Papers
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. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal Of Economic Perspectives, 29(3), 3-30. https://doi.org/10.1257/jep.29.3.3
. (2017). Where is technology taking the economy. Mckinsey & Company, (McKinsey Analytics). Retrieved de mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy
. (2017). What to expect from artificial intelligence. Mit Sloan Management Review , 58. Retrieved de http://ilp.mit.edu/media/news_articles/smr/2017/58311.pdf
. (2018). Transparency and fairness in machine learning applications. Texas A&M Journal Of Property Law, 4. Retrieved de https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?article=1079&context=journal-of-property-law
. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358 - 367. https://doi.org/10.1016/j.giq.2018.10.001
. (2019). Towards an AI economy that works for all. In Keystone Research Center Future of Work Project sponsored by The Heinz Endowments. Retrieved de https://www.keystoneresearch.org/sites/default/files/FOW_TowardAIEconomyForAllFinalEdit.pdf
. (2018). Towards a Sociological Conception of Artificial Intelligence. In Artificial General Intelligence (AGI). Presented at the Artificial General Intelligence (AGI). https://doi.org/10.1007/978-3-319-97676-1_13
. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings Of The National Academy Of Sciences, 116, 6531–6539. https://doi.org/10.1073/pnas.1900949116
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The Tqm Journal, ahead-of-p. https://doi.org/10.1108/TQM-12-2019-0303
. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The Tqm Journal, ahead-of-p. https://doi.org/10.1108/TQM-12-2019-0303
. (2018). Technology, productivity and employment: an empirical analysis of indian industries. Journal Of Business Thought, 9, 1 - 10. https://doi.org/10.18311/jbt/2018/21173
. (2000). Technical change, inequality, and the labor market. https://doi.org/10.3386/w7800
. (2018). System architecture of a driverless electric car in the grand cooperative driving challenge. Ieee Intelligent Transportation Systems Magazine, 10(1), 47 - 59. https://doi.org/10.1109/MITS.2017.2776135
. (2019). Synthesis of Strategies for Robotic Process Automation. (DIAG, Sapienza University of Rome, Italy). Retrieved de http://ceur-ws.org/Vol-2400/paper-53.pdf
. (2017). SPIE ProceedingsBuilding a framework to manage trust in automation. In SPIE Defense + SecurityMicro- and Nanotechnology Sensors, Systems, and Applications IX (10194, 101941U). https://doi.org/10.1117/12.2264245
. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal Of Economics, 118, 1279–1333. https://doi.org/10.1162/003355303322552801
. (2020). The Robot Economy : Here It Comes. International Journal Of Social Robotics. https://doi.org/10.1007/s12369-020-00686-1
. (2018). Robot companions: A legal and ethical analysis. The Information Society, 34(3), 130 - 140. https://doi.org/10.1080/01972243.2018.1444249
. (2017). Revisiting the risk of automation. Economics Letters, 159, 157 - 160. https://doi.org/10.1016/j.econlet.2017.07.001
. (2018). Reports of the Workshops Held at the Sixth AAAI Conference on Human Computation and Crowdsourcing. Ai Magazine, 39, 57–63.
. (2018). Recent advances in social & cognitive robotics and imminent ethical challenges. In 10th International RAIS Conference on Social Sciences and Humanities. Retrieved de https://www.atlantis-press.com/proceedings/rais-18/25902731
. (2018). A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis. Npj Digital Medicine, 1(1). https://doi.org/10.1038/s41746-018-0057-x