Establishing Standards for Responsible Generative AI
With the rapid advance of AI across industries, responsible AI has become a hot topic for decision-makers and data scientists alike. But with the advent of easy-to-access generative AI, it’s now more important than ever. There are several reasons why responsible AI is critical as the technology continues to advance.
Some of these include concerns about bias/discrimination, data privacy and protection, safety, and of course transparency and accountability. So let’s dig a bit deeper and look at some reasons why the principles of responsible AI are a critical factor when it comes to AI, and what some tech leaders are doing.
Microsoft’s Responsible Generative AI Commitment
Microsoft is hoping to display its commitment to responsible generative AI by going public with its six principles for responsible generative AI. According to the company, this includes fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. In creating these principles, Microsoft has operationalized its commitment through governance, policy, and research.
Some specific examples of Microsoft’s responsible generative AI initiatives include the Human-AI Experience (HAX) Workbook, the AI Fairness Checklist, and the Responsible AI Dashboard. Microsoft is also collaborating with organizations like UNESCO to promote responsible generative AI.
Design for Responsibility
One of the most important things that can be done to promote responsible generative AI is to design AI systems with responsibility in mind. This means thinking about the potential risks and challenges of AI systems from the start, not to be an afterthought once a system has come online. Once the decision to move forward with an AI system has been made, responsible generative AI should become a cornerstone of any design.
Of course, this also means designing AI systems in a way that is fair, reliable, safe, inclusive, transparent, and accountable.
Conduct adversarial testing
This aspect often doesn’t get talked about enough, and that is using adversarial training and testing to promote responsibility in AI. The way adversarial testing works is by testing or redlining using prompts and other methods to find weaknesses in AI systems. For example, this can include attempts to jailbreak an AI system by using a series of prompt chains in an attempt to force an unwanted response.
These responses can range from something as simple as getting factual information wrong to legitimate safety concerns due to the content the AI generates. So by conducting adversarial testing, internal teams are able to identify and fix potential vulnerabilities in AI systems before they can be exploited. These teams often are not solely made up of data science professionals to utilize a range of skills that are more likely to find any security issues.
Be careful with communication
Believe it or not, communication is key to promoting responsible generative AI. That’s because when communicating about AI, it is important to be clear, concise, and accessible to a wide audience, especially non-technical stakeholders. The reason for this is pretty easy to understand. All one has to do is look toward ChatGPT’s rapid adoption by those outside of tech since last year. ChatGPT showed that society was ready to use AI now, not later.
This is why clear is important. It helps to build trust and transparency with audiences who may not possess certain technical expertise. It also helps to mitigate bias and discrimination and ensure that AI systems are aligned with human values.
Monitor for bias
Bias has a clear negative undertone for very good reason. No one wants AI to be biased against any group, as this helps to reduce trust and increases other risks. This is why it’s important to monitor AI systems for bias by ensuring data sets are clean and other methods. But the truth is, that bias can creep into AI systems in many ways, so it’s important to be vigilant. There are a number of ways to monitor for bias, including:
- Analyzing the data that is used to train AI systems
- Evaluating the outputs of AI systems
- Conducting user studies
Using high-quality datasets
As we’ve seen over the last year, web scraping has done a tremendous job of creating unique and functional LLMs. As complexity grows, it is clear that the quality of the datasets used to train models will become more important. This is one reason why synthetic data has become more popular and it will likely grow in popularity in the coming years. However one doesn’t only need to rely on synthetic data.
This is where data scientists and other data professionals come in. They ensure that the quality of all data sets is maintained as the risks associated with a poor quality set can ruin hundreds of not thousands of hours of work by teams within a company.
Conclusion on Responsible Generative AI
Responsible generative AI is helping to ensure that the technology stays fair, transparent, and accessible to the greatest amount of people possible while minimizing any harm. Now if you’re interested in this topic, Sarah Bird, PhD, Global Lead for Responsible AI Engineering at Microsoft will be speaking at ODSC West in a few short weeks.
So don’t miss out, and see for yourself what’s on the horizon for AI. Register now while tickets are still 30% off!
You can also check out our Generative AI track and see how you can use GenAI yourself! Some session titles include:
- Aligning Open-source LLMs Using Reinforcement Learning from Feedback
- Generative AI, Autonomous AI Agents, and AGI — How new Advancements in AI will Improve the Products we Build
- Implementing Gen AI in Practice
- Scope of LLMs and GPT Models in Security Domain
- Prompt Optimization with GPT-4 and Langchain
- Building Generative AI Applications: An LLM Case Study
- Graphs: The Next Frontier of GenAI Explainability
- Stable Diffusion: A New Frontier for Text-to-Image Paradigm
- Generative AI, Autonomous Agents, and Neural Techniques: Pioneering the Next Era of Games, Simulations, and the Metaverse
- Generative AI in Enterprises: Unleashing Potential and Navigating Challenges
- The AI Paradigm Shift: Under the Hood of a Large Language Models
- Deploying Trustworthy Generative AI
Originally posted on OpenDataScience.com
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