The Evolution of Machine Learning from Theoretical to Possible to Ethical

By the end of 2018, Forrester predicted that 70% of enterprises (paywall) would implement artificial intelligence (AI). This is up from 40% in 2016 and 51% in 2017. There’s one thing that these statistics make crystal clear – AI and Machine Learning are here to stay.  Amidst all the excitement about what is possible, the beginning of this new calendar year is an opportunity to pause for a moment and reflect on the current status of ML.  2018 established that it’s no longer a question of whether AI will take off, but rather which companies will keep up. 

From conversational AI to ‘deep fake’ technologies and even robots doing parkour, the impact of ML is being felt everywhere.

Mercanto is at the vanguard of this massive wave as we pursue our vision to use ML to hyper-personalise customer experience and improve marketing productivity and performance based on a foundation of ethical Machine Learning (ML).

In 2018, and as expected, the application of ML took off across the enterprise, including processes as diverse as changing the way social media is used, enhanced fraud prevention abilities, and constant customer support —to name but a few.

Our focus on ML personalisation enables enterprises to realise measurable business value by helping improve not just the performance of the email channel, but also the productivity of marketers operating those processes. A recent survey from McKinsey & Co. suggests that AI’s most significant effect on the workforce could be changes in the work that people do—particularly ever-greater collaboration between machines and people—rather than overall workforce reductions. We wholeheartedly agree. And while in 2018 discussions started with where do you start? Andhow do you bring ML as a strategic capability into customer communications? In 2019, executives are now moving from ‘how CAN we implement ML in our customer communications?’ to ‘how SHOULD we implement ML in our customer communications?’

The time for Ethical ML is now

While marketers have started to operationalise ML within their organisation, they realise that operationalising without governance is a recipe for disaster. There have been some shocking cases of unintended consequences from automated ML systems going rogue. As ML becomes more prevalent in our everyday lives, questions around ethics, fairness, and responsibility of ML systems have started to move to the fore. It’s crucial for businesses to deploy systems they trust, while also reflecting core company values and legal requirements – that’s why the time for Ethical ML is now.

Most of the AI disasters today come from the tech giants because they are the most sophisticated when it comes to machine learning and artificial intelligence. However, we are at the beginning of an ML revolution, and as more companies adopt machine learning, we will see many more missteps. Businesses and society as a whole must engage in a discussion about ethics in AI — about when it is appropriate to treat different types of people differently using advanced machine learning. Marketing pros should help lead this discussion at their firms. Firms that tackle this proactively will be better equipped to manage these issues than those that wait to react.

Ethics before technology

Ethics must come before technology, not the other way around. That’s why we’ve announced a new set of guiding principles the company will be using as a code of ethics to abide by when developing future ML.

Machine Learning Guiding Principles
ML Guiding Principles

These principles provide an intentional framework for building and using Mercanto’s ML systems. These principles state that Mercanto is committed to delivering an ML platform that will be:

  • Human-centric
  • Fair & unbiased
  • Safe & secure
  • Privacy-focused
  • Transparent & explainable

The Mercanto team embraces these principles: they are not theoretical concepts but real standards that will actively govern our product development and will impact our business decisions. Of course, we’re far from alone in thinking about these topics, and we’d like to express our thanks and gratitude to The Future of Life and the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems for their inspirational work on AI ethics and whose work helped inform these principles.

At its core, Ethical ML is about establishing trust and confidence. If everyone takes a people and ethics first approach to embracing ML frameworks in the coming years, they will be able to unlock its full value and benefit consumers and organisations alike.

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