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Full-Text Articles in Law
Introduction To The Future Of Remote Work, Nicola Countouris, Valerio De Stefano, Agnieszka Piasna, Silvia Rainone
Introduction To The Future Of Remote Work, Nicola Countouris, Valerio De Stefano, Agnieszka Piasna, Silvia Rainone
Articles & Book Chapters
Debates on the future of work have taken a more fundamental turn in the wake of the Covid-19 pandemic. Early in 2020, when large sections of the workforce were prevented from coming to their usual places of work, remote work became the only way for many to continue to perform their professions. What had been a piecemeal, at times truly sluggish, evolution towards a multilocation approach to work suddenly turned into an abrupt, radical and universal shift. It quickly became clear that the consequences of this shift were far more significant and far-reaching than simply changing the workplace’s address. They …
Out Of Sight, Out Of Mind? Remote Work And Contractual Distancing, Nicola Countouris, Valerio De Stefano
Out Of Sight, Out Of Mind? Remote Work And Contractual Distancing, Nicola Countouris, Valerio De Stefano
Articles & Book Chapters
Since the Covid-19 pandemic, remote work has acquired quasi-Marmite status. It has become difficult, if not impossible, to approach the issue in a measured and dispassionate way, which is one of the reasons books such as the present one are being published. Remote work is often seen as anathema by some who associate it with laziness, low productivity and the degradation of the social fabric of firms and of their creative and collaborative potential. The notorious views of CEOs such as Tesla and Twitter’s Elon Musk or JP Morgan’s Jamie Dimon come to mind, indicative – in the view of …
Human-Centered Design To Address Biases In Artificial Intelligence, Ellen W. Clayton, You Chen, Laurie L. Novak, Shilo Anders, Bradley Malin
Human-Centered Design To Address Biases In Artificial Intelligence, Ellen W. Clayton, You Chen, Laurie L. Novak, Shilo Anders, Bradley Malin
Vanderbilt Law School Faculty Publications
The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. …