Answers.Body: <p>Not all of the mistrust aimed at AI systems is unjustified, particularly when it comes to neural networks and other such systems that rely on large training data sets. There are a number of high profile cases, facial recognition being one that has often (understandably) received a lot of flak, where improperly configured training data has resulted in skewed and questionable results. </p>
  
  <p>If you want to foster more trust in the systems, it will require better tools for analyzing how they are approaching problems and reaching decisions, as well as determining if there are holes in the training data. It will require a community working in the field that gives a lot more thought and care to how they approach their training data and what unintended biases they may be introducing by forgetting something than has often been displayed presently. </p>
  
  <p>Of course, some people are just distrustful of new technology, but I think it's more interesting to address the more legitimate concerns. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-19T20:07:03.390Z
Answers.Text: I think it would be interesting to cite a case where NNs failed catastrophically or were highly "biased".
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Answers.CreationDate: 2019-03-19T20:35:23.837Z
Answers.Text: I really like the fact you mention to use better tools for analyzing on how data analysts and decision makers are approaching problems and reaching decisions. But can you think of may be one tool that can could help in the process? May be in any industry sector? and what do you exactly mean by holes in the training data? is it like a bad(faulty) datasets?
Answers.UserDisplayName: Labelle Doriane
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Answers.CreationDate: 2019-03-19T19:50:11.673Z
Answers.LastActivityDate: 2019-03-19T19:50:11.673Z
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Body: <p>Nowadays, there is too much data for humans to work on alone, and it is very normal for data analysts to use AI techniques to treat and process these data so it can lead to a faster and more accurate result. But many data analysts and decision-makers still don't trust AI methods or techniques and are reluctant to use them. How can we encourage them to accept or prefer these AI solutions? </p>
  
  <p>For example, if AU gives advice to solve a problem, then decision-makers must trust the results and data analysts must trust the mechanism, so that decision-makers can be confident in the AI work and also data analysts can concentrate their activities on added value.</p>
  
  <p>How can we encourage data analysts and decision-makers to adopt AI?</p>
  
Tags: <applications><social>
Title: How can we encourage data analysts and decision-makers to adopt AI?
UserDisplayName: Labelle Doriane
ThreadText: # How can we encourage data analysts and decision-makers to adopt AI?
  
  (Asked by: username_0 on 2019-03-19)
  
  Nowadays, there is too much data for humans to work on alone, and it is very normal for data analysts to use AI techniques to treat and process these data so it can lead to a faster and more accurate result. But many data analysts and decision-makers still don't trust AI methods or techniques and are reluctant to use them. How can we encourage them to accept or prefer these AI solutions?
  
  For example, if AU gives advice to solve a problem, then decision-makers must trust the results and data analysts must trust the mechanism, so that decision-makers can be confident in the AI work and also data analysts can concentrate their activities on added value.
  
  How can we encourage data analysts and decision-makers to adopt AI?
  
  **Tags:** applications, social
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-03-19. Score: 2
  
  Not all of the mistrust aimed at AI systems is unjustified, particularly when it comes to neural networks and other such systems that rely on large training data sets. There are a number of high profile cases, facial recognition being one that has often (understandably) received a lot of flak, where improperly configured training data has resulted in skewed and questionable results.
  
  If you want to foster more trust in the systems, it will require better tools for analyzing how they are approaching problems and reaching decisions, as well as determining if there are holes in the training data. It will require a community working in the field that gives a lot more thought and care to how they approach their training data and what unintended biases they may be introducing by forgetting something than has often been displayed presently.
  
  Of course, some people are just distrustful of new technology, but I think it's more interesting to address the more legitimate concerns.
  
  > **Comments:**
  > **username_2**: I think it would be interesting to cite a case where NNs failed catastrophically or were highly "biased".  
  > **username_0**: I really like the fact you mention to use better tools for analyzing on how data analysts and decision makers are approaching problems and reaching decisions. But can you think of may be one tool that can could help in the process? May be in any industry sector? and what do you exactly mean by holes in the training data? is it like a bad(faulty) datasets?  
  
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