How do you align AI with your goals?

Let's start defining which goal we are talking about. If it is a 'technical' goal such as discarding defected products and AI is a proper tool for the problem, then AI is aligned with my goal. Well-defined goals help us to find the right tool for them. However, if it is a 'social' goals such as ethics, company values or mission why should we be worried about AI being aligned with them? Well, the most important aspect in my opinion is well-defining the limitations and boundaries of the model, especially the generative ones, because it is an extrapolation of reality with all risks linked to its use. Secondly, the risk related to the potential autonomy in decision-making of any device powered by AI has to be considered. Who is in command?

To harness AI's full potential, commence by crystallising your objectives. Understand what you aim to achieve through AI, outlining concrete outcomes and the resultant implications. Equipped with distinct objectives, you can select or tailor an AI solution aligning with your requisites. Moreover, lucid goals foster coherent communication, ensuring stakeholders and collaborators share a unified understanding and vision of the AI endeavour, promoting cohesive efforts towards desired results.

We can further enhance this by asking the AI to repeat the instructions it received in their own words. This will prompt it to paraphrase the problem statement, and it will become clear if the understanding is correct or not.

Sure data. It is all about data. AI is a data driven model. And you need a huge amount of reliable data. But whenever you manage data, you may found yourself in a delicate situation regarding ethics and transparency. Especially if the data is sensitive. But it is not AI-related. It is just that with AI the problem scales up. And, being AI a black box, it is not transparent by definition.

To aptly harness AI, a deep comprehension of your data is paramount. AI thrives on data, with its quality and volume directly influencing AI accuracy and efficacy. It's crucial to discern data sources, its collection, processing, and storage methodologies, and recognise potential data biases or discrepancies. Furthermore, upholding data privacy and security, while adhering to pertinent legislations, is essential. A robust understanding of your data ensures the optimal selection or development of AI methodologies, ensuring they are adept for your unique data landscape.

Of course we should never share confidential data or anything that includes PII (personally identifiable information) with an AI system. Before sharing anything with the AI, it is a good practice to enter it into a note taking app and censor all PII.

To effectively align AI with your objectives, selecting the appropriate AI approach is crucial. The vast AI spectrum, encompassing machine learning, natural language processing, computer vision, and deep learning, offers varied solutions for diverse challenges. It's vital to grasp the advantages and constraints of each approach, factoring in its compatibility with your data and goals. Moreover, weigh the trade-offs concerning complexity, rapidity, expense, and scalability. Opting for the right AI method ensures the creation or adoption of an AI solution that maximises outcomes and efficiency.

Any model needs to be validated before deploy it. This phase helps you to define limitations and boundaries. Limitations and boundaries are necessary for a safe and proper use of the tool. The final user feedback is if paramount importance as for any service.

The penultimate step in harmonising AI with your objectives is assessing the AI's results. It's paramount to consistently oversee and gauge the performance of your AI solution to ascertain if it aligns with your aspirations. Utilise pertinent metrics like accuracy, precision, recall, or the F1-score to quantify AI results. Additionally, employ qualitative measures, such as user feedback and surveys, to capture the user experience. This periodic evaluation not only highlights areas of improvement but also facilitates refinements, ensuring your AI solution continually offers optimal quality and value.

The fifth step in synchronising AI with your objectives is to prioritise the ethical dimensions of your AI's impact. Delve into the broader ethical, societal, and ecological repercussions of your AI system. Adhering to the foundational tenets of ethical AI - transparency, fairness, accountability, and explainability - is vital. This ensures that your AI not only functions optimally but does so with integrity, responsibility, and trust. Additionally, proactively address and ameliorate potential pitfalls like bias, discrimination, or data breaches. This proactive ethical stance ensures that your AI system contributes positively while minimising adverse impacts.

The sixth phase in harmonising AI with your objectives is to continuously evolve alongside AI. Recognising the myriad of learning avenues and potential growth AI brings is paramount. It's essential to perpetually refresh your expertise and stay abreast with AI advancements. This entails delving deeper into the ever-evolving AI field, attending workshops, and participating in AI communities. Engaging with other AI aficionados, thought leaders, and forums enriches your perspective and broadens your understanding. By dedicating oneself to lifelong learning in the AI realm, you ensure that your AI initiatives not only meet but often surpass your set objectives.

AI is not always the answer. Some of the problems you define, could be resolved way easier without using AI. For example by tapping into data and analytics.