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Supports datetime, date, time, year, month. A Vuejs draggable date selector with a fresh responsive design, mobile ready and 0 dependencies, 17kb gzipped. Slider vue-slider-component – Slider for vue1. Drag and Drop vuedraggable – Vue component allowing drag-and-drop sorting in sync with View-Model.
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Static website generator VuePress – Minimalistic Vue-powered static site generator. Peco – A static website generator for humans. Gridsome – Build super fast, modern websites with Vue. Myfirebase – A decoupled Single page application Framework which is highly compatible with google firebase. Vue-Access-Control Frontend access control framework based Vue. Basys Toolbox for building full-stack Vue. V-Craft – A toolset for building extensible page builders with Vue.
FormKit – Vue 3 form development. Form inputs, validation, submission, error handling, generation, accessibility, theming, and more.
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Virtual scrollbar VBar – The virtual responsive cross-browser scrollbar component for Vue. Vuebar – Vue 2 directive for custom scrollbar that uses native scroll behavior. Lightweight, performant, customizable and without dependencies. Use as a vue component, a vue directive or with vanilla js. Using vue directive. Fast, small, has no dependencies, live demo. Customize the scroll behavior vue-scroll-behavior – Customize the scroll behavior on route navigation. Especially hash mode. Seamless scrolling vue-seamless-scroll – A simple, Seamless scrolling for Vue.
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DotVue – Implement. NET with server ViewModel postback. Single-file components with server-side ViewModel. Use all power of VueJS with simple C server-side data access. It’s fast! These three foundational elements for a bias impact statement are reflected in a discrete set of questions that operators should answer during the design phase to filter out potential biases Table 1. Operators of algorithms should also consider the role of diversity within their work teams, training data, and the level of cultural sensitivity within their decision-making processes.
Employing diversity in the design of algorithms upfront will trigger and potentially avoid harmful discriminatory effects on certain protected groups, especially racial and ethnic minorities. While the immediate consequences of biases in these areas may be small, the sheer quantity of digital interactions and inferences can amount to a new form of systemic bias. The bias impact statement should not be an exhaustive tool. For algorithms with more at stake, ongoing review of their execution should be factored into the process.
The goal here is to monitor for disparate impacts resulting from the model that border on unethical, unfair, and unjust decision-making. When the process of identifying and forecasting the purpose of the algorithm is achieved, a robust feedback loop will aid in the detection of bias, which leads to the next recommendation promoting regular audits.
The formal and regular auditing of algorithms to check for bias is another best practice for detecting and mitigating bias. While some audits may require technical expertise, this may not always be the case. Facial recognition software that misidentifies persons of color more than whites is an instance where a stakeholder or user can spot biased outcomes, without knowing anything about how the algorithm makes decisions.
Developing a regular and thorough audit of the data collected for the algorithmic operation, along with responses from developers, civil society, and others impacted by the algorithm, will better detect and possibly deter biases. The experience of government officials in Allegheny County reflects the importance of third-party auditing.
The county took ownership of its use of the tool, worked collaboratively with the developer, and commissioned an independent evaluation of its direct and indirect effects on the maltreatment screening process, including decision accuracy, workload, and consistency.
County officials also sought additional independent research from experts to determine if the software was discriminating against certain groups. In , the findings did identify some statistical imbalances, with error rates higher across racial and ethnic groups. White children who were scored at the highest-risk of maltreatment were less likely to be removed from their homes compared to African-American children with similar risk scores.
Facebook recently completed a civil rights audit to determine its handling of issues and individuals from protected groups. Recent actions by Facebook to ban white nationalist content or address disinformation campaigns are some of the results of these efforts. Roundtable participants largely acknowledged the notion that organizations should employ cross-functional teams.
Not all decisions will necessitate this type of cross-team review, but when these decisions carry risks of real harm, they should be employed. In the mitigation of bias and the management of the risks associated with the algorithm, collaborative work teams can compensate for the blind-spots often missed in smaller, segmented conversations and reviews.
Bringing together experts from various departments, disciplines, and sectors will help facilitate accountability standards and strategies for mitigating online biases, including from engineering, legal, marketing, strategy, and communications. Even with all the precautionary measures listed above, there is still some risk that algorithms will make biased decisions. People will continue to play a role in identifying and correcting biased outcomes long after an algorithm is developed, tested, and launched.
While more data can inform automated decision-making, this process should complement rather than fully replace human judgement. We need to hire more and get them to focus on edge cases. As the U. Because the values of creators and users of algorithms shift over time, humans must arbitrate conflicts between outcomes and stated goals.
