arbiter
JournalsPricingAccount

Development Practices of Trusted AI Systems among Canadian Data Scientists

Jinnie Shin et al.

International Review of Information Ethics2020https://doi.org/10.29173/irie377article
ABDC C
Weight
0.34

What the paper says

The introduction of Artificial Intelligence (AI) systems has demonstrated impeccable potential and benefits to enhance the decision-making processes in our society. However, despite the successful performance of AI systems to date, skepticism and concern remain regarding whether AI systems could form a trusting relationship with human users. Developing trusted AI systems requires careful consideration and evaluation of its reproducibility, interpretability, and fairness, which in in turn, poses increased expectations and responsibilities for data scientists. Therefore, the current study focused on understanding Canadian data scientists’ self-confidence in creating trusted AI systems, while relying on their current AI system development practices.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.29173/irie377

Or copy a formatted citation

@article{jinnie2020,
  title        = {{Development Practices of Trusted AI Systems among Canadian Data Scientists}},
  author       = {Jinnie Shin et al.},
  journal      = {International Review of Information Ethics},
  year         = {2020},
  doi          = {https://doi.org/https://doi.org/10.29173/irie377},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Development Practices of Trusted AI Systems among Canadian Data Scientists

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.34

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.