Artificial intelligence (AI) is expected to have a dramatic impact on our professional and personal lives. The big question is how and when. I think both those depend, at least in part, on how we (that is, people in general) respond to the technology as it develops. There are significant questions about what is possible, and also what is right. We would do well to address those questions before AI matures to such an extent that it is too late.

Ethics, education and acceptability

As one of my colleagues said, I think that ethics need to be addressed in data science education - and I believe this needs to start now. The data scientists of the future need to be thinking about these issues. In fact, the more people who have these conversations, the better, because they will raise awareness and affect perceptions of AI, and therefore also affect its acceptability.

In thinking about ethics, we need to be aware that the situation is not static. New regulations, such as the General Data Protection Regulation (GDPR), affect both what is possible, and what is acceptable. Will there be more regulation in future? Almost certainly! Will they be tighter? It is hard to say, but I cannot see it being otherwise. Organisations may see GDPR as a "just another regulation" to comply with, but it is designed to place control of data back in the hands of individuals, and I do not think this will change. This trend means that we all need to think about how we should behave, not how we must because of regulations.

First things first

I think it is essential to address ethical issues before AI matures to a level where we start seeing human-like robots, proper level-5 self-driving cars, and so on. I think it may be even more critical, in the short term, to consider how AI adoption and company culture affect each other. In other words, to talk about - and influence - people's perception of AI and its likely impact on work, education, and the society.

Let's begin with talking about people's perception of #AI and its likely impact on work, education, and the society. #AIethics Click To Tweet

I believe that company culture must be nudged in the right direction to ensure the successful adoption of AI across the enterprise. We can all act as champions of AI and machine learning, by taking some actions, such as:

• Educate yourself. Anyone who works as if AI adoption is a personal crusade, long on emotion but short on facts, will lack credibility. Passion and belief are both excellent. However, to persuade others of the positive impact of AI, you must also have the facts to back up your arguments logically: and be prepared to listen to alternative arguments.
• Lead by example. If you believe that AI can help increase revenue, improve customer satisfaction, or do something else to support your business strategy, then you must demonstrate this. You could, for example, occasionally share well-researched papers on the positive impact of AI, and how it is likely to assist with specific tasks.
• Work with like-minded colleagues. Many of us work in large organisations, which means that cultural change is an even bigger task. However, it also means that you are probably not the only person who thinks that AI can have a positive impact on your business. It may be helpful to look for colleagues who share your passion and start spending time with them. You can then discuss specific steps that each of you can take to influence your organisation.
• Stay positive. However strong your beliefs about AI, you are likely not in charge of corporate policies, the definition of KPIs or the business strategy. You, therefore, need to recognise the limits of your influence, while staying positive about what you can do. Being realistic is important.
• Be patient. Changing company culture can take years, especially when it comes to adopting new technologies such as AI. Every organisation is different, and there is no magic wand to ensure a successful rollout of any technology.

The way forward are "small steps". Start looking for some defined decision use cases where AI can be applied in a small way, and lead by example. This approach means proving its value using focused and contained use cases that do not disrupt critical business processes. Examples that others can also adopt are likely to be more valuable.

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