Ethics and data science:
As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. W...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Sebastopol, CA
O'Reilly Media
[2018]
|
| Edition: | First edition. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781492043898/?ar |
| Summary: | As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today. |
| Item Description: | Online resource; title from title page (Safari, viewed August 17, 2018) |
| Physical Description: | 1 Online-Ressource (1 volume) |
Staff View
MARC
| LEADER | 00000cam a22000002c 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-047629665 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228120540.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2018 xx |||||o 00| ||eng c | ||
| 035 | |a (DE-627-1)047629665 | ||
| 035 | |a (DE-599)KEP047629665 | ||
| 035 | |a (ORHE)9781492043898 | ||
| 035 | |a (DE-627-1)047629665 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 100 | 1 | |a Loukides, Michael Kosta |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Ethics and data science |c Mike Loukides, Hilary Mason, and DJ Patil |
| 250 | |a First edition. | ||
| 264 | 1 | |a Sebastopol, CA |b O'Reilly Media |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 Online-Ressource (1 volume) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Online resource; title from title page (Safari, viewed August 17, 2018) | ||
| 520 | |a As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today. | ||
| 650 | 0 | |a Data mining |x Moral and ethical aspects | |
| 650 | 0 | |a Big data | |
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Quantitative research | |
| 650 | 4 | |a Exploration de données (Informatique) ; Aspect moral | |
| 650 | 4 | |a Données volumineuses | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Recherche quantitative | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Quantitative research | |
| 700 | 1 | |a Mason, Hilary |e VerfasserIn |4 aut | |
| 700 | 1 | |a Patil, DJ |e VerfasserIn |4 aut | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781492043898/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Record in the Search Index
| DE-BY-TUM_katkey | ZDB-30-ORH-047629665 |
|---|---|
| _version_ | 1829007779813130240 |
| adam_text | |
| any_adam_object | |
| author | Loukides, Michael Kosta Mason, Hilary Patil, DJ |
| author_facet | Loukides, Michael Kosta Mason, Hilary Patil, DJ |
| author_role | aut aut aut |
| author_sort | Loukides, Michael Kosta |
| author_variant | m k l mk mkl h m hm d p dp |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)047629665 (DE-599)KEP047629665 (ORHE)9781492043898 |
| edition | First edition. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02642cam a22004932c 4500</leader><controlfield tag="001">ZDB-30-ORH-047629665</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228120540.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047629665</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP047629665</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781492043898</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047629665</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Loukides, Michael Kosta</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Ethics and data science</subfield><subfield code="c">Mike Loukides, Hilary Mason, and DJ Patil</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Sebastopol, CA</subfield><subfield code="b">O'Reilly Media</subfield><subfield code="c">[2018]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1 volume)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Online resource; title from title page (Safari, viewed August 17, 2018)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Data mining</subfield><subfield code="x">Moral and ethical aspects</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Big data</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Quantitative research</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exploration de données (Informatique) ; Aspect moral</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Données volumineuses</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recherche quantitative</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Big data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quantitative research</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mason, Hilary</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Patil, DJ</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781492043898/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-047629665 |
| illustrated | Not Illustrated |
| indexdate | 2025-04-10T09:35:44Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (1 volume) |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | O'Reilly Media |
| record_format | marc |
| spelling | Loukides, Michael Kosta VerfasserIn aut Ethics and data science Mike Loukides, Hilary Mason, and DJ Patil First edition. Sebastopol, CA O'Reilly Media [2018] ©2018 1 Online-Ressource (1 volume) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; title from title page (Safari, viewed August 17, 2018) As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today. Data mining Moral and ethical aspects Big data Machine learning Quantitative research Exploration de données (Informatique) ; Aspect moral Données volumineuses Apprentissage automatique Recherche quantitative Mason, Hilary VerfasserIn aut Patil, DJ VerfasserIn aut |
| spellingShingle | Loukides, Michael Kosta Mason, Hilary Patil, DJ Ethics and data science Data mining Moral and ethical aspects Big data Machine learning Quantitative research Exploration de données (Informatique) ; Aspect moral Données volumineuses Apprentissage automatique Recherche quantitative |
| title | Ethics and data science |
| title_auth | Ethics and data science |
| title_exact_search | Ethics and data science |
| title_full | Ethics and data science Mike Loukides, Hilary Mason, and DJ Patil |
| title_fullStr | Ethics and data science Mike Loukides, Hilary Mason, and DJ Patil |
| title_full_unstemmed | Ethics and data science Mike Loukides, Hilary Mason, and DJ Patil |
| title_short | Ethics and data science |
| title_sort | ethics and data science |
| topic | Data mining Moral and ethical aspects Big data Machine learning Quantitative research Exploration de données (Informatique) ; Aspect moral Données volumineuses Apprentissage automatique Recherche quantitative |
| topic_facet | Data mining Moral and ethical aspects Big data Machine learning Quantitative research Exploration de données (Informatique) ; Aspect moral Données volumineuses Apprentissage automatique Recherche quantitative |
| work_keys_str_mv | AT loukidesmichaelkosta ethicsanddatascience AT masonhilary ethicsanddatascience AT patildj ethicsanddatascience |