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Social and Behavioral Sciences Commons™
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Articles 1 - 5 of 5
Full-Text Articles in Social and Behavioral Sciences
Intelligent Software Tools For Recruiting, Swatee B. Kulkarni, Xiangdong Che
Intelligent Software Tools For Recruiting, Swatee B. Kulkarni, Xiangdong Che
Journal of International Technology and Information Management
In this paper, we outline how recruiting and talent acquisition gained importance within HRM field, then give a brief introduction to the newest tools used by the professionals for recruiting and lastly, describe the Artificial Intelligence-based tools that have started playing an increasingly important role. We also provide further research suggestions for using artificial intelligence-based tools to make recruiting more efficient and cost-effective.
The Incorporation Of Moral-Development Language For Machine-Learning Companion Robots, Patrick Lee Plaisance, Joe Cruz
The Incorporation Of Moral-Development Language For Machine-Learning Companion Robots, Patrick Lee Plaisance, Joe Cruz
Computer Ethics - Philosophical Enquiry (CEPE) Proceedings
Among the ongoing debates over ethical implications of artificial-intelligence development and applications, AI morality, and the nature of autonomous agency for robots, how to think about the moral assumptions implicit in machine-learning capacities for so-called companion robots is arguably an urgent one. This project links the development of machine-learning algorithmic design with moral-development theory language. It argues that robotic algorithmic responses should incorporate language linked to higher-order moral reasoning, reflecting notions of universal respect, community obligation and justice to encourage similar deliberation among human subjects.
The New Legal Landscape For Text Mining And Machine Learning, Matthew Sag
The New Legal Landscape For Text Mining And Machine Learning, Matthew Sag
Faculty Articles
Now that the dust has settled on the Authors Guild cases, this Article takes stock of the legal context for TDM research in the United States. This reappraisal begins in Part I with an assessment of exactly what the Authors Guild cases did and did not establish with respect to the fair use status of text mining. Those cases held unambiguously that reproducing copyrighted works as one step in the process of knowledge discovery through text data mining was transformative, and thus ultimately a fair use of those works. Part I explains why those rulings followed inexorably from copyright's most …
Automatically Extracting Meaning From Legal Texts: Opportunities And Challenges, Kevin D. Ashley
Automatically Extracting Meaning From Legal Texts: Opportunities And Challenges, Kevin D. Ashley
Articles
This paper examines impressive new applications of legal text analytics in automated contract review, litigation support, conceptual legal information retrieval, and legal question answering against the backdrop of some pressing technological constraints. First, artificial intelligence (Al) programs cannot read legal texts like lawyers can. Using statistical methods, Al can only extract some semantic information from legal texts. For example, it can use the extracted meanings to improve retrieval and ranking, but it cannot yet extract legal rules in logical form from statutory texts. Second, machine learning (ML) may yield answers, but it cannot explain its answers to legal questions or …
Transparency And Algorithmic Governance, Cary Coglianese, David Lehr
Transparency And Algorithmic Governance, Cary Coglianese, David Lehr
All Faculty Scholarship
Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …