�ݺ�ߣshows by User: eestola

�ݺ�ߣshows by User: eestola / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: eestola / Fri, 31 Mar 2017 18:54:50 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: eestola When recommendation systems go bad - machine eatable /slideshow/when-recommendation-systems-go-bad-machine-eatable/74094888 whenrecommendationsystemsgobad-machineeatable-170331185451
Latest version of my talk on ethics in Machine Learning and Recommendation Systems, given at Microsoft's Machine Eatable series at Civic Hall on 3/31/17]]>
Latest version of my talk on ethics in Machine Learning and Recommendation Systems, given at Microsoft's Machine Eatable series at Civic Hall on 3/31/17]]> Fri, 31 Mar 2017 18:54:50 GMT /slideshow/when-recommendation-systems-go-bad-machine-eatable/74094888 eestola@slideshare.net(eestola) When recommendation systems go bad - machine eatable eestola Latest version of my talk on ethics in Machine Learning and Recommendation Systems, given at Microsoft's Machine Eatable series at Civic Hall on 3/31/17 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whenrecommendationsystemsgobad-machineeatable-170331185451-thumbnail.jpg?width=120&height=120&fit=bounds" /> Latest version of my talk on ethics in Machine Learning and Recommendation Systems, given at Microsoft's Machine Eatable series at Civic Hall on 3/31/17

When recommendation systems go bad - machine eatable from Evan Estola

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0 9 17-16 - when recommendation systems go bad - rec sys /slideshow/9-1716-when-recommendation-systems-go-bad-rec-sys/66124146 9-17-16-whenrecommendationsystemsgobad-recsys-160917162032
Evan Estola - Meetup RecSys 2016 Industry Track]]>
Evan Estola - Meetup RecSys 2016 Industry Track]]> Sat, 17 Sep 2016 16:20:32 GMT /slideshow/9-1716-when-recommendation-systems-go-bad-rec-sys/66124146 eestola@slideshare.net(eestola) 9 17-16 - when recommendation systems go bad - rec sys eestola Evan Estola - Meetup RecSys 2016 Industry Track <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/9-17-16-whenrecommendationsystemsgobad-recsys-160917162032-thumbnail.jpg?width=120&height=120&fit=bounds" /> Evan Estola - Meetup RecSys 2016 Industry Track

9 17-16 - when recommendation systems go bad - rec sys from Evan Estola

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0 Estola 5 20-16 ml_conf - when recommendation systems go bad /slideshow/estola-5-2016-mlconf-when-recommendation-systems-go-bad/62282026 estola-52016mlconf-whenrecommendationsystemsgobad-160522231031
My talk from MLConf Seattle 2016 When Recommendations Systems Go Bad: Machine learning and recommendations systems have changed the way we interact with not just the internet, but some of the basic products and services that we use to run our lives. While the reach and impact of big data and algorithms will continue to grow, how do we ensure that people are treated justly? Certainly there are already algorithms in use that determine if someone will receive a job interview or be accepted into a school. Misuse of data in many of these cases could have serious public relations, legal, and ethical consequences. As the people that build these systems, we have a social responsibility to consider their effect on humanity, and we should do whatever we can to prevent these models from perpetuating some of the prejudice and bias that exist in our society today. In this talk I intend to cover some examples of recommendation systems that have gone wrong across various industries, as well as why they went wrong and what can be done about it. The first step towards solving this larger issue is raising awareness, but there are concrete technical approaches that can be employed as well. Three that will be covered are: Accepting simplicity with interpretable models. Data segregation via ensemble modelling. Designing test data sets for capturing unintended bias.]]>
My talk from MLConf Seattle 2016 When Recommendations Systems Go Bad: Machine learning and recommendations systems have changed the way we interact with not just the internet, but some of the basic products and services that we use to run our lives. While the reach and impact of big data and algorithms will continue to grow, how do we ensure that people are treated justly? Certainly there are already algorithms in use that determine if someone will receive a job interview or be accepted into a school. Misuse of data in many of these cases could have serious public relations, legal, and ethical consequences. As the people that build these systems, we have a social responsibility to consider their effect on humanity, and we should do whatever we can to prevent these models from perpetuating some of the prejudice and bias that exist in our society today. In this talk I intend to cover some examples of recommendation systems that have gone wrong across various industries, as well as why they went wrong and what can be done about it. The first step towards solving this larger issue is raising awareness, but there are concrete technical approaches that can be employed as well. Three that will be covered are: Accepting simplicity with interpretable models. Data segregation via ensemble modelling. Designing test data sets for capturing unintended bias.]]> Sun, 22 May 2016 23:10:31 GMT /slideshow/estola-5-2016-mlconf-when-recommendation-systems-go-bad/62282026 eestola@slideshare.net(eestola) Estola 5 20-16 ml_conf - when recommendation systems go bad eestola My talk from MLConf Seattle 2016 When Recommendations Systems Go Bad: Machine learning and recommendations systems have changed the way we interact with not just the internet, but some of the basic products and services that we use to run our lives. While the reach and impact of big data and algorithms will continue to grow, how do we ensure that people are treated justly? Certainly there are already algorithms in use that determine if someone will receive a job interview or be accepted into a school. Misuse of data in many of these cases could have serious public relations, legal, and ethical consequences. As the people that build these systems, we have a social responsibility to consider their effect on humanity, and we should do whatever we can to prevent these models from perpetuating some of the prejudice and bias that exist in our society today. In this talk I intend to cover some examples of recommendation systems that have gone wrong across various industries, as well as why they went wrong and what can be done about it. The first step towards solving this larger issue is raising awareness, but there are concrete technical approaches that can be employed as well. Three that will be covered are: Accepting simplicity with interpretable models. Data segregation via ensemble modelling. Designing test data sets for capturing unintended bias. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/estola-52016mlconf-whenrecommendationsystemsgobad-160522231031-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk from MLConf Seattle 2016 When Recommendations Systems Go Bad: Machine learning and recommendations systems have changed the way we interact with not just the internet, but some of the basic products and services that we use to run our lives. While the reach and impact of big data and algorithms will continue to grow, how do we ensure that people are treated justly? Certainly there are already algorithms in use that determine if someone will receive a job interview or be accepted into a school. Misuse of data in many of these cases could have serious public relations, legal, and ethical consequences. As the people that build these systems, we have a social responsibility to consider their effect on humanity, and we should do whatever we can to prevent these models from perpetuating some of the prejudice and bias that exist in our society today. In this talk I intend to cover some examples of recommendation systems that have gone wrong across various industries, as well as why they went wrong and what can be done about it. The first step towards solving this larger issue is raising awareness, but there are concrete technical approaches that can be employed as well. Three that will be covered are: Accepting simplicity with interpretable models. Data segregation via ensemble modelling. Designing test data sets for capturing unintended bias.

