As billions of people around the world continue to use products or services with AI at their core, it becomes more important than ever that AI is deployed responsibly: preserving trust and putting each individual user’s well-being first. It has always been our highest priority to build products that are inclusive, ethical, and accountable to our communities, and in the last month, especially as the US has grappled with its history of systemic racism, that approach has been, and continues to be, as important as ever.
, which guide the ethical development and use of AI in our research and products. The AI principles articulate . As one of the most popular ML frameworks in the world, with millions of downloads and a global developer community, TensorFlow is not only used across Google, but around the globe to solve challenging real-world problems. This is why we’re continuing to expand the Responsible AI toolkit in the TensorFlow ecosystem, so that developers everywhere can better integrate these principles in their ML development workflows.
In this blog post, we will outline ways to use TensorFlow to build AI applications with Responsible AI in mind. The collection of tools here are just the beginning of what we hope will be a growing toolkit and library of lessons learned and resources to apply them.
You can find all the tools discussed below at
To simplify our discussion, we’ll break the workflow into 5 key steps:
- Step 1: Define the problem
- Step 2: Collect and prepare the data
- Step 3: Build and train the model
- Step 4: Evaluate performance
- Step 5: Deploy and monitor
Step 1: Define the Problem
What am I building? What is the goal?Who am I building this for?
How are they going to use it? What are the consequences for the user when it fails?
The first step in any development process is the definition of the problem itself. When is AI actually a valuable solution, and what problem is it addressing? As you define your AI needs, make sure to keep in mind the different users you might be building for, and the different experiences they may have with the product.
For example, if you are building a medical model to screen individuals for a disease, as is done in this
The People + AI Research (PAIR) Guidebook, which focuses on designing human-centered AI, is a companion as you build, outlining the key questions to ask as you develop your product. It's based on insights from Googlers across 40 product teams. We recommend reading through the key questions—and use the helpful worksheets!—as you define the problem, but referring back to these questions as development proceeds.
You can utilize and
Details on Data Cards, a framework on how to create them, and guidance on how to integrate aspects of Data Cards into processes or tools you use will be published soon.
Step 3: Build and Train the Model
How do I preserve privacy or think about fairness while training my model?What techniques should I use?
Training your TensorFlow model can be one of the most complex pieces of the development process. How do you train it in such a way that it performs optimally for everyone while still preserving user privacy? We’ve developed a set of tools to simplify aspects of this workflow, and enable integration of best practices while you are setting up your TensorFlow pipeline:
library.
[New] We recently released a
You can also support privacy in training with differential privacy, which adds noise in your training to hide individual examples in the datasets. TensorFlow Privacy provides a set of optimizers that enable you to train with differential privacy, from the start.
In addition to building in privacy considerations when training your model, there may be a set of metrics that you want to configure and use in training machine learning problems to achieve desirable outcomes. Creating more equitable experiences across different groups, for example, is an outcome that may be difficult to achieve unless you consider taking into account a combination of metrics that satisfy this real-world requirement. The
Last week, we announced a , , and to help developers easily identify appropriate metrics for their needs and set up Fairness Indicators with a TensorFlow model. Visualizations are available via the widely popular highlighting how Fairness Indicators can be used with pandas, to enable evaluations over more datasets and data types.
Fairness Indicators is built on top of
Beyond the integration in Fairness Indicators, the What-If Tool can also be used in other user flows as a standalone tool and is accessible from , notebooks.
[New] Today, to help WIT users get started faster, we’re releasing , to Google Cloud users can take WIT’s capabilities a step further with Explainable AI, a toolkit that builds upon WIT to introduce additional interpretability features including You may also be interested in these tutorials for handling , similar to that mentioned above.
