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1. Statistically Efficient Off-Policy Policy Gradients

   Kallus, Nathan; Uehara, Masatoshi (January 2020, International Conference on Machine Learning)
2. Modular Policy Composition with Policy Centroids

   Adhikaray, Sandesh; Boots, Byron (April 2022, The Multi-disciplinary Conference on Reinforcement Learning and Decision Making)
3. The Privacy Policy Landscape After the GDPR

   https://doi.org/10.2478/popets-2020-0004  

   Linden, Thomas; Khandelwal, Rishabh; Harkous, Hamza; Fawaz, Kassem (January 2020, Proceedings on Privacy Enhancing Technologies)

   Abstract The EU General Data Protection Regulation (GDPR) is one of the most demanding and comprehensive privacy regulations of all time. A year after it went into effect, we study its impact on the landscape of privacy policies online. We conduct the first longitudinal, in-depth, and at-scale assessment of privacy policies before and after the GDPR. We gauge the complete consumption cycle of these policies, from the first user impressions until the compliance assessment. We create a diverse corpus of two sets of 6,278 unique English-language privacy policies from inside and outside the EU, covering their pre-GDPR and the post-GDPR versions. The results of our tests and analyses suggest that the GDPR has been a catalyst for a major overhaul of the privacy policies inside and outside the EU. This overhaul of the policies, manifesting in extensive textual changes, especially for the EU-based websites, comes at mixed benefits to the users. While the privacy policies have become considerably longer, our user study with 470 participants on Amazon MTurk indicates a significant improvement in the visual representation of privacy policies from the users’ perspective for the EU websites. We further develop a new workflow for the automated assessment of requirements in privacy policies. Using this workflow, we show that privacy policies cover more data practices and are more consistent with seven compliance requirements post the GDPR. We also assess how transparent the organizations are with their privacy practices by performing specificity analysis. In this analysis, we find evidence for positive changes triggered by the GDPR, with the specificity level improving on average. Still, we find the landscape of privacy policies to be in a transitional phase; many policies still do not meet several key GDPR requirements or their improved coverage comes with reduced specificity. 

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4. The Future of HCI-Policy Collaboration

   https://doi.org/10.1145/3613904.3642771  

   Yang, Qian; Wong, Richmond Y; Jackson, Steven; Junginger, Sabine; Hagan, Margaret D; Gilbert, Thomas; Zimmerman, John (May 2024, ACM)

   Policies significantly shape computation’s societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the “border” of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if HCI fosters a mosaic of methods and knowledge contributions that blend system, human, and policy expertise in various ways, just like HCI has done with blending system and human expertise? We present this re-imagined HCI-policy relationship as a provocation and highlight its usefulness: It spotlights previously overlooked system-people-policy interaction work in HCI. It unveils new opportunities for HCI’s futuring, empirical, and design projects. It allows HCI to coordinate its diverse policy engagements, enhancing its collective impact on policy outcomes. 

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5. The Environmental Bias of Trade Policy*

   https://doi.org/10.1093/qje/qjaa042  

   Shapiro, Joseph S (December 2020, The Quarterly Journal of Economics)

   Abstract This article describes a new fact, then analyzes its causes and consequences: in most countries, import tariffs and nontariff barriers are substantially lower on dirty than on clean industries, where an industry’s “dirtiness” is defined as its carbon dioxide (CO2) emissions per dollar of output. This difference in trade policy creates a global implicit subsidy to CO2 emissions in internationally traded goods and contributes to climate change. This global implicit subsidy to CO2 emissions totals several hundred billion dollars annually. The greater protection of downstream industries, which are relatively clean, substantially accounts for this pattern. The downstream pattern can be explained by theories where industries lobby for low tariffs on their inputs but final consumers are poorly organized. A quantitative general equilibrium model suggests that if countries applied similar trade policies to clean and dirty goods, global CO2 emissions would decrease and global real income would change little. 

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