Over the past year, responsibly developed AI has transformed health screenings, supported<\/a>fact-checking<\/a> to battle misinformation and save lives, predicted Covid-19 cases<\/a> to support public health, and protected wildlife<\/a> after bushfires. Developing AI in a way that gets it right for everyone requires openness, transparency, and a clear focus on understanding the societal implications. That is why we were among the first companies to develop and publish AI Principles<\/a>\u00a0and why, each year, we share updates on our progress.<\/p>\n<\/div>\n<\/div>\n Building on previous AI Principles updates in 2018<\/a>,\u00a0 2019<\/a>, and 2020<\/a>, today we\u2019re providing our latest overview of what we\u2019ve learned, and how we\u2019re applying these learnings.<\/p>\n<\/div>\n<\/div>\n <\/p>\n In the last year, to ensure our teams have clarity from day one, we\u2019ve added an introduction to our AI Principles for engineers and incoming hires in technical roles. The course presents each of the Principles as well as the applications we will not pursue.<\/p>\n<\/div>\n<\/div>\n Integrating our Principles into the work we do with enterprise customers is key, so we\u2019ve continued to make our AI Principles in Practice training mandatory for customer-facing Cloud employees. A version of this training is available to all Googlers.<\/p>\n<\/div>\n<\/div>\n There is no single way to apply the AI Principles to specific features and product development. Training must consider not only the technology and data, but also where and how AI is used. To offer a more comprehensive approach to implementing the AI Principles, we\u2019ve been developing opportunities for Googlers to share their points of view on the responsible development of future technologies, such as the AI Principles Ethics Fellowship for Google\u2019s Employee Resource Groups. Fellows receive AI Principles training and craft hypothetical case studies to inform how Google prioritizes socially beneficial applications. This inaugural year, 27 fellows selected from 191 applicants from around the world wrote and presented case studies on topics such as genome datasets and a Covid-19 content moderation workflow.<\/p>\n<\/div>\n<\/div>\n Other programs include a bi-weekly Responsible AI Tech Talk Series featuring external experts, such as the Brookings Institution\u2019s Dr. Nicol Turner Lee presenting on detecting and mitigating algorithmic bias.<\/p>\n<\/div>\n<\/div>\n <\/p>\n To bring together multiple teams working on technical tools and research, this year we formed<\/a> the Responsible AI and Human-Centered Technology organization. The basic and applied researchers in the organization are devoted to developing technology and best practices for the technical realization of the AI Principles guidance.<\/p>\n<\/div>\n<\/div>\n As discussed in our December 2020 End-of-Year report<\/a>, we regularly release these tools to the public. Currently, researchers are developing Know Your Data<\/a> (in beta) to help developers understand datasets with the goal of improving data quality, helping to mitigate fairness and bias issues.<\/p>\n<\/div>\n<\/div>\n Know Your Data, a Responsible AI tool in beta<\/p>\n<\/div>\n<\/figcaption><\/div>\n<\/div>\n Product teams use these tools to evaluate their work\u2019s alignment with the AI Principles. For example, the Portrait Light<\/a> feature available in both Pixel\u2019s Camera and Google Photos uses multiple machine learning components to instantly add realistic synthetic lighting to portraits. Using computational methods to achieve this effect, however, raised several responsible innovation challenges, including potentially reinforcing unfair bias (AI Principle #2) despite the goal of building a feature that works for all users. So the Portrait Light team generated a training dataset containing millions of photos based on an original set of photos of different people in a diversity of lighting environments, with their explicit consent. The engineering team used various Google Responsible AI tools to test proactively whether the ML models used in Portrait Light performed equitably across different audiences.<\/p>\n<\/div>\n<\/div>\n Our ongoing technical research related to responsible innovation in AI in the last 12 months has led to more than 200 research papers and articles that address AI Principles-related topics. These include exploring and mitigating data cascades<\/a>; creating the first model-agnostic framework for partially local federated learning<\/a> suitable for training and inference at scale; and analyzing the energy- and carbon-costs of training six recent ML models to reduce the carbon footprint of training an ML system<\/a> by up to 99.9%.<\/p>\n<\/div>\n<\/div>\n <\/p>\n To help our teams put the AI Principles into practice, we deploy our decision-making process<\/a> for reviewing new custom AI projects in development \u2014 from chatbots to newer fields such as affective technologies. These reviews are led by a multidisciplinary Responsible Innovation team, which draws on expertise in disciplines including trust and safety, human rights, public policy, law, sustainability, and product management. The team also advises product areas, on specific issues related to serving enterprise customers. Any Googler, at any level, is encouraged to apply for an AI Principles review of a project or planned product or service.