• Proactive guidelines can help secure the future of AI

• Comprehensibility & interpretability are the basis for Responsible AI use cases

• Zühlke's Responsible AI framework defines the process

Adopting safe, ethical, and sustainable practices around AI isn’t just a moral imperative. It’s set to be a global mandate with the onset of AI regulation. Here we explore everything your organisation needs to know about the future of responsible AI (artificial intelligence). 

For many, AI is both a powerful enabler and a Pandora’s box.

It’s already capable of incredibly creative and algorithmic thinking. But ever-increasing smarts alone won’t make it ready to police itself. Instead, human hands need to help shape AI into an ethical, trustworthy tool – one that can unlock new business value without landing anyone in hot water. 

Managing that process relies on understanding a few core concepts around the potential – and potential pitfalls – that this burgeoning technology is in the midst of opening up. But really, it all boils down to three things: the what, the why, and the how behind responsible thinking. 

First up, the big question: what does the word ‘responsible’ even mean when it comes to AI? 

What is responsible AI? 

Responsibility and ethics aren’t exactly the same thing, but they do have common roots. When it comes to artificial intelligence – and technology in general – they both mean operating openly, and from the seed of good intentions.  

AI capabilities are changing rapidly. So while it’s impossible to set rules in place that predict every use case, what you can – and should – do is be willing to show people what’s going on behind the curtain. 

It's very hard to have ethical AI if you don't know what the AI is doing. That's why you need a framework for responsible AI that includes explainability and interpretability. 

‘Most companies have good intentions with AI. But if a model lacks thorough validation, it's easy to end up with unintended consequences – with potentially serious ramifications for the end user’. 

Explainability here means being able to show your workings: what kind of datasets and sources go into an AI system to make it produce results? It’s also about understanding which input data had the biggest influence on the output – to understand why a model made a certain decision. This can either be achieved using an inherently explainable model, or applying explainability tools on top of the original model.   

There are different ways of defining Interpretability . One is linked to understanding how a decision is being made by an AI model. But it can also be about how you help the end user make sense of the result. 

But a true framework for AI responsibility goes even deeper than just that transparency layer. It can also foster an ‘AI for good’ mindset and processes that unlock better outcomes – for people, planet, and profit. 

So that’s explainability, interpretability, sustainability, and equality – all wrapped up under the banner of responsibility. But why? What makes formalising this approach such a necessity? 

Why is responsible AI so important? 

The short answer here is to break the why into two areas:  

1. Mitigating unintended consequences 

Most companies have good intentions in mind with AI. But if a model lacks thorough validation, and careful risk analysis and mitigation, it's easy to end up with unintended consequences. This can have serious ramifications to the end user. That’s why it’s essential to invest time and resources in understanding all the possible risks a product opens up. 

Take the Instagram algorithm, for example, which served weight loss messages to teenagers, or the Apple Credit Card vetting system, which unwittingly discriminated against women.  

With the latter example, even removing gender from the data pool wasn’t enough of a safeguard. That’s because, if you just remove gender from the AI model’s training, these models can still detect gender in other behaviours like shopping habits, and make biased judgements based on that.  

That’s obviously very problematic, but it’s a great example of why you need explainable AI – so you can understand why the technology is making its decisions. 

2. Adapting to regulatory changes 

Another part of the responsibility pie is rapidly approaching in the form of sweeping legislative changes and AI regulation.

The recently tabled EU AI Act seeks to codify requirements around how AI can be deployed in consumer-facing products – with a focus on data openness and banning practices it sees as posing ‘unacceptable risk’, like AI-powered social scoring and behavioural manipulation. 

Crucially, the AI Act has the ability to set the tone globally – much like GDPR before it. And that's a good thing. Up until now the AI space has been a bit of a Wild West. 

‘The AI Act seeks to codify responsible AI requirements. Until now, the AI space has been a bit of a Wild West’. 

Failure to comply with regulatory changes will obviously have legal ramifications for any business doing business with the EU, but there is also a third, more cutthroat incentive than even that: avoiding monetary losses. 

Even outside the concept of responsibility, a badly developed AI can have quite devastating consequences if it presents errors or makes decisions that result in reputational damages. As any business knows: if you do things wrong, it can lead to costly lawsuits. 

So how do you codify and put responsible AI thinking into practice? The answer lies in planning ahead, rather than racing forwards and trying to course-correct when it’s already too late…

Ensure your framework fits within your existing workflows

At every layer, it's all about putting the right processes in place. 

The good news is that this doesn’t need to be transformative or prohibitive. In fact, our responsible AI framework should slot right alongside any modern business’ ethical data workflows.  

These checklists and processes should map against the way you're working already. Nobody wants to adopt a framework that’s just a bunch of hurdles and complications.  

So these processes need to be implemented in a way that facilitates high-quality outputs and actually make it easier for the data engineers to work.  

Because, otherwise, being responsible with AI is just something extra that needs to be done, rather than a mindset shift that enables smarter, more futureproof projects from the outset. 

Speak to us today about how our ISO-accredited strategists, scientists, and engineers can help you create new value at scale with the right data strategy and responsible AI solutions.