Building, Operationalizing & Monitoring AI Workloads with Trust & Transparency | Coursera Community
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Building, Operationalizing & Monitoring AI Workloads with Trust & Transparency

  • 25 June 2019
  • 4 replies
  • 2123 views
Building, Operationalizing & Monitoring AI Workloads with Trust & Transparency
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  • Community Manager
  • 1058 replies
With the widespread use of AI in different industries and institutions such as healthcare, finance, mobility, social media, and criminal justice comes an increased probability of unintended consequences and potential harms.

For example, in autonomous driving, we should have liability for accidents and torts, knowing who/what determines responsibility for incidents. In healthcare, for cancer screening, we should have liability for malpractice, collection of data and potential bias in treatment. In finance, we should avoid having unintended bias in credit and lending decisions.

Regulation will likely emerge to address societal concerns and harms brought about by AI. The law has evolved to deal with human mistakes; however, AI is expected to make different types of mistakes. We need to be able to account for the possibilities of both human- and AI-induced error, while promoting the potential benefits of AI.

Join IBM Data Scientists/Machine Learning Engineers Dr. Sepideh Seifzadeh and Annamaria Balazs for a free webinar on building, operationalizing, and monitoring AI workloads with trust and transparency.

You'll have a chance to submit questions during the webinar. You can also share your questions by replying to this post.

Can't attend live? That's okay! The webinar will be recorded and all registrants will be sent a link to the recording!

This webinar took place on 10 July 2019.

Webinar Recording



About the Speakers

Sepideh Seifzadeh
Sepi is a Data Scientist/Machine Learning Engineer on the IBM Data Science Elite (DSE) team, based in San Francisco. After completing her PhD at the Center for Pattern Analysis and Machine Intelligence at University of Waterloo, she started her journey as a Big Data & Analytics consultant. Sepi joined IBM as an open source solution engineer, working for IBM Canada for 2 years where she was honored to be awarded “The Best of IBM” 2018. She really enjoys being on the DSE team and applying AI in real world applications to help customers on their data science journey. She enjoys working with DSE team members who are top talent in the field.

Sepi is a speaker at conferences, meetups and summits, and she is passionate about sharing recent trends in technology with everyone. She mostly uses open source tools in Watson Studio, with her recent passion being around detecting model bias using Watson Open Scale to ensure Machine Learning models provide fair decisions with trust and transparency.

In her free time, Sepi goes hiking, scuba diving, and biking. She has a passion for technology and always tries to keep herself up to date about the recent trends and innovations in the field.

Annamaria Balazs
Annamaria is a Data Scientist and AI Evangelist in the IBM Data Science Elite team. She works with clients globally, helping them become successful on their data science journey. She joined IBM over a year ago, and held previous positions as a General Manager and EMEA Regional Manager for large international organizations effectively using data to drive business decisions while managing and growing the business. Being a lifelong learner, her studies are varied and diverse and she holds degrees in Transport Engineering, Psychology and Computer Sciences. In her everyday job, she utilizes her skills in Machine Learning, Python, R, Sparks and Big Data Analytics.

4 replies

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Tagging a bunch of people here who I think might be interested in this webinar! @Kalyan @THANGA MANICKAM M @AlexIca82 @hamster @antoinesavine @AlexDH @Alex Margos @vivekverma1019 @Pancaldi
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My name is Angelina, and I am currently signed up for your Webinar on July 10th. After looking through IBM's website and coursera's classes on IBM AI Watson and enjoying the companies and industries IBM supports has me interested in job opportunities I could find in the future with IBM/Apple. I have used Apple for many years. My background is in finance: Series 6 Mutual Funds, Series 7 Brokerage Securities, Series 31Managed Futures, Series 63 Uniform Securities State Laws and Life and Health and Client Relationship Management. I was hired out of college with Fidelity Investments a long time ago, and I never finished my bachelor's degree. I currently just bought 2019 Series 7 Brokerage Securities to update myself on the industry in addition to participating in future Webinars at Fidelity Investments.

Would you recommend starting with the IBM Data Science Professional Certificate, and then completing the AI Foundations for Everyone? Or just going straight to AI Foundations for Everyone? Do I still need the Bachelor's Degree to work at IBM? or starting somewhere completely different?
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Thank you for the informative presentation!

My name is Xinru Cheng, and I was the one who asked Sepi about the regulatory bodies at the government and corporate level during the Q&A. :)

What I meant to ask was how you think AI regulation would develop - since corporations that implement AI are unlikely to invest in regulating themselves (you mentioned on a slide that "60% of business stakeholders see regulation as a barrier for implementing AI", which makes sense), do we need to rely on governments or international regulatory bodies after something goes really wrong and gets their attention (like with Cambridge Analytica and the following data privacy regulations)?

Or is it faster and more efficient to start from higher education, and hoping most ML practitioners will use AI Fairness 360 or similar toolkits to check their own or each other's work? (IBM's prediction that "only unbiased AI would survive" just seems a bit too optimistic for me. Maybe there's more context to it.)


Another question I had was, what are some of the skills/courses to focus on for someone planning to start as a data scientist and eventually move towards AI ethics and regulation? Would gaining knowledge in law or business be helpful, or is it better to focus on mastering the technical skills in ML first?

Anyway, thanks for reading my long reply. It was a very thought-provoking talk. And I'd be happy to continue the discussion elsewhere also (https://www.linkedin.com/in/xinru-cheng-3171706b/)
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Hi @XinruCheng thank you for your message, I'm glad you liked the presentation.
Regarding your first point, i think we should focus on both and move them forward at the same time. As governments and tech companies,... define their Regulations,... there should be some tools such as Fainess 360 or OpenScale to help detect and mitigate bias and help us with Trust and Transparency in the ML lifecycle. There is a huge adaptation of AI in different industries and institutions and with widespread use of AI comes an increased probability of unintended consequences and potential harms, therefore we need some regulations as well as tools to detect and to address societal concerns and harms brought about by AI. We need to recognize that the choices we make in building new technologies can have dire social ramifications. As such, universities are beginning to require computer science majors to take ethics and morality in computer science courses. Cornell, Harvard, MIT, Stanford, and the University of Texas are among the colleges offering these courses.
Regarding your second question on skills/courses needed to learn datascience please refer to our ongoing webinar:
https://coursera.community/data-science-8/become-a-data-scientist-the-journey-through-data-science-and-ai-4829 if you are interested to learn more about AI fairness 360 http://aif360.mybluemix.net/ please follow the links to learn more and try out the platform: AI Fairness 360 Toolkit Slack https://aif360.slack.com/ AI Fairness 360 Toolkit Public Repo
https://github.com/IBM/AIF360

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