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An algorithmic equity toolkit for technology audits by community advocates and activists (preprint: Under review)

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arXiv 2019年

作者： Katell, Michael Young, Meg Herman, Bernease Dailey, Dharma Tam, Aaron Guetler, Vivian Binz, Corinne Raz, Daniella Krafft, P.M. Information School University of Washington Information School University of Washington Science Institute University of Washington Human Centered Design and Engineering University of Washington Evans School of Public Policy and Governance University of Washington Department of Sociology West Virginia University Department of Computer Science Middlebury College School of Information University of Michigan Oxford Internet Institute University of Oxford

A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective accountability and regulation is stymied by a persistent lack of resources supporting public understanding of algorithms and artificial intelligence. Through interactions with a US-based civil rights organization and their coalition of community organizations, we identify a need for (i) heuristics that aid stakeholders in distinguishing between types of analytic and information systems in lay language, and (ii) risk assessment tools for such systems that begin by making algorithms more legible. The present work delivers a toolkit to achieve these aims. This paper both presents the Algorithmic Equity Toolkit (AEKit) Equity as an artifact, and details how our participatory process shaped its design. Our work fits within HCI scholarship as a demonstration of the value of HCI methods and approaches to problems in the area of algorithmic transparency and accountability. Copyright © 2019, The Authors. All rights reserved.

关键词： Transparency

Electric Vehicle policy Formulation Framework for SIDS in the Caribbean

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Electric Vehicle Policy Formulation Framework for SIDS in th...

IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Chris Meetoo Sanjay Bahadoorsingh Dillon Jaglal Vickram Balbadar Chandrabhan Sharma Kevin Baboolal Marlon Williams Department of Electrical and Computer Engineering The University of the West Indies St. Augustine Republic of Trinidad and Tobago Government Electrical Inspectorate Ministry of Public Utilities Republic of Trinidad and Tobago

Small Caribbean island power systems face numerous unique policy, technical, infrastructural and financial challenges which have stymied electric vehicle (EV) adoption. This paper outlines the approach to strategically plan for long-term EV penetration toward the development of an EV policy in Trinidad and Tobago with application to other Caribbean territories. Through analysis of the EV registration processes and roles of local stakeholders, legislation and coordinated enforcement processes have been identified to ensure safe development of EV systems. Internationally accepted practices have also been suitably modified for adoption by small island developing states (SIDS) to ensure an enabling environment for EVs is legislated.

关键词： Standards Batteries Safety Fuels Finance Europe Maintenance engineering

‘It’s reducing a human being to a percentage’;perceptions of justice in algorithmic decisions

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arXiv 2018年

作者： Binns, Reuben van Kleek, Max Veale, Michael Lyngs, Ulrik Zhao, Jun Shadbolt, Nigel Dept. of Computer Science University of Oxford United Kingdom Dept. of Science Technology Engineering & Public Policy University College London United Kingdom

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to ‘meaningful information about the logic’ behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people’s perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles—under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no ‘best’ approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions. Copyright © 2018, The Authors. All rights reserved.

关键词： Decision making

MODELLING AGENTS ENDOWED WITH SOCIAL PRACTICES: STATIC ASPECTS

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arXiv 2018年

作者： Mercuur, Rijk Dignum, Virginia Jonker, Catholijn M. Faculty of Technology Policy and Management Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Leiden Institute of Advanced Computer Science Leiden University

To understand societal phenomena through simulation, we need computational variants of socio-cognitive theories. Social Practice Theory has provided a unique understanding of social phenomena regarding the routinized, social and interconnected aspects of behaviour. This paper provides the Social Practice Agent (SoPrA) model that enables the use of Social Practice Theory (SPT) for agent-based simulations. We extract requirements from SPT, construct a computational model in the Unified Modelling Language, verify its implementation in Netlogo and Protégé and show how SoPrA maps on a use case of commuting. The next step is to model the dynamic aspect of SPT and validate SoPrA’s ability to provide understanding in different scenario’s. This paper provides the groundwork with a computational model that is a correct depiction of SPT, computational feasible and can be directly mapped to the habitual, social and interconnected aspects of a target scenario. Copyright © 2018, The Authors. All rights reserved.

关键词： Computation theory

Publisher Correction: The effect of large-scale anti-contagion policies on the COVID-19 pandemic

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Nature 2020年第7824期585卷 E7页

作者： Solomon Hsiang Daniel Allen Sébastien Annan-Phan Kendon Bell Ian Bolliger Trinetta Chong Hannah Druckenmiller Luna Yue Huang Andrew Hultgren Emma Krasovich Peiley Lau Jaecheol Lee Esther Rolf Jeanette Tseng Tiffany Wu Global Policy Laboratory Goldman School of Public Policy UC Berkeley Berkeley CA USA. shsiang@berkeley.edu. National Bureau of Economic Research Cambridge MA USA. shsiang@berkeley.edu. Centre for Economic Policy Research London UK. shsiang@berkeley.edu. Global Policy Laboratory Goldman School of Public Policy UC Berkeley Berkeley CA USA. Agricultural & Resource Economics UC Berkeley Berkeley CA USA. Manaaki Whenua - Landcare Research Auckland New Zealand. Energy & Resources Group UC Berkeley Berkeley CA USA. Electrical Engineering & Computer Science Department UC Berkeley Berkeley CA USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Voltage Control in Distributed Generation under Measurement Falsification Attacks

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IFAC-PapersOnLine 2017年第1期50卷 8379-8384页

作者： Ma M. Teixeira A.M.H. van den Berg J. Palensky P. Faculty Electrical Engineering Mathematics and Computer Science United States Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands

Low-voltage distribution grids experience a rising penetration of inverter-based, distributed generation. In order to not only contribute to but also solve voltage problems, these inverters are increasingly asked to participate in intelligent grid controls. Communicating inverters implement distributed voltage droop controls. The impact of cyber-attacks to the stability of such distributed grid controls is poorly researched and therefore addressed in this article. We characterize the potential impact of several attack scenarios by employing the positivity and diagonal dominance properties. In particular, we discuss measurement falsification scenarios where the attacker corrupts voltage measurement data received by the voltage droop controllers. Analytical, control-theoretic methods for assessing the impact on system stability and voltage magnitude are presented and validated via simulation. © 2017

关键词： Cyber security distribution network risk assessment stability voltage control

The redshirt in engineering consortium: Progress and early insights 125

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The redshirt in engineering consortium: Progress and early i...

125th ASEE Annual Conference and Exposition

作者： Riskin, Eve A. Milford, Jana Callahan, Janet Cosman, Pamela Schneider, John B. Pitts, Kevin Knaphus-Soran, Emily Llewellyn, Donna C. Delaney, Ann E. Myers, Beth A. Tetrick, Katherine Christine Cunningham, Sonya Ennis, Tanya D. O'Connor, Kevin Ferrez, Michelle Pan, Tiffany D. Baldis, Jessica EE Department University of Washington United States Engineering GoldShirt Program University of Colorado Boulder United States Micron School of Materials Science and Engineering Boise State University United States Department of Electrical and Computer Engineering University of California San Diego United States Voiland College of Engineering and Architecture United States Department of Physics University of Illinois Urbana-Champaign United States University of Washington United States Institute for STEM and Diversity Initiatives Boise State University United States Public Policy and Administration Boise State University United States Department of Analytics Assessment and Accreditation University of Colorado Boulder United States Program Washington State University United States University of Washington STARS Program Diversity and Access College of Engineering United States Engineering GoldShirt Program University of Colorado Boulder College of Engineering and Applied Science United States Department of Educational Psychology and Learning Sciences University of Colorado Boulder United States United States

The NSF-funded Redshirt in engineering Consortium was formed in 2016 with the goal of enhancing the ability of academically talented but underprepared students coming from low-income backgrounds to successfully graduate with engineering degrees. The Consortium takes its name from the practice of redshirting in college athletics, with the idea of providing an extra year and support to help promising engineering students complete a bachelor's degree. The Consortium builds on the success of three existing "academic redshirt" programs and expands the model to three new schools. The Existing Redshirt Institutions (ERIs) help mentor and train the new Student Success Partners (SSPs), and SSPs contribute their unique expertise to help ERIs improve existing Redshirt programs. The Redshirt model consists of seven main programmatic components aimed at improving the engagement, retention, and graduation of students underrepresented in engineering. These components include: "intrusive" academic advising and support services, an intensive first-year academic curriculum, community-building (including pre-matriculation summer programs), career awareness and vision, faculty mentorship, NSF S-STEM scholarships, and second-year support. Successful implementation of these activities is intended to produce two main long-term outcomes: a six-year graduation rate of 60%-75% for Redshirt students, and increased rates of enrollment and graduation of Pell-eligible, URM, and women students in engineering at participating universities. In the first year of the grant (AY 16-17), SSPs developed their own Redshirt programs, hired and trained staff, and got their programs off the ground. ERIs implemented faculty mentorship programs and expanded support to Redshirt students into their sophomore year. In the second year (AY 17-18), Redshirt programs were expanded at the ERIs while SSPs welcomed their first cohorts of Redshirt students. This Work in Progress paper describes the Redshirt programs at e

