How can you ensure AI transparency?

In the early stages of AI's integration into society, the primary focus is often on its revolutionary aspects. As AI continues to evolve, there's growing concern about potential obsolescence of certain jobs. Therefore, it's vital to establish clear goals & values before progressing further. This is particularly important for various professional groups who need to adapt AI in line with their professional duties. At this nascent stage of AI's societal integration, it's essential to acknowledge the uncertainties surrounding its impact. These uncertainties should be understood in context of the agreed-upon goals & values. When introducing system technology like AI, it's important to ensure the involvement of diverse groups in the process.

Transparency in AI demands a multi-faceted approach. Use interpretable algorithms, provide clear documentation, and maintain a record of data sources and model versions. Regularly audit models for bias and fairness, and consider publishing AI ethics guidelines. Encourage transparency as a cultural value within your organization.

In my experience the transparency of a model needs can have three audiences. These audiences are (1) other models, (2) software that will embedded the model, and (3) software that will expose the model directly to a user/consumer. Each audience will need a different level of detail of the attributes, raising, and data used to grow/train the model. While other models (audience type 1) might need a ton of information, end users/consumer (audience type 3) might only need something that is simple and easy/quick to understand. For example, a user/consumer might just need the equivalent of a FICO model to evaluate if the model is coexistent with preserving human values in the long term or not.

This is particularly important process when it comes selecting what method to apply for specific data architecture for use cases involving the deployment & use of ML/DL systems that operate as black boxes & are hardly understandable by any business/government sectors stakeholders. Therefore, a key requirement for ML/DL applications in any business/government sector is to be able to explain their outcomes. There should be also a multidisciplinary & provide a collaborative approach across several disciplines that are critical for interdisciplinary thinking when deploying ML/DL systems. This foster AI design team to think about aspects of informed consent, privacy, transparency, bias, legal issues even before the AI model development process.

The choice of your "model supply chain", said differently the tapestry of models that you leverage and include in your architecture is paradoxical. The paradox is not that different than the paradox of open-source software. One the one hand, open models are, well open. On the other hand, models that are closed (black boxes) tend to out perform open source models, and are better document, and supported. There is a new breed of companies that are keeping the model architecture black box, but starting to share information about the attributes, training, tuning, and biases of the model. So the ingredients of the model are shared, but not the way the sausage was made. These are not trivial choices to make.

At the heart of this process is understanding that every AI system, no matter how sophisticated, is only as effective as the data it's trained on. Regular assessment of the quality and relevance of this data is essential. An AI model can "drift" over time. it begins to deviate from its initial accuracy because the environment in which it operates changes. Regular evaluations can detect such drifts early on, allowing for recalibration. While AI might be a technological tool, its outcomes have human implications. Every evaluation should not just be quantitative but also qualitative. Feedback from human users gives invaluable insights into how the system is perceived, its utility, and areas where it might be causing friction.

As AI system moves from research labs to everyday applications, the need for system feedback shifts from a controlled environment to real-world scenarios. While AI systems undergo rigorous testing with curated data sets in labs, the monitoring and feedback mechanisms in real-world applications often lack the same level of thoroughness. This makes effective feedback essential for AI systems to function properly and safeguard civic values. As AI algorithms increasingly impact citizens and their legal standing, it becomes imperative to actively process feedback about these implications. Therefore, it’s essential to enforce obligatory feedback procedures in all business/government AI applications to maintain transparency in AI operations.

Ensuring transparency in AI is crucial, and this can be achieved by empowering all relevant stakeholders. Publicly sharing information about AI usage is one way to accomplish this. Incorporating AI literacy into funding policies and training programs is another effective strategy. It’s also beneficial to foster collaboration among various interest groups and similar organizations within the digital sphere. Involving a diverse range of stakeholders in the technology development process is key to ensuring that their interests are taken into account. It’s equally important to keep these stakeholders informed about the different ways they can participate in decision-making related to AI usage, thereby enhancing its transparency.

It's important to understand the diverse roles that make up the AI journey: Frontline Employees: The end-users. Regular feedback sessions can unearth practical challenges and areas for improvement. Middle Management: They bridge the strategy-execution gap. Ensure alignment with the AI vision to drive acceptance and pinpoint bottlenecks. C-Suite Executives: Essential for financial support and championing AI. Keep them updated about progress and ROI to boost adoption rates. Every stakeholder offers a unique perspective; proactive engagement harnesses a comprehensive view. Use a tool like SilkFlo for end-to-end strategic oversight.

The transparency of AI is ensured by the establishment of ethical guidelines for AI. These guidelines can act as a compass during the development of AI systems. In 2019, the European Commission, tasked with the responsibility of shaping Europe’s digital future, published a significant report titled “Ethics Guidelines for Trustworthy AI”. According to this report, Trustworthy AI should be: 1)Lawful - complying with all applicable laws & regulations 2)Ethical - upholding ethical principles & values 3)Robust - technically proficient while considering its social context. The implementation of AI ethics guidelines will guarantee adherence to best practices in preserving trustworthy AI across various entities that incorporate & use AI systems.

In the ever-evolving world of AI, there's a growing need for a standardized way to evaluate AI models' alignment with Human-AI Coexistence (HAICO). An illustrative concept is the HAICO score, inspired by the FICO score used for financial trustworthiness. It's a multidimensional framework that assesses AI models' attributes, such as fairness, robustness, and transparency, and distills them into a user-friendly score. This score can help the general public swiftly judge whether an AI model is trustworthy and aligned with human needs. The HAICO score's potential lies in bridging the gap between complex technical details and public understanding, fostering trust and cooperative coexistence in the age of AI.

Ethical AI is also a critical component of AI development and deployment, becoming increasingly significant. Considering the ethical implications of AI systems is not only a moral obligation but also a strategic imperative. Transparency, accountability, and fairness are essential principles. This list can be further enriched with privacy, beneficence, non-maleficence, and autonomy. These principles contribute to the creation of more resilient, reliable, and enduring AI applications while reducing various risks. As AI's presence in our society expands, the significance of ethical considerations will only intensify.