In addition to periodical audits, human involvement provides continuous feedback on the performance of bias mitigation efforts. As indicated throughout the paper, policymakers play a critical role in identifying and mitigating biases, while ensuring that the technologies continue to make positive economic and societal benefits.
Regulatory sandboxes are perceived as one strategy for the creation of temporary reprieves from regulation to allow the technology and rules surrounding its use to evolve together. These policies could apply to algorithmic bias and other areas where the technology in question has no analog covered by existing regulations. Rather than broaden the scope of existing regulations or create rules in anticipation of potential harms, a sandbox allows for innovation both in technology and its regulation.
Even in a highly regulated industry, the creation of sandboxes where innovations can be tested alongside with lighter touch regulations can yield benefits. For example, companies within the financial sector that are leveraging technology, or fintech, have shown how regulatory sandboxes can spur innovation in the development of new products and services. Some of these activities mirror those of regular banks, and those would still fall under existing rules, but new ways of approaching tasks would be allowed within the sandbox.
The U. Treasury recently reported not only on the benefits that countries that have adopted fintech regulatory sandboxes have realized, but recommended that the U. Regulatory safe harbors could also be employed, where a regulator could specify which activities do not violate existing regulations. For example, Section of the Communications Decency Act removed liability from websites for the actions of their users, a provision widely credited with the growth of internet companies like Facebook and Google.
Applying a similar approach to algorithms could exempt their operators from liabilities in certain contexts while still upholding protections in others where harms are easier to identify. In line with the previous discussion on the use of certain protected attributes, safe harbors could be considered in instances where the collection of sensitive personal information is used for the specific purposes of bias detection and mitigation. Widespread algorithmic literacy is crucial for mitigating bias.
Given the increased use of algorithms in many aspects of daily life, all potential subjects of automated decisions would benefit from knowledge of how these systems function.
Just as computer literacy is now considered a vital skill in the modern economy, understanding how algorithms use their data may soon become necessary. The subjects of automated decisions deserve to know when bias negatively affects them, and how to respond when it occurs.
Feedback from users can share and anticipate areas where bias can manifest in existing and future algorithms. Over time, the creators of algorithms may actively solicit feedback from a wide range of data subjects and then take steps to educate the public on how algorithms work to aid in this effort.
Public agencies that regulate bias can also work to raise algorithmic literacy as part of their missions. In both the public and private sector, those that stand to lose the most from biased decision-making can also play an active role in spotting it. In December , President Trump signed the First Step Act, new criminal justice legislation that encourages the usage of algorithms nationwide.
There is a likelihood that these algorithms will perpetuate racial and class disparities, which are already embedded in the criminal justice system. As a result, African-Americans and poor people in general will be more likely to serve longer prison sentences. As outlined in the paper, these types of algorithms should be concerning if there is not a process in place that incorporates technical diligence, fairness, and equity from design to execution.
That is, when algorithms are responsibly designed, they may avoid the unfortunate consequences of amplified systemic discrimination and unethical applications. Some decisions will be best served by algorithms and other AI tools, while others may need thoughtful consideration before computer models are designed. Further, testing and review of certain algorithms will also identify, and, at best, mitigate discriminatory outcomes.
For operators of algorithms seeking to reduce the risk and complications of bad outcomes for consumers, the promotion and use of the mitigation proposals can create a pathway toward algorithmic fairness, even if equity is never fully realized. The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public.
The conclusions and recommendations of any Brookings publication are solely those of its author s , and do not reflect the views of the Institution, its management, or its other scholars. Paul Resnick is also a consultant to Facebook, but this work is independent and his views expressed here are his own. The findings, interpretations, and conclusions posted in this piece are not influenced by any donation.
Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. Angwin, Julia, and Terry Parris Jr. ProPublica, October 28, Barocas, Solon, and Andrew D. Blass, Andrea, and Yuri Gurevich. Algorithms: A Quest for Absolute Definitions. Chessell, Mandy. Chodosh, Sara. Courtland, Rachel. DeAngelius, Stephen F. Elejalde-Ruiz, Alexia. Hiring is in the midst of technological revolution with algorithms, chatbots.
Eubanks, Virginia. Garbade, Michael J. Machine Learning vs. Griggs v. Duke Power Company, Oyez. Guerin, Lisa. Hadhazy, Adam. Hamilton, Isobel Asher. Hardesty, Larry. High-level Expert Group on Artificial Intelligence. Ingold, David, and Spencer Soper. Should It? Kearns, Michael. Locklear, Mallory. Lopez, German. Mnuchin, Steven T. Department of the Treasury, July Romei, Andrea, and Salvatore Ruggieri.
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