How can you make your AI system transparent and explainable?

Defining your goals and understanding the fundamental limitations of any architecture is vital to guiding expectations and architecting systems to meet those goals. Having an intended purpose in mind with what you want to do is wholly inadequate if you select an architecture that isn't capable of it. Explainability and Transparency-oriented goals, as well as Ethical concerns and Legal grounding, have many shallow and trivial proposed solutions. Goals such as these require architectures to be designed and built from scratch with competence and the express intention to meet them. LLMs, RL, and other narrow AI cannot fundamentally achieve these things in any non-trivial sense. Either abandon those as goals or choose another architecture.

LinkedIn here has identified that it can be helpful to understand who your beneficiaries are. From an ethical perspective, also ask yourself the question: Who does this work have the potential to harm?

Defining goals for AI systems is a pivotal step towards ensuring transparency and explainability. Clarity in objectives helps in choosing the right data, model architecture, and performance metrics, making it easier to articulate the system's design and function to stakeholders. Don't treat explainability as an afterthought; set clear benchmarks for it. Depending on the domain, this could range from highlighting influential features to providing a step-by-step logic of the decision-making process. It's key to establishing your system as reliable and trustworthy. Finally, always ask, "Who could this harm?" This ethical lens will help you foresee unintended consequences, shaping a more responsible and transparent AI system.

documentation is really the unsung hero when we talk about making AI transparent and accountable. The power of documentation is that when you refer to your process your instinct will redirect to the previous errors that you faced building the process flow. The most important part of the documentation is the jot down limitations and assumptions in your model. When you have to make tough choices, like trading off accuracy for explainability. People like to know who's behind the wheel, outlining your project teammates It's a simple way to be accountable and give kudos at the same time. Keep a running log or changelog so anyone can trace back the steps if they need to.

Follow a documentation process covering all the following points: Define the AI's objective and operational constraints. Summarize the model's design, chosen algorithms, and essential tools. Document data sources, ensuring ethics and privacy; detail preprocessing and data splits. For training, list hyperparameters, techniques, and metrics. Emphasize feature importance, visualization, and input-output variations. Share validation results, model comparisons, and real-world tests. Address biases, ensure regulatory/GDPR compliance, and use tools like DVC for versioning. Guide users on model use and risks. Regularly update documentation, gather feedback, and consider third-party audits for critical AI systems to ensure transparency and trust.

Thorough documentation is required for the Scientific Method, and can greatly improve the process of iteration. Data needs to be organized so that it can be examined in relation to events, as well as more gradual shifts, and shifts following updates. Avoid baking contaminated data into your documentation schemes, such as the notorious examples of LLMs grading their own performance, now being banned from some peer-reviewed journals. Even when data contamination is noted as limitations and assumptions, mere observation of that data can trigger cognitive biases in the human developers, testers, operators, and auditors. Contaminating human perception of a system can be just as damaging as contaminating the system itself.

Outcomes are usually measured several orders of magnitude more narrowly than they actually occur. Measuring them is itself an iterative process, and every subsequent iteration should incorporate new targets for measurement. These new targets should be evenly split between measuring positive and negative impacts. Splitting outcomes equally and searching for new ways to measure impact may seem counterintuitive, but disclosing negative impacts has strong benefits. A company disclosing its progress on reducing negative impacts calls its competitors who lack such disclosures into question, placing pressure on them. This can strongly benefit society and industries over time. Remain vigilant of Confirmation Bias, and other Biases.

explaining outcomes is an essential part as it would let you showcase your transparency and accountability in AI systems. Providing details about the logic, criteria, and factors influencing outputs allows users and stakeholders to understand the system's decision-making process. It's also important to disclose any uncertainties, risks, and trade-offs that could impact outcomes. AI developers can enhance user confidence, improve system performance through feedback, and meet ethical and potentially legal standards for transparency and accountability.

Stakeholders are often one of the quickest and most cost-effective methods of learning which outcomes need to be measured. They offer a type of Collective Intelligence, where many diverse perspectives can come together to form a more robust and coherent picture of a system's strengths, weaknesses, and impact. Like all potential systems of collective intelligence, this needs to be architected for, otherwise, you risk the value of your stakeholders being reduced to GroupThink. Feedback should be double-blind and highly structured whenever possible, with open-ended opportunities to point out where that structure needs adjustment and expansion. It should be considered at regular intervals, in free flow, and in response to events.

Communication is tantamount in all business relationships, especially those were all stakeholders may not be as savvy as those producing the deliverables. In the case of AI, it is the new buzz… and many don’t really understand HOW it REALLY works. Therefore, clear and concise communication to keep everyone abreast of what is going on and engaged when they may not be experts in the field can keep those relationships strong and your reputation stronger.

Engaging stakeholders is crucial for more than just technicalities; its key responsibility is to build a socially and ethically responsible AI system. From the design phase to evaluation, including stakeholders ensures the system aligns with real-world needs and values. Their feedback can reveal overlooked flaws and help fine-tune the system. This kind of inclusive approach is essential for any AI system aiming for long-term success and social acceptability.

Regularly reviewing your AI system is essential for ensuring it stays transparent, reliable, and aligned with its original goals and current standards. This should include performance checks, quality assessments, and impact evaluations. Always update the system based on new data, techniques, or societal expectations, and communicate these changes to stakeholders. Consistent reviews make your system robust, responsible, and responsive to evolving conditions. It's the best way to catch issues early and adapt to changing demands.