and bias. We review a long-standing wisdom that a widespread practical deployment of any technology may produce adverse side effects misusing the knowhow. This includes AI but AI systems are not solely responsible for societal risks. We describe some of the common and AI specific risks in health industries and other sectors and propose both broad and specific solutions. Each technology requires very… Expand

Figures from this paper

References

SHOWING 1-10 OF 102 REFERENCES
Artificial Intelligence and the Problem of Control
  • Stuart Russell
  • Perspectives on Digital Humanism
  • 2021
A long tradition in philosophy and economics equates intelligence with the ability to act rationally—that is, to choose actions that can be expected to achieve one’s objectives. This framework is soExpand
Algorithmic Fairness
TLDR
An overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms is presented and the most commonly used fairness-related datasets in this field are described. Expand
Preventing undesirable behavior of intelligent machines
TLDR
A general framework for algorithm design is introduced in which the burden of avoiding undesirable behavior is shifted from the user to the designer of the algorithm, and this framework simplifies the problem of specifying and regulating undesirable behavior. Expand
Artificial Intelligence: A Modern Approach
The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.Expand
The Deep Learning Revolution
To a greater or lesser extent, we business, finance and real estate researchers are all empiricists, making a presumption that knowledge is based on experience and that data is essential to prove anyExpand
Machine learning
TLDR
Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Expand
Avoiding Discrimination through Causal Reasoning
TLDR
This work crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion and put forward natural causal non-discrimination criteria and develop algorithms that satisfy them. Expand
Counterfactual Fairness
TLDR
This paper develops a framework for modeling fairness using tools from causal inference and demonstrates the framework on a real-world problem of fair prediction of success in law school. Expand
Evidence for a Collective Intelligence Factor in the Performance of Human Groups
TLDR
A psychometric methodology for quantifying a factor termed “collective intelligence” (c), which reflects how well groups perform on a similarly diverse set of group problem-solving tasks, and finds converging evidence of a general collective intelligence factor that explains a group’s performance on a wide variety of tasks. Expand
Thinking fast and slow.
  • N. McGlynn
  • Medicine, Biology
  • Australian veterinary journal
  • 2014
TLDR
Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of the authors' brain’s wiring. Expand
...
1
2
3
4
5
...