What do you do if your clients are unaware of the potential risks and limitations of AI technologies?

Well, the best thing to do would be to inform them clearly about this. It is a critical step in encouraging responsible AI usage. Clients need to know what they can do and what they can't do with AI. They also need to know the need for regularly monitoring and checking the outputs of AI systems.

Unfortunately, they are probably blinded by the bright lights of AI evangelists on stage declaring how close we are to AI becoming sentient, showing off well orchestrated demos intended to make AI solutions appear more powerful than they actually are, or constantly professing that AI is the miracle cure to all problems. It starts with a basic understanding of how the technology works. Run through the construct of a large language model, the pros and cons of using private versus open-source models, the best use cases and how to make the most of their data. As part of this, introduce them to logical fallacies in AI, where LLMs will necessarily struggle and highlight what is wrong with some of the demos and media reports we are seeing.

If clients are unaware of AI technology's potential risks and limitations, take the time to educate them about the technology and its limitations. Conduct a risk assessment and develop a risk management plan to address any identified risks. Provide transparent communication about the technology's limitations, involve stakeholders, and use explainable AI techniques to build trust. Continuously monitor and improve the technology to ensure responsible and ethical implementation.

Launch an "AI 101" series tailored for non-technical clients, demystifying AI through real-world scenarios and stories. Replace abstract concepts with relatable examples, such as comparing AI's learning process to the way a chef refines recipes over time. This personal approach can bridge the knowledge gap, fostering a deeper understanding and respect for AI's capabilities and limitations.

1. Clarify AI is powerful yet not infallible. 2. Explain AI's dependency on data quality. 3. Highlight the issue of biased or flawed data. 4. Emphasize the lack of AI's human judgment. 5. Inform that AI can't make moral decisions. 6. Present AI as a tool needing oversight. 7. Stress the importance of human supervision. 8. Educate on AI's limitations and potential risks. 9. Advocate for responsible AI use. 10. Prepare clients for thoughtful AI integration.

Nós enfatizamos a importancia de estabelecer expectativas realistas com nossos clientes sobre a implementa??o de IA, explicando o conceito de "lixo entra, lixo sai" para destacar a crucialidade da qualidade dos dados. Ressaltamos que, para que a IA seja eficaz, s?o necessários dados de alta qualidade, manuten??o e ajustes contínuos. Nosso objetivo é ajudar os clientes a entender que, embora a IA possa oferecer melhorias significativas aos processos de negócios, ela n?o é uma solu??o instantanea ou mágica, mas sim uma ferramenta que requer cuidado e aten??o contínua para funcionar adequadamente.

Develop an AI Expectation Meter, a simple visual tool to help clients gauge realistic outcomes from AI projects. This dynamic scale could show ranges from “Experimental” to “Proven” applications, accompanied by case studies. It transforms abstract capabilities into tangible expectations, helping clients visualize where their projects stand and what they can realistically achieve.

Establishing realistic expectations is foundational for the successful integration of AI. This means conveying to clients that the outcomes of AI are heavily dependent on the quality of input data. The emphasis on ongoing maintenance and the iterative nature of AI system tuning is to prepare clients for a journey rather than a one-time setup. This perspective seeks to balance optimism with realism, ensuring clients are both excited about AI’s potential and aware of the commitment required to achieve success.

Why: Unrealistic expectations lead to dissatisfaction. How: Openly discuss AI’s capabilities and boundaries. What: Address accuracy, response time, and context limitations. Example: Clarify that AI isn’t infallible and may not handle all scenarios perfectly

To manage expectations, I rely on OKR right from the start. In this way, I actively involve the customer in the target design. Progress can be measured using KPIs and the success achieved can be easily measured. In this way, the customer also understands that the use of AI must be continuously developed in line with the company's objectives.

With the emergence of laws like the EU AI Act, ethics are not optional nor extra credit but are a core foundation to how your client tests, executes, and maintains AI-enabled applications across the enterprise. Establish a common ground in AI literacy so the business and IT teams understand the boundaries and requirements of AI solutions. They can have an intelligent exchange of pros and cons as well as where the guardrails need to be. Ensure observability, ongoing governance, and an LLM abstraction layer, so if one LLM strays from the mark, the application has the ability to swap it out or optimize across many.