Estola 5 20-16 ml_conf - when recommendation systems go bad from Evan Estola

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0 When recommendation systems go bad /slideshow/when-recommendation-systems-go-bad/56058697 whenrecommendationsystemsgobad-151211155037
A presentation on ethics in Machine Learning and Recommendation Systems given at the NYC Data Science Meetup at MeetupHQ on 12/10 http://www.meetup.com/NYC-Data-Science/events/226998694/]]>
A presentation on ethics in Machine Learning and Recommendation Systems given at the NYC Data Science Meetup at MeetupHQ on 12/10 http://www.meetup.com/NYC-Data-Science/events/226998694/]]> Fri, 11 Dec 2015 15:50:37 GMT /slideshow/when-recommendation-systems-go-bad/56058697 eestola@slideshare.net(eestola) When recommendation systems go bad eestola A presentation on ethics in Machine Learning and Recommendation Systems given at the NYC Data Science Meetup at MeetupHQ on 12/10 http://www.meetup.com/NYC-Data-Science/events/226998694/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whenrecommendationsystemsgobad-151211155037-thumbnail.jpg?width=120&height=120&fit=bounds" /> A presentation on ethics in Machine Learning and Recommendation Systems given at the NYC Data Science Meetup at MeetupHQ on 12/10 http://www.meetup.com/NYC-Data-Science/events/226998694/

When recommendation systems go bad from Evan Estola

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0 Machine learning and data at Meetup /slideshow/ml-data-at-meetup/25683442 mldataatmeetup-130828094304-phpapp02
Presentation given for Tech Talks at Meetup event on 8/27/13]]>
Presentation given for Tech Talks at Meetup event on 8/27/13]]> Wed, 28 Aug 2013 09:43:04 GMT /slideshow/ml-data-at-meetup/25683442 eestola@slideshare.net(eestola) Machine learning and data at Meetup eestola Presentation given for Tech Talks at Meetup event on 8/27/13 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mldataatmeetup-130828094304-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation given for Tech Talks at Meetup event on 8/27/13

Machine learning and data at Meetup from Evan Estola

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0 https://cdn.slidesharecdn.com/profile-photo-eestola-48x48.jpg?cb=1558719190 https://cdn.slidesharecdn.com/ss_thumbnails/whenrecommendationsystemsgobad-machineeatable-170331185451-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/when-recommendation-systems-go-bad-machine-eatable/74094888 When recommendation sy... https://cdn.slidesharecdn.com/ss_thumbnails/9-17-16-whenrecommendationsystemsgobad-recsys-160917162032-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/9-1716-when-recommendation-systems-go-bad-rec-sys/66124146 9 17-16 - when recomme... https://cdn.slidesharecdn.com/ss_thumbnails/estola-52016mlconf-whenrecommendationsystemsgobad-160522231031-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/estola-5-2016-mlconf-when-recommendation-systems-go-bad/62282026 Estola 5 20-16 ml_co...