<\/p>\n<\/div>\n<\/div>\n Teams can also request other Responsible Innovation services, such as informal consultations or product fairness testing with the Product Fairness (ProFair) team. ProFair tests products from the user perspective to investigate known issues and find new ones, similar to how an academic researcher would go about identifying fairness issues.<\/p>\n<\/div>\n<\/div>\n Our Google Cloud, Image Search, Next Billion Users and YouTube product teams have engaged with ProFair to test potential new projects for fairness. Consultations include collaborative testing scenarios, focus groups, and adversarial testing of ML models to address improving equity in data labels, fairness in language models, and bias in predictive text, among other issues. Recently, the ProFair team spent nine months consulting with Google researchers on creating an object recognition dataset for physical landmarks (such as buildings), developing criteria for how to choose which classes to recognize and how to determine the amount of training data per class in ways that would assign a fairer relevance score to each class.<\/p>\n<\/div>\n<\/div>\n Reviewers weigh the nature of the social benefit involved and whether it substantially exceeds potential challenges. For example, in the past year, reviewers decided not to publicly release ML models that can create photo-realistic synthetic faces made with generative adversarial networks (GANs), because of the risk of potential misuse by malicious actors to create \u201cdeepfakes\u201d for misinformation purposes.<\/p>\n<\/div>\n<\/div>\n As another example, a research team requested a review of a new ML training dataset for computer vision fairness techniques that offered more specific attributes about people, such as perceived gender and age-range. The team worked with Open Images<\/a>, an open data project containing ~9 million images spanning thousands of object categories and bounding box annotations for 600 classes. Reviewers weighed the risk of labeling the data with the sensitive labels of perceived gender presentation and age-range, and the societal benefit of these labels for fairness analysis<\/a> and bias mitigation<\/a>. Given these risks, reviewers required creation of a data card<\/a> explaining the details and limitations of the project. We released the MIAP (More Inclusive Annotations for People)<\/a> dataset in the Open Images Extended collection. The collection contains more complete bounding box annotations for the person class hierarchy in 100K images containing people. Each annotation is also labeled with fairness-related attributes. MIAP<\/a> was accepted and presented<\/a> at the 2021 Artificial Intelligence, Ethics and Society conference.<\/p>\n<\/div>\n<\/div>\n <\/p>\n We remain committed to contributing to emerging international principles for responsible AI innovation. For example, we submitted a response<\/a> to the European Commission consultation<\/a> on the inception impact assessment on ethical and legal requirements for AI and feedback on NITI Aayog\u2019s working document on Responsible Use of AI<\/a> to guide national principles for India. We also supported the Singaporean government\u2019s Guide to Job Redesign in the Age of AI<\/a>, which outlines opportunities to optimize AI benefits by redesigning jobs and reskilling employees.<\/p>\n<\/div>\n<\/div>\n Our efforts to engage global audiences, from students to policymakers, center on educational programs and discussions to offer helpful and practical ML education, including:<\/p>\n<\/div>\n<\/div>\n To support these workshops and events with actionable and equitable programming designed for long-term collaboration, over the past year we\u2019ve helped launch:<\/p>\n<\/div>\n<\/div>\n <\/p>\n In the three years since Google released our AI Principles, we\u2019ve worked to integrate this ethical charter across our work \u2014 from the development of advanced technologies to business processes. As we learn and engage with people and organizations across society we\u2019re committed to sharing tools, processes and discoveries with the global community. You\u2019ll find many of these in our recently updated People + AI Research Guidebook<\/a> and on the Google AI responsibilities site<\/a>, which we update quarterly with case studies and other resources.\u00a0<\/p>\n<\/div>\n<\/div>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: Over the past year, responsibly developed AI has transformed health screenings, supportedfact-checking to battle misinformation and save lives, predicted Covid-19 cases to support public […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":4794,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/4793"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=4793"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/4793\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/4794"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=4793"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=4793"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=4793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Internal Education<\/h3>\n<\/div>\n<\/div>\n
Tools and Research<\/h3>\n<\/div>\n<\/div>\n
![\"Image](\"https:\/\/storage.googleapis.com\/gweb-uniblog-publish-prod\/original_images\/Know_Your_Data.gif\")
<\/figure>Operationalizing the Principles<\/h3>\n<\/div>\n<\/div>\n
External Engagement<\/h3>\n<\/div>\n<\/div>\n
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Committed to sharing tools and discoveries<\/h3>\n<\/div>\n<\/div>\n