With several companies coming under scrutiny for perpetuating biases due to the generous use of Gen AI, it is often quite challenging to identify some of the biases exhibited by Gen AI. Therefore, it is vital to ensure responsible gen AI use while using the capabilities of AI. As gen AI is being rapidly adopted across industries for daily projects, you need to be aware of the potential biases that the systems could exhibit as an AI product manager or a gen AI developer.
Companies use off the shelf life tools, enterprise solutions, and leverage open models that are tailored for specific requirements. In this case, when a company starts training an AI application on a particular vertical, it is likely to exhibit some biases based on the model that it is trained on. With 73% of organizations planning to use both traditional and gen AI, you need to ensure that all AI systems deployed are free from any biases and do not raise any ethical concerns. Here are some of the potential risks of using gen AI and how to ensure the responsible use of AI.
Risks of Using Gen AI
When it comes to data privacy, gen AI models are notorious for long-term privacy concerns and potential copyright infringement. They could exhibit unintended data exposure with models potentially revealing personal information or copyright material from training data. Gen AI models are designed with a black box nature, where it is challenging for developers to understand how the models make decisions and why certain outputs are produced. Organizations developing gen AI systems are often not practicing transparency as they withhold details about training data, model architectures, and decision making processes.
Generative AI systems are also known to hallucinate or assume false information, thereby making them untrustworthy in addition to being biased on training data. For instance, gen AI models could reinforce harmful stereotypes and discriminate against a certain race, caste, or gender. The models could also be vulnerable in terms of injection attacks, which could cause data leaks and provision dangerous information that could potentially jeopardize the organization. However, despite the risks and biases exhibited by gen AI, there is always a way for AI product managers and developers to use AI responsibly and fruitfully.
Actions to Ensure Responsible AI Use
Now that you have understood the potential biases and limitations of generative AI, here are some actions that you need to incorporate to ensure responsible gen AI use.
As an organizational leader, you need to align leadership, governance policies, and the overall culture to establish accountability and trust. Most importantly, you need to ensure that the gen AI models are trained with multiple data sets so that it does not exhibit biases. Here are some key tips for leaders to address ethical concerns in gen AI and ensure responsible use.
- ● Make sure the organization's leadership understands the importance of using genAI responsibly, creates responsible AI standards, and lets all staff members know that the company is committed to being responsible.
- ● Establish guidelines and related requirements to guarantee that generative AI is used responsibly.
- ● Create a thorough framework for responsible AI governance that outlines important responsibilities, creates organizational frameworks, and promotes a shared accountability culture.
- ● Incentives should be updated to reflect accountability in metrics, product development, and performance.
- ● Provide specialized training to fill in the gaps and encourage the proper usage of genAI.
Responsible use of Gen AI as a Product Manager
As an AI product manager, you need to outline the practical actions to incorporate responsible usage with on-time product development. Here are some common checks that need to be performed to ensure seamless evaluation.
- ● To assess responsibility risks in work use cases and product development, perform "gut checks."
- ● Determine a model for genAI goods by weighing the risks and requirements. By recording the model, optimizing the data, and taking important factors into account, you can guarantee transparency.
- ● Use cross-functional teams, professional supervision, and techniques that are in line with business values and key risks to conduct risk assessments and audits for genAI products.
- ● Use adversarial testing and red-teaming to find vulnerabilities while gradually gathering and reacting to user input.
- ● Keep track of your "micro-moments" of responsibility—small, meaningful acts that reveal responsible decision-making—and highlight them in performance evaluations.
Business Cases for Responsible Use of Gen AI
With the responsible use of generative AI, you can build trust, strengthen brand reputation, and establish regulatory compliance by avoiding costly changes while mitigating risks and driving sustainable growth. Here are some business cases for the responsible use of generative AI.
- ● Assured compliance with regulatory norms and avoidance of costly changes.
- ● Standing out from the competitors and gaining an edge in the industry.
- ● Develop trust and brand reputation: Ethical AI practices increase stakeholder trust, which in turn promotes a favorable brand image and devoted clientele.
- ● Decreased risks and sustainable development: Addressing ethical issues early on reduces risks and can eventually lead to increased value creation.
