Trust Building in AI systems: A critical thinking perspective

How do I know when to trust AI,and when not to?

Who goes to jail if a self driving car kills someone tomorrow?

Do you know scientists say people will believe anything,repeated enough

Designing AI systems is also an exercise in critical thinking because an AI is only as good as its creator.This talk is for discussions like these,and more.

With the exponential increase in computing power available, several AI algorithms that were mere papers written decades ago have become implementable. For a data scientist, it is very tempting to use the most sophisticated algorithm available. But given that its applicability has moved beyond academia and out into the business world, are numbers alone sufficient? Putting context to AI, or XAI (explainable AI) takes the black box out of AI to enhance human-computer interaction. This talk shall revolve around the interpret-ability-complexity trade-off, challenges, drivers and caveats of the XAI paradigm, and an intuitive demo of translating inner workings of an ML algorithm into human understandable formats to achieve more business buy-ins.

Prepare to be amused and enthralled at the same time.

 
 

Outline/Structure of the Talk

  • Introduction ( 2 mins)
  • Need for XAI ( 2 mins)
  • Introduction to the explainable AI paradigm ( 2mins)
  • How cognitive distortions manifest in AI ( 2 mins)
  • Attempts at building explain-ability in AI systems (Code level,audit level and industry attempts) ( 10 mins)
  • Future scope of work ( 2 mins)

Learning Outcome

Any scientifically designed system is only as good or as bad as its creator.

At the end of this session,users walk away with an understanding of appreciation of how human thought process influences an AI design process.They shall also be able to critically evaluate an existing AI implementation and weigh on its pros and cons without getting into the inner workings of the actual algorithm.

Building upon my previous talks at ODSC Delhi's first ever meetup (June 2019) and Google Devfest New Delhi 2019,This part explores attempts at reconciling cognition and meta-cognition.

Finally,the next time someone tries to tell them 9 out of 10 dentists recommend brand X,they know what they're hearing is only a part of the bigger picture.

Target Audience

This is for people who have already started using AI,or are skeptical about it,Any existing practitioner can walk away with new insights,and newbies can expect to find a new avenue of thinking.

Prerequisites for Attendees

Working knowledge of R/Python/Basic ML is good to have,but an inquisitive mindset beats everything.

schedule Submitted 9 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Santonu Goswami
    By Santonu Goswami  ~  3 months ago
    reply Reply

    Hi Kriti, 

    Thank you for the submission. 

    After going through your proposal, outline of talks etc I am trying to get a few things clarified to help me review the proposal:

    • Your title of the proposal clearly states that it is a perspective and the first few sentences of the proposal states that the talk will focus on general philosophical discussions on AI. 
    • Then in prerequisites you put working knowledge of R/Python/Basic ML but on a loose terms. 
    • Your stucture of the talk includes "Attempts at building explainability in AI systems (hands on demo)-10mins" , which is also quite unclear. 

    I request you to throw light on how these would be tied into a concise 20 mins talk. 

    This will help review the proposal. 

    Thanks, 

    Santonu

     

    • Kriti Doneria
      By Kriti Doneria  ~  2 months ago
      reply Reply

      Hi Santonu,
      Thanks for the detailed feedback.
      Below are the my responses, pointwise.

      1. The discussion does include a brief about the guiding principles of XAI to lay context,convoluted with the applied parts of imbibing explain-ability into AI systems on different levels.
      2. The prerequisites are loose in nature in a sense that someone who can read code just fine can make use of the session.
      3. Attempts include code level attempts ( Python libraries),methodologies( for instance,sensitivity analysis) and full fledged model auditing for black boxes.
        A 20 min talk can throw brief light on all of the above without diving deep into it. The objective is to introduce the paradigm to help practitioners into their existing workstreams.
  • Natasha Rodrigues
    By Natasha Rodrigues  ~  3 months ago
    reply Reply

    Hi Kriti,

    Thanks for your proposal! Requesting you to update the Outline/Structure section of your proposal with a time-wise breakup of how you plan to use 20 mins for the topics you've highlighted?

    To help the program committee understand your proposal a little better, can you add the slides for your proposal.

    Thanks,

    Natasha

    • Kriti Doneria
      By Kriti Doneria  ~  2 months ago
      reply Reply

      Hi Natasha,

      Thanks for the feedback. I've updated the time-wise breakup.
      The slides I've used for a previous presentation are in the slides section. I'll add some more content to it basis the research I've done after that conference.

       

      Thanks and regards,

      Kriti

  • Ashay Tamhane
    By Ashay Tamhane  ~  3 months ago
    reply Reply

    Hi Kriti, thanks for the submission. Could you elaborate on "Attempts at building explainability in AI systems (hands on demo) ( 10 mins)"? Is this going to a demo on some public data? Some details will help.

    Also, if you have implemented XAI in an industry setting, covering that use case in the talk would be very useful.

    • Kriti Doneria
      By Kriti Doneria  ~  2 months ago
      reply Reply

      Hi Akshay,

      Yes this demo would be on a public available dataset and will include industry attempts from secondary sources.
      Thanks


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