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An Open Natural Language Processing (NLP) Framework for EHR-based Clinical Research: A Case Demonstration Using the National COVID Cohort Collaborative (N3C)

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arXiv 2021年

作者： Liu, Sijia Wen, Andrew Wang, Liwei He, Huan Fu, Sunyang Miller, Robert Williams, Andrew Harris, Daniel Kavuluru, Ramakanth Liu, Mei Abu-El-rub, Noor Schutte, Dalton Zhang, Rui Rouhizadeh, Masoud Osborne, John D. He, Yongqun Topaloglu, Umit Hong, Stephanie S. Saltz, Joel H. Schaffter, Thomas Pfaff, Emily Chute, Christopher G. Duong, Tim Haendel, Melissa A. Fuentes, Rafael Szolovits, Peter Xu, Hua Liu, Hongfang Department of Artificial Intelligence and Informatics Mayo Clinic United States Tufts Clinical and Translational Science Institute Tufts Medical Center United States Department of Internal Medicine University of Kentucky United Kingdom Department of Internal Medicine University of Kansas Medical Center United States Department of Pharmaceutical Care & Health Systems University of Minnesota Twin Cities United States Department of Pharmaceutical Outcomes & Policy University of Florida United States Department of Computer Science University of Alabama at Birmingham United States University of Michigan Medical School United States Wake Forest Baptist Medical Center United States Department of Medicine Johns Hopkins University United States Department of Biomedical Informatics Stony Brook University United States Sage Bionetwork United States Department of Medicine University of North Carolina Chapel Hill United States Albert Einstein College of Medicine United States Center for Health AI University of Colorado Anschutz Medical Campus United States Alex Informatics United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Biomedical Informatics University of Texas Health Science Center Houston United States

Despite recent methodology advancements in clinical natural language processing (NLP), adoption of clinical NLP models within the clinical and translational research community remains hindered by issues with ETL process heterogeneity and human factor variations. In this study, we proposed an open NLP development framework with the aim of addressing these issues. The viability of such a platform was evaluated on a COVID-19 use case through sites participating in the National COVID Cohort Collaborative (N3C). As part of our assessment of the impact of single vs. multi-site NLP algorithm development, we evaluated the performance of both an NLP ruleset developed solely using a single site’s clinical narratives as well as one further refined using a synthetic derived dataset sourced from three sites (Mayo, UKen, and UMN). The single-site ruleset resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively, while the multi-site NLP ruleset improved performances to 0.884, 0.769 and 0.806. The results of our use case test run inform us of the importance of a multisite federated development, evaluation, and implementation framework. As such, we aim to meet this need with our framework by providing the tools necessary to conduct NLP development in a collaborative manner through consensus building, process coordination, and best practice sharing. © 2021, CC BY.

关键词： Natural language processing systems

Examining interconnection and net metering policy for distributed generation in the United States

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Renewable Energy Focus 2017年 22-23卷 10-19页

作者： Schelly, Chelsea Louie, Edward P. Pearce, Joshua M. Department of Social Sciences Michigan Technological University 1400 Townsend Drive HoughtonMI49931 United States School of Public Policy Oregon State University CorvallisOR97331-3703 United States Materials Science and Engineering and Department of Electrical & Computer Engineering Michigan Technological University 1400 Townsend Drive HoughtonMI49931 United States

Following requirements of the Energy policy Act of 2005, most U.S. states require utility companies to adopt interconnection and net metering policies, allowing customers to become prosumers who both consume and produce electricity, generating electricity using distributed renewable energy technologies, connecting to the existing electric utility grid and receiving compensation for excess electricity generation. This paper reviews existing interconnection and net metering policies instituted by investor owned utilities (IOUs) across the U.S., specifically focused on policies regulating installations of small scale, residential or Tier 1 (a term used to indicate policies applicable to smaller scale rather than larger scale, although the size at which DG systems are classified as either Tier 1 or higher tiers varies by utility). publicly available data from each IOU reveal inconsistencies in interconnection and net metering policies, within states and even within individual companies. In addition, accurate information is often unavailable to consumers. Perhaps most importantly, results suggest that compensation for excess distributed generation often lacks transparent articulation in utility policy. The results of this study provide important insight into interconnection and net metering policies for distributed renewable energy generation, as states and utilities continue to modify interconnection and net metering policies in response to increased adoption of distributed renewable energy systems. © 2017 Elsevier Ltd

关键词： Distributed power generation