The ethical discourse around AI is not just about compliance but about fostering a culture of responsibility. Encouraging clients to engage with the ethical implications of AI use is to advocate for a future where technology serves humanity ethically and justly. This includes a deep dive into how AI systems can impact societal norms around privacy, employment, and bias. The goal is to instill a sense of ethical stewardship in clients, guiding them to consider how their use of AI aligns with broader societal values.

Purpose: Emphasize data protection and system security. How: Talk about data privacy, encryption, and secure practices. Raise awareness of cyber threats. Ensure clients understand the risks associated with data breaches and unauthorized access. Collaborate with security experts to implement robust safeguards.

Initiate an “Ethics First” AI policy with your clients, incorporating ethical checklists and impact assessments into the AI development lifecycle. Host regular forums where clients and developers can discuss ethical dilemmas and case studies. This proactive approach encourages a culture of ethical awareness and decision-making, aligning AI projects with broader societal values.

Purpose: Prepare for unexpected AI outcomes. How: Acknowledge that AI systems can fail or produce incorrect results. Discuss fallback strategies, error handling, and contingency plans. Encourage clients to consider scenarios where AI predictions are incorrect. Mitigate risks by being proactive rather than reactive.

Security in the context of AI is about safeguarding not just data but the integrity of decision-making processes. By highlighting the various security threats unique to AI, such as data poisoning and adversarial attacks, the focus is on proactively addressing vulnerabilities. This perspective underlines the importance of a comprehensive security strategy that evolves in tandem with AI advancements, ensuring clients’ AI systems are resilient against emerging threats.

Offer monthly security audits and workshops focusing on the unique vulnerabilities of AI systems. Create a shared platform for clients to report and learn from security incidents, turning each challenge into a learning opportunity. By fostering a community of shared knowledge, clients become more resilient against threats and better prepared to protect their AI assets.

Switching to using only AI for decisions isn't smart for many situations. It's better to also have a way where people do tasks by hand. This way, if the AI doesn't work right, we can still count on the human way of doing things.

Introduce an AI Resilience Toolkit, complete with checklists, templates, and response plans, tailored to different levels of AI failure. Host simulation workshops to walk clients through potential failure scenarios, enhancing their preparedness and response strategies. This practical preparation can transform anxiety about failure into confidence in risk management.

Purpose: Build trust and manage expectations. How: Be transparent about AI processes, algorithms, and decision-making. Share insights into model training, data sources, and any limitations. Transparency fosters confidence and helps clients understand the rationale behind AI recommendations. Regularly update clients on system performance and any changes.

Discussing the inevitability of failure in AI systems serves to normalize setbacks as part of the AI lifecycle. This conversation about contingency planning is crucial for building resilient systems that can withstand or quickly recover from disruptions. The emphasis here is on adaptability and the importance of human oversight, reinforcing the idea that AI, while powerful, benefits from continuous human engagement to stay aligned with changing objectives and conditions.

Implement a transparent AI reporting system, offering clients real-time insights into their AI's decision-making processes, performance metrics, and training data sources. Establish monthly transparency reviews, where clients can ask questions and provide feedback, fostering a partnership built on trust and open communication.

Transparent reporting and open communication on data sharing and data privacy are critical for maintaining awareness. For example, my org ensures every conversation will have a clear alert on what data gets shared with LLMs or used for model training to keep customers aware of what’s happening behind the scenes.

Transparency is the cornerstone of trust in the client-provider relationship. Keeping clients informed about the evolving capabilities and limitations of AI technologies fosters a collaborative partnership. This involves open communication about the challenges and successes, encouraging clients to remain actively engaged and informed about the AI field. The aim is to cultivate a shared understanding and mutual respect for the complexities and potentials of AI.

Exploring the broader context of AI, beyond immediate practical considerations. Sharing success stories, emerging trends, and speculative futures invites clients to think of AI not just as a tool for solving today's problems but as a dynamic field with the potential to reshape industries. This perspective encourages a forward-thinking mindset, inviting clients to consider their role in shaping the future of AI within their industry and society at large.

Most clients today are aware of the looming issues with AI w.r.t security, privacy among others. However, for the uninitiated, and as a Best practice, I follow a step by step process which are workshops of both the happy path ( AI success stories) and SAD path ( Security, Access and Deployment) as AI caveats. This is setting the context before Feasibility kicks in with data quantity, quality and ownership is evaluated. After this the Scope of Work is mutually evolved to define success criterion and then handover Features Specifications Document as a sign off between all stakeholders. These Guidelines, checklists and templates should highlight ethics at the core.The bottom line here is that Responsible Ai is everyone's Responsibility ??