1 | What is #MarchResponsibly?
March is known for International Women’s Day – but did you know that women are one of the under-represented demographics when it comes to artificial intelligence prediction or data for machine learning? And did you know that Responsible AI is a key tool to ensure that the AI solutions of the future are built in a safe, trustworthy, and ethical manner that is representative of all demographics? J As we celebrate Women’s History Month, we will take this opportunity to share technical resources, Cloud Skills Challenges, and learning opportunities to build AI systems that behave more responsibly. Let’s #MarchResponsibly together.
2 | What is Responsible AI?
Responsible AI principles are essential principles that guide organizations and AI developers to build AI systems that are less harmful and more trustworthy.
Reference the Responsible AI Standards
Fairness issues occur when the AI system favors one group of people vs another, even when they share similar characteristics. Inclusiveness is another area that we need to examine whether the AI system is intentionally or unintentionally excluding certain demographics. Reliability and Safety are another area that we must make sure to consider outliers and all the possible things that could go wrong. Otherwise, it can lead to negative consequences when AI has abnormal behavior. Accountability is the notion that people who design and deploy AI systems must be accountable for how their systems operate. We recently saw this example in the news where the U.S. Congress summoned social media tech leads to hearing how their social media algorithms are influencing teenagers to lose their lives and inflict self-harm. At the end of the day, who compensated the victims or their families for the loss or grief? Transparency is particularly important for AI developers to find out why AI models are making mistakes or not meeting regulatory requirements. Finally, security and privacy are an evolving concern. When an AI system exposes or accesses unauthorized confidential information this is a privacy violation.
3 | Why is Responsible AI Important?
Artificial Intelligence is at the center of many conversations. On a daily basis we are seeing increasing news headlines on the positive and negative impact of AI. As a result, we are seeing unprecedented scrutiny for governments to regulate AI and governments acting as a response. The trend has moved from building traditional machine learning models to Large Language models (LLMs). However, the AI issues remain the same. At the heart of everything is data. The underlining data collected is based on human behavior and content we create, which often includes biases, stereotypes, or lack of adequate information. In addition, data imbalance where there is an over or under representation of certain demographics is often a blind spot that leads to bias favoring one group verse another. Lastly, there are other data risks that could have undesirable AI effects such as using unauthorized or unreliable data. This can cause infringement and privacy lawsuits. Using data that is not credible will yield erroneous AI outcomes; or back decision-making based on AI predictions. As a business, not only is your AI system untrustworthy, but this can ruin your reputation. Other societal harms AI systems can inflict are physical or psychological injury, and threats to human rights.
3 | Empowering Responsible AI Practices
Having practical responsible AI tools for organizations and AI practitioners is essential to reducing negative impacts of AI system. For instance, debugging and identifying AI performance metrics are usually numeric value. Human-centric tools to analyze AI models are beneficial in revealing what societal factors impact erroneous outputs and prediction. To illustrate, the Responsible AI dashboard tools empowers data scientists and AI developers to discovers areas where there are issues:
Addressing responsible AI with Generative AI applications is another area where we often see undesirable AI outcomes. Understanding prompt engineering techniques and being able to detect offensive text or image, as well as adversarial attacks, such as jailbreaks are valuable to prevent harm.
Having resources to build and evaluate LLM applications in a fast and efficient manner is a challenge that is much needed. We'll be sharing awesome services organizations and AI engineers can adopt to their machine learning lifecycle implement, evaluate and deploy AI applications responsibly.
4 | How can we integrate Responsible AI into our processes?
Data scientists, AI developers and organizations understand the importance of responsible AI, however the challenges they face are finding the right tools to help them identify, debug, and mitigate erroneous behavior from AI models.
Researchers, organizations, open-source community, and Microsoft have been instrumental in developing tools and services to empower AI developers. Traditional machine learning model performance metrics are based on aggregate calculations, which are not sufficient in pinpointing AI issues that are human-centric. In this #MarchResponsibly initiative you will gain knowledge on:
- Identifying and diagnosing where your AI model is producing error.
- Exploring data distribution
- Conducting fairness assessments
- Understanding what influences or drives your model’s behavior.
- Preventing jailbreaks and data breach
- Mitigating AI harms.
4 | How can you #MarchResponsibly?
- Join in the learning and communications – each week we will share our Responsible AI learnings!
- Share, Like or comment.
- Celebrate Women making an impact in responsible AI.
- Check out the Azure Responsible AI workshop.
- Check out the Responsible AI Study Guide to Microsoft’s FREE learning resources.
- Get a series of Blogs and Tutorials on Azure Responsible AI services and features.
- Stay tuned to our #MarchResponsibly hashtag more resources.
5 | Share the Message!
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