1) Google Cloud's Responsible AI Approach AI Principles:
2) How Google Cloud implements our AI Principles
Google Cloud's Responsible AI Approach AI Principles:
Google's AI Principles have acted as a living constitution since 2018, keeping us motivated by a single goal. The Responsible Innovation team, our center of excellence, directs how we apply these values across the firm and defines Google Cloud's approach to developing innovative technologies, conducting research, and writing our policies.
- Putting theories into action: Thorough assessments are an essential component in developing effective AI. To ensure consistency with Google Cloud's AI Principles, two distinct review committees conduct in-depth ethical evaluations as well as risk and opportunity assessments for each technological product we produce and early-stage transactions including bespoke work. Find out more.
- Education and tools: Responsible AI tools are becoming a more effective means to analyze and comprehend AI models. To provide model transparency in an organized, accessible manner, we are developing tools such as Explainable AI, Model Cards, and the TensorFlow open-source toolkit. Through Responsible AI practices, fairness best practices, technical references, and tech ethics resources, we share what we're learning.
How Google Cloud implements our AI Principles:
Our governance methods are meant to ensure that our AI Principles are implemented in a systematic and repeatable manner. These procedures include product and deal evaluations, best practices for machine learning development, internal and external education, tools and solutions like Cloud's Explainable AI, and guidelines for how we discuss and engage with our customers and partners. We established two distinct review processes in Cloud. One concentrates on sophisticated technology items, while the other on early-stage projects involving specialized work above and beyond our usually accessible products.
1) Coordinating our product development:
When we set out to create new products and services involving advanced technologies, as well as similar products and services that were already widely available when we began this process, we conduct rigorous and in-depth ethical analyses, assessing risks and opportunities across each principle and conducting robust live reviews that can involve difficult but often inspiring conversations. We freely address vital but difficult themes including machine learning fairness, unconscious bias, how our personal experiences may differ significantly from those potentially touched by AI, and a variety of other factors that may influence whether we proceed with a certain product. We devote significant time and effort to establishing and maintaining psychological safety within our review teams in order to ensure that all views are heard. We devote significant time and effort to establishing and maintaining psychological safety within our review teams in order to guarantee that all perspectives and issues are heard and respected. These evaluations have made it easier to have uncomfortable conversations about possibly negative effects, which we can then seek to prevent.
2) Using bespoke AI to align our customer deals:
We also assess early-stage commercial arrangements that include us constructing bespoke AI for a customer before they are signed. We work early on to identify whether the project will include the use of sophisticated technology in ways that would violate our AI Principles. We've iterated on what we deem to be in or out of scope for our engagement reviews as we've gained skill in our evaluations, generated "case law" over time, and thought hard about where we may draw the lines of our responsibilities. We have been able to focus our deal reviews on unique and creative use cases in how our generally accessible goods may be used since we have done in-depth assessments of our generally available products and established alignment plans for each of them.
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3) Our product reviews help to shape our roadmap and best practices:
The outcomes of these evaluations might take a variety of shapes. We might reduce the product's scope, maybe concentrating on a single use case rather than a general-purpose API. In tackling facial recognition, for example, we created our Celebrity Recognition API as a restricted, thoroughly studied solution. Other mitigations might include launch-related training materials or best practices, such as a Model Card, or more particular implementation guidance, such as our Inclusive ML guide. In certain circumstances, we may impose policies or terms of service, while in others, we may decide not to proceed with a product at all.
Over the previous five years, we've built a number of best practices that I frequently share with firms looking to implement their own ideas. They are as follows:
- Designing responsibility: We strive for thorough evaluations early in the development lifecycle, which has shown to be critical for establishing alignment.
- There is no ethical checklist: It might be tempting to make a decision tree list that readily categorizes certain things as fine and others as not fine. I've tried it, as has nearly everyone I know who is going on this road. I'm sorry to be the bearer of terrible news, but this is not an option. Alignment decisions are guided by the junction of technology, data, use case, and where and how it is applied, which may be comparable but are never the same.
Google AI is the company's specialist research and development division devoted to the advancement of artificial intelligence (AI) and its applications. It was first known as Google Research. During the 2018 Google I/O conference, Google AI was relaunched as an Alphabet subsidiary, emphasizing its role as a pure research enterprise. Unlike other divisions, the major goal of Google AI is to do cutting-edge research rather than to offer commercial products.
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