What’s Responsible Machine Learning—and Why Should You Care?

The shift toward more responsible use of machine learning is becoming more evident and widespread. But what does it mean?
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When Twitter introduced its Responsible Machine Learning Initiative back in April, it was hardly a surprise. Other huge companies (such as Google and Microsoft) had already taken their own steps toward a more ethical, compliant, secure, and human-centered artificial intelligence. And, if you wait some time, you’ll quickly see how other companies will follow suit. 

It makes sense, especially when ML and AI are implemented across multiple products we use every day. Without responsible development and use of those technologies, people could be affected in a variety of ways, from harmless things like a limited user experience to more serious matters like suffering from discrimination. 

Fortunately, this shift toward more responsible use of machine learning is becoming more evident and widespread. That’s why it’s important to truly understand what it means and why we all need to embrace it, discussing responsible machine learning and even demanding it from ML engineers, development teams, Python development services, freelance developers, startups, big companies, and any other actor that plays a role in machine learning development. 

What’s Responsible Machine Learning?

There isn’t a single definition of responsible machine learning. That’s because different people and organizations have different views on the limits of that responsibility. Thus, for instance, Twitter’s Responsible Machine Learning Initiative states that such responsibility includes taking care of its ML algorithm’s decisions, ensuring equity and fairness in its outcomes, guaranteeing transparency about all ML-related decisions, and enabling agency and algorithmic choice. It also encompasses studying the effects ML can have over time.

As comprehensive as that definition may seem, it surely is more tailored to Twitter’s own use of machine learning. But it certainly shows what a good definition of responsible machine learning should consist of: the use of ML itself as well as its development and effects. 

That’s why I think that the best definition comes from the Institute for Ethical AI & Machine Learning, an organization that developed a series of principles to guide the responsible development of machine learning systems.

Those principles are the following:

  1. Human augmentation. The belief that ML can offer incorrect predictions, which is why it always needs humans to supervise it. 
  2. Bias evaluation. The commitment to continuously analyze potential biases in ML to correct them.
  3. Explainability by justification. Anyone developing ML-based tools should aim to improve their transparency. 
  4. Reproducible operations. ML should have the proper infrastructure to guarantee reproducibility across the operations of ML systems.
  5. Displacement strategies. ML development should mitigate the human impact of ML adoption, especially when automation solutions displace workers. 
  6. Practical accuracy. ML solutions should be as precise as possible, which can only be achieved through high-quality processes. 
  7. Trust by privacy. The commitment to build processes that protect the data handled by ML and guarantee its privacy.
  8. Data risk awareness. The belief that ML is vulnerable to attacks, which is why engineers have to constantly develop new processes to ensure a high level of security.

While those principles are geared toward ML engineers, I think they extend their reach beyond the development itself. As you can see, the principles cover every important aspect of machine learning use: They take into account human perspectives, aim to constantly improve to leave all biases behind, worry about security and privacy, and even focus on mitigating the impact on the workforce.

Using those principles, I could say that responsible machine learning is the practice of developing and using machine learning algorithms to empower humans while aiming to limit their impact by continuously improving them based on a thorough analysis of technical, structural, and human factors. 

Why You Should Care about Responsible Machine Learning

Depending on who you are, there are 2 ways to justify why you should care about responsible machine learning. First and foremost, you might be a business owner, an executive, a manager, or even a developer working on machine learning solutions, so you have a direct impact on how those solutions come to be. 

That reason is easy to understand. If you have a say on the development and implementation of ML, then you have a moral, social, and even technical imperative to adopt the principles mentioned above. Why? Because those principles can prevent your solutions from causing harm and damage to users, teams, and even your own company. A machine learning solution without any guidance can become racist and discriminatory, can provide incorrect insights, or even derail complete processes with its outcomes. 

All of that can harm and offend users, destroy your company’s reputation, and/or affect your inner processes. You can’t risk doing any of that, so going the responsible machine learning route feels like the most sensible thing to do. 

But then again, you might be just a regular user who doesn’t have any influence on how machine learning solutions are developed. While you might think that relieves you from having to do anything, the reality is that users should push for a responsible machine learning agenda.

Most of the services we use today employ ML in some way or another, from YouTube to Amazon to Spotify, Netflix, and beyond. Without responsible ML use, you could end up suffering from poor ML development and implementation. That can be reflected in offensive and discriminatory messaging, privacy breaches, and poor customer experience and service, among others. 

Real-life examples abound, from algorithms that were taught to be racist to ML solutions that make discriminatory assessments.

As a consumer, you have a say on all of this. You can choose to engage with companies and organizations that carefully develop and implement ML. You can demand development teams to take action about poor ML implementations. You can support those that take steps toward developing machine learning responsibly.  

Responsible Machine Learning should be a goal for everyone, as ML will affect all of us at some point. Building ML consciously to mitigate biases and ensure the best outcomes and demanding companies to do just that should be the course of action everyone should take from now on. As the shift toward a more responsible AI starts to happen, we should embrace it, discuss it, and monitor it, because it can definitely reshape how ML implementations happen in the near future.

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