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Cool vs Creepy - Ethics and Data Science - Cooper 2Feb
1.
Cool vs. Creepy:
Ethics in Data Science Cathy Cooper 3 February 2017
2.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 2Public (Image of a hand with a credit card + JetBlue plane to Orlando?) xxx Planes, meals, theme parks and … credit cards?
3.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 3Public Connection to ethics & data science DATA SCIENCE Data Analysis Applications EXPERIENCE Transparency Permission
4.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 4Public Data, Analysis and Applications: Biased Data Google tested machine learning analysis on their News data to try to find relationships between concepts. One of the examples was the following “gender + profession” comparison. man + computer programmer vs. woman + _________ __________________ = Homemaker Why? Because the system was trained with an inherently biased source.
5.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 5Public Data, Analysis and Applications: Anonymized Data How many time & location records does it take to link cell phone usage data to an individual? Only four
6.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 6Public Data, Analysis and Applications: Context A financial institution was looking to predict customer churn. Who did they identify as the prime target for their retention offers? Spouses getting their finances in order before filing for divorce. A test was conducted using just social media sentiment analysis to predict changes in US unemployment rate. It did predict an unemployment drop – but it was wrong. Why? Relying only on the tool – and poor timing: Steve Jobs had just died.
7.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 7Public Data, Analysis and Applications: Data/Predictions Change Behavior and Vice Versa GPS Flu projection
8.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 8Public Data, Analysis and Applications: Human Intervention Required “… people have too much trust in data to be intrinsically objective, even though it is in fact only as good as the human processes that collected it.” -- Cathy O’Neill “Humans will need to check on the outcomes and see if the models and the algorithms and the rules are performing as intended, and then intervene if they don’t.” – Tom Davenport
9.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 9Public Transparency: Try it for yourself University of Cambridge The Psychometrics Centre uses your Facebook profile and activity for: ► YouAreWhatYouLike describe openness to new ideas, extraversion and introversion, your warmth or competitiveness, and other personality traits. ► Apply Magic Sauce, predicts your politics, relationship status, sexual orientation, gender, and more. Pennebaker Conglomerate’s ► AnalyzeWords leverages linguistics to discover the personality you (other others) portray on Twitter. You can analyze anyone, not just yourself.
10.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 10Public Transparency: How It Can Work Banks have to prove: ►their algorithms are not based on variables like race & gender ►their models don’t focus on patterns that disfavor specific demographic groups and they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. Consider what it will takes to build transparency into AI system design: human access and action plan
11.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 11Public Permission/Fairness Challenge and opportunity: ► Additional uses for previously collected/aggregated data in new types of analysis, applications and businesses ► Coming legal and regulatory requirements to audit and correct algorithm-based decision making
12.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 12Public In closing… “… algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” – Cathy O’Neill, “Unmasking Unconscious Bias in Algorithms”, The Digitalist, data scientist and author Weapons of Math Destruction “…a fundamental rethinking and a careful approach to software creation itself is needed. Extra care has to be put into training the people who create the systems, and into incorporating research into the machine learning algorithms so you don’t accidentally create more bias. The system has to be re-trained to think differently.” – Yvonne Baur, “Is Machine Learning Sexist?”, Techcrunch, and Head of Predictive Analytics/Machine Learning, SAP SuccessFactors,
13.
© 2016 SAP
SE or an SAP affiliate company. All rights reserved. 13Public Additional Readings and Resources on Ethics & Data Science http://www.digitalistmag.com/digital-supply- networks/2017/01/16/cathy-oneil- unmasking-unconscious-bias-in-algorithms- 04839140 http://www.digitalistmag.com/executive- research/how-ai-can-end-bias http://www.digitalistmag.com/executive- research/an-ai-shares-my-office http://www.digitalistmag.com/digital- economy/digital- futures/2017/01/23/machine-learning-real- business-intelligence-04830225 http://www.digitalistmag.com/executive- research/empathy-the-killer-app-for- artificial-intelligence http://www.digitaltrends.com/cool- tech/women-in-artificial-intelligence/ https://techcrunch.com/2016/10/11/is- machine-learning-sexist/ https://open.sap.com/courses/ds1 http://www.slate.com/articles/technology/fut ure_tense/2014/10/youarewhatyoulike_find_ out_what_algorithms_can_tell_about_you_b ased_on_your.html http://searchcio.techtarget.com/opinion/Dat a-products-introduce-ethical-dilemmas-for- data-scientists http://searchcio.techtarget.com/opinion/Big- data-ethics-Why-the-CIO-needs-to-get- involved http://searchcloudapplications.techtarget.co m/feature/Big-data-collection-efforts-spark- an-information-ethics-debate https://arstechnica.com/science/2015/11/ne w-flu-tracker-uses-google-search-data- better-than-google/ https://gigaom.com/2013/10/09/the-upside- of-prism-at-least-were-talking-about-data- privacy-or-lack-the-thereof/ http://nymag.com/thecut/2016/09/cathy- oneils-weapons-of-math-destruction-math- is-biased.html?mid=twitter-share-thecut https://www.bloomberg.com/news/articles/2 016-06-23/artificial-intelligence-has-a-sea-of- dudes-problem http://boingboing.net/2016/01/06/weapons- of-math-destruction-h.html http://fortune.com/2015/12/04/ethics- training-for-data-scientists/ https://gigaom.com/2013/03/25/why-the- collision-of-big-data-and-privacy-will- require-a-new-realpolitik/ http://www.nature.com/articles/srep01376 http://www.slate.com/articles/technology/fut ure_tense/2016/02/how_to_bring_better_ethi cs_to_data_science.htmlhttp://searchcio.tec htarget.com/opinion/Big-data-bad-analytics http://www.slate.com/articles/technology/fut ure_tense/2016/02/how_to_bring_better_ethi cs_to_data_science.html https://open.sap.com/courses/ml1 https://motherboard.vice.com/en_us/article/ big-data-cambridge-analytica-brexit-trump
14.
Thank you Contact information: Cathy
Cooper Sr. Director, Big Data Analytics cathy.cooper@sap.com 212 653 9555 @catcooper1
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