How to Regulate AI? Start With the Data. Aaronson, S. A. Barron's, June, 2023. ![How to Regulate AI? Start With the Data. [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex The article discusses the importance of data in the development and regulation of AI. Aaronson argues that while AI developers rely on large data sets to train their systems, U.S. policymakers do not view data governance as a significant means to regulate AI. The author suggests that data governance is an effective way to regulate AI, as demonstrated by the European Union and more than 30 other countries that provide their citizens with a right not to be subject to automated decision-making without explicit consent. The article also highlights the risks of relying on scraped data sets, which can contain incomplete or inaccurate data, leading to problems of bias, propaganda, and misinformation. Aaronson concludes by suggesting several steps for Congress to take in governing AI, including passing a national personal data protection law, requiring the Securities and Exchange Commission to develop rule-making related to the data underpinning AI, and re-examining the legality of web scraping. She focues on the SEC, because these firms control ever more of the world’s data, and they should not be opaque about how they collect, utilize, and value data. In addition, the SEC requires firms to report on hacks of their data, hence they are already using corporate governance to regulate some aspects of data. In the context of trustworthy AI, this article emphasizes the importance of data governance and the need for regulations that ensure the responsible collection and use of data. For policymakers, it suggests the need for regulations that protect personal data, ensure the responsible use of data in AI, and re-examine the legality of web scraping, which could enhance the trustworthiness and acceptability of AI systems in society. (How policy should be developed, how to govern AI)
@article{aaronson_how_2023,
chapter = {Daily},
title = {How to {Regulate} {AI}? {Start} {With} the {Data}.},
shorttitle = {How to {Regulate} {AI}?},
url = {https://www.barrons.com/articles/ai-data-regulation-bfded1d4},
abstract = {The article discusses the importance of data in the development and regulation of AI. Aaronson argues that while AI developers rely on large data sets to train their systems, U.S. policymakers do not view data governance as a significant means to regulate AI. The author suggests that data governance is an effective way to regulate AI, as demonstrated by the European Union and more than 30 other countries that provide their citizens with a right not to be subject to automated decision-making without explicit consent. The article also highlights the risks of relying on scraped data sets, which can contain incomplete or inaccurate data, leading to problems of bias, propaganda, and misinformation. Aaronson concludes by suggesting several steps for Congress to take in governing AI, including passing a national personal data protection law, requiring the Securities and Exchange Commission to develop rule-making related to the data underpinning AI, and re-examining the legality of web scraping. She focues on the SEC, because these firms control ever more of the world’s data, and they should not be opaque about how they collect, utilize, and value data. In addition, the SEC requires firms to report on hacks of their data, hence they are already using corporate governance to regulate some aspects of data.
In the context of trustworthy AI, this article emphasizes the importance of data governance and the need for regulations that ensure the responsible collection and use of data. For policymakers, it suggests the need for regulations that protect personal data, ensure the responsible use of data in AI, and re-examine the legality of web scraping, which could enhance the trustworthiness and acceptability of AI systems in society. (How policy should be developed, how to govern AI)},
language = {en-US},
urldate = {2024-01-26},
journal = {Barron's},
author = {Aaronson, Susan Ariel},
month = jun,
year = {2023},
keywords = {20, Data and AI Governance, gw\_abstracts},
}
Downloads: 0