| to screen patients, for example, may no longer work effectively during a pandemic. It’s important that developers have tools that enable tracking of models, and clear channels and frameworks for communicating helpful details about their models, especially to developers who may inherit a model, or to users and policy makers who seek to understand how it will work for various people. The TensorFlow ecosystem has tools to help with this kind of lineage tracking and transparency: As you deploy your model, you could also accompany its deployment with a Model Card—a document structured in a format that serves as an opportunity for you to communicate the values and limitations of your model. Model Cards could enable developers, policy makers, and users to understand aspects about trained models, contributing to the larger developer ecosystem with added clarity and explainability so that ML is less likely to be used in contexts for which it is inappropriate. Based on a framework proposed in an , including their Object and Face Detection APIs, as well as a number of open source models. Today, you can get inspiration from the paper and It’s important to note that while Responsible AI in the ML workflow is a critical factor, building products with AI ethics in mind is a combination of technical, product, policy, process, and cultural factors. These concerns are multifaceted and fundamentally sociotechnical. Issues of fairness, for example, can often be traced back to histories of bias in the world’s underlying systems. As such, proactive AI responsibility efforts not only require measurement and modelling adjustments, but also policy and design changes to provide transparency, rigorous review processes, and a diversity of decision makers who can bring in multiple perspectives. This is why many of the tools and resources we covered in this post are founded in the sociotechnical research work we do at Google. Without such a robust foundation, these ML and AI models are bound to be ineffective in benefiting society as they could erroneously become integrated into the entanglements of decision-making systems. Adopting a cross-cultural perspective, grounding our work in human-centric design, extending transparency towards all regardless of expertise, and operationalizing our learnings into practices—these are some of the steps we take to responsibly build AI. We understand Responsible AI is an evolving space that is critical, which is why we are hopeful when we see how the TensorFlow community is thinking about the issues we’ve discussed—and more importantly, when the community takes action. In our latest explored areas of fairness, privacy, and interpretability, and showed us that Responsible AI tools should be well integrated into TensorFlow ecosystem libraries. We will be focusing on this to ensure these tools are easily accessible. As you begin your next TensorFlow project, we encourage you to use the tools above, and to provide us feedback at [email protected]. Share your learnings with us, and we’ll continue to do the same, so that we can together build products that truly work well for everyone. Vollständiger Original-Bericht Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org. Wie bewertest du diesen Beitrag? 1 Klick Feedback Teilen mit Netzwerk & Team: Hat Ihnen dieser Tipp / Anleitung geholfen? Community-Analysen & Experten-Meinungen 0Verfasse deine eigene Analyse, teile Workarounds oder diskutiere diesen Vorfall im Blog. Noch keine Community-Analyse verfasst. Markiere einen Textabschnitt oder klicke oben auf „ Eigene Analyse verfassen“! Community Pulse: Relevanz-Einschätzung 1 Klick Experten-Votum 🔴 Akute Relevanz 0% 🟡 In Evaluierung 0% 🟢 Keine Auswirkung 0% Spannende Innovation 0% Verwandte Story-Cluster & Quellen (Vektor-KI) Tipp: Mit Pfeiltasten [ ← ] und [ → ] blättern 
Ähnliche Beiträge
🔍 Verwandte News
Auch interessante Nachrichten Responsible AI with TensorFlowThematisch verwandte Begriffe: Responsible, with, TensorFlow · 6 Treffer 🔧 AI Nachrichten MacDailyNews Apple accuses OpenAI of destroying evidence as trade-secrets fight intensifies 🔧 AI Nachrichten The Mac Observer GPT-6 Astra Release Today? OpenAI’s Next Major AI Model Is Almost Here
Laden...
Videos werden geladen ...
Laden...
Beiträge werden geladen ...
Laden...
Videos werden geladen ...
Laden...
Beiträge werden geladen ...
Laden...
Videos werden geladen ...
Laden...
Beiträge werden geladen ...
Laden...
Videos werden geladen ...
🔖 Gespeicherte Artikel
📂
Keine gespeicherten Artikel vorhanden.
📂 News
⏱️ 3 Min
vor 10 Min
Artikeldaten werden geladen...
tsecurity.de AppOffline-Lesen, Eilmeldungen & 0ms Ladezeit
Installiere tsecurity.de direkt auf deinen Home-Bildschirm für das ultimative Vollbild-Magazinerlebnis ohne Browser-Leisten.
Nächster Beitrag
🤖
Community Radar & Live Chat
Sentinel Bot online • Live-Stream
Dein Cluster:
Security Explorer
👥 Match:
lädt…
Verbindung zum Community-Stream wird aufgebaut...
📡 Aktivitäten deiner Analystenlädt…
💡 Neues Thema oder Eilmeldung einreichenReiche interessante Links, Zero-Days oder Debatten ein. Die Community entscheidet per Upvote über die Veröffentlichung. 🔥 Heiß diskutierte Einreichungen |
SOCIAL SHARE CARD GENERATOR