{"_id":"EYsiB6xTXGsHNWTwr","bibbaseid":"aaronson-howtoregulateaistartwiththedata-2023","author_short":["Aaronson, S. A."],"bibdata":{"bibtype":"article","type":"article","chapter":"Daily","title":"How to Regulate AI? Start With the Data.","shorttitle":"How to Regulate AI?","url":"https://www.barrons.com/articles/ai-data-regulation-bfded1d4","abstract":"The article discusses the importance of data in the development and regulation of AI. Aaronson argues that while AI developers rely on large data sets to train their systems, U.S. policymakers do not view data governance as a significant means to regulate AI. The author suggests that data governance is an effective way to regulate AI, as demonstrated by the European Union and more than 30 other countries that provide their citizens with a right not to be subject to automated decision-making without explicit consent. The article also highlights the risks of relying on scraped data sets, which can contain incomplete or inaccurate data, leading to problems of bias, propaganda, and misinformation. Aaronson concludes by suggesting several steps for Congress to take in governing AI, including passing a national personal data protection law, requiring the Securities and Exchange Commission to develop rule-making related to the data underpinning AI, and re-examining the legality of web scraping. She focues on the SEC, because these firms control ever more of the world’s data, and they should not be opaque about how they collect, utilize, and value data. In addition, the SEC requires firms to report on hacks of their data, hence they are already using corporate governance to regulate some aspects of data. In the context of trustworthy AI, this article emphasizes the importance of data governance and the need for regulations that ensure the responsible collection and use of data. For policymakers, it suggests the need for regulations that protect personal data, ensure the responsible use of data in AI, and re-examine the legality of web scraping, which could enhance the trustworthiness and acceptability of AI systems in society. (How policy should be developed, how to govern AI)","language":"en-US","urldate":"2024-01-26","journal":"Barron's","author":[{"propositions":[],"lastnames":["Aaronson"],"firstnames":["Susan","Ariel"],"suffixes":[]}],"month":"June","year":"2023","keywords":"20, Data and AI Governance, gw_abstracts","bibtex":"@article{aaronson_how_2023,\n\tchapter = {Daily},\n\ttitle = {How to {Regulate} {AI}? {Start} {With} the {Data}.},\n\tshorttitle = {How to {Regulate} {AI}?},\n\turl = {https://www.barrons.com/articles/ai-data-regulation-bfded1d4},\n\tabstract = {The article discusses the importance of data in the development and regulation of AI. Aaronson argues that while AI developers rely on large data sets to train their systems, U.S. policymakers do not view data governance as a significant means to regulate AI. The author suggests that data governance is an effective way to regulate AI, as demonstrated by the European Union and more than 30 other countries that provide their citizens with a right not to be subject to automated decision-making without explicit consent. The article also highlights the risks of relying on scraped data sets, which can contain incomplete or inaccurate data, leading to problems of bias, propaganda, and misinformation. Aaronson concludes by suggesting several steps for Congress to take in governing AI, including passing a national personal data protection law, requiring the Securities and Exchange Commission to develop rule-making related to the data underpinning AI, and re-examining the legality of web scraping. She focues on the SEC, because these firms control ever more of the world’s data, and they should not be opaque about how they collect, utilize, and value data. In addition, the SEC requires firms to report on hacks of their data, hence they are already using corporate governance to regulate some aspects of data.\nIn the context of trustworthy AI, this article emphasizes the importance of data governance and the need for regulations that ensure the responsible collection and use of data. For policymakers, it suggests the need for regulations that protect personal data, ensure the responsible use of data in AI, and re-examine the legality of web scraping, which could enhance the trustworthiness and acceptability of AI systems in society. (How policy should be developed, how to govern AI)},\n\tlanguage = {en-US},\n\turldate = {2024-01-26},\n\tjournal = {Barron's},\n\tauthor = {Aaronson, Susan Ariel},\n\tmonth = jun,\n\tyear = {2023},\n\tkeywords = {20, Data and AI Governance, gw\\_abstracts},\n}\n\n\n\n","author_short":["Aaronson, S. A."],"key":"aaronson_how_2023","id":"aaronson_how_2023","bibbaseid":"aaronson-howtoregulateaistartwiththedata-2023","role":"author","urls":{"Paper":"https://www.barrons.com/articles/ai-data-regulation-bfded1d4"},"keyword":["20","Data and AI Governance","gw_abstracts"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero-group/dcambrid/5266609","dataSources":["e4qi3jRmPhPzc7C9a"],"keywords":["20","data and ai governance","gw_abstracts"],"search_terms":["regulate","start","data","aaronson"],"title":"How to Regulate AI? Start With the Data.","year":2023}