Machine learning systems learn from experience and without explicit instructions. They learn patterns from data, then analyze and make predictions based on past behavior and the patterns learned. Artificial intelligence is a combination of technologies and can include machine learning. AI systems perform tasks that mimic human intelligence, such as learning from experience and problem solving. Most importantly, AI makes its own decisions without human intervention.

Now more than ever, every company is a data company. By 2025, individuals and companies around the world will produce an estimated 463 exabytes of data each day, 1 1. Jeff Desjardins, "How much data is generated each day?" World Economic Forum, April 17, 2019. With that in mind, most businesses have begun to address the operational aspects of data management--for instance, determining how to build and maintain a data lake or how to integrate data scientists and other technology experts into existing teams. Fewer companies have systematically considered and started to address the ethical aspects of data management, which could have broad ramifications and responsibilities. If algorithms are trained with biased data sets or data sets are breached, sold without consent, or otherwise mishandled, for instance, companies can incur significant reputational and financial costs. Board members could even be held personally liable.

Welcome back to the last article in our Lean Machine Learning series. Before beginning this article, we recommend you check out Part 1 and Part 2 first. As a quick recap, we've been discussing how to build better machine learning products by applying Eric Ries' Lean Startup approach. In Parts 1 and 2, we described how to make a desirable, feasible, and viable machine learning product. Each of these areas is part of the traditional Innovation Sweet Spot (ISS). We've dedicated our final article in this series to an extremely important topic -- Ethics.

I give a short introduction to data ethics. My focal audience is mathematicians, but I hope that my discussion will also be useful to others. I am not an expert about data ethics, and my article is only a starting point. I encourage readers to examine the resources that I discuss and to continue to reflect carefully on data ethics and on the societal implications of data and data analysis throughout their lives.

Companies that misuse personal data are hit with eye-watering fines and their reputation is tarnished – sometimes irreparably. Despite this, four in 10 UK-based employees working with data do not believe data ethics is relevant to their role, research has shown. Data ethics, as defined by Harvard Business School, deals with the moral obligations of gathering, protecting, and using personally identifiable information, and how it affects individuals. Essentially, anyone who handles data needs to be well-versed in the basic principles of data ethics. But a survey of 1,000 British employees working with data by analytics automation company Alteryx found 42% do not believe data ethics is their concern.

One of the key lessons taught by Mary Shelley's infamous story of Frankenstein's monster is that things aren't always greater than the sum of their parts, regardless of the quality of the parts themselves An altogether less visceral but equally composition-based process goes into building today's artificial intelligence (AI) platforms. One of the most powerful AI models used today is deep learning, a machine learning algorithm that identifies patterns in different sets of input data, and uses them to generate insights that help inform human decision-making. Deep learning applies vast layers of artificial neural networks to data, creating a'black box' of calculations that are impossible for humans to understand. Luckily for data scientists, preventing the creation of a'monster' when developing AI requires an understanding of data validity, rather than the supernatural. AI platforms built on deep learning assume that more data equals better accuracy.

To unleash the potential of AI safely, however, issues such as accuracy, human control, transparency, bias and privacy need to be addressed. So governments should be role-modelling the ethical use of AI, and educating their people on AI and how to be ready for the opportunities and challenges. One way countries could do this would be through setting up a body that is a visible focus for AI: a centre of excellence. Our project recommends this as a way to increase ethical AI use in a country and build public support for it across the economy and society. The centre could draw staff from industry, government, academia and civil society, using a multidisciplinary and collaborative approach to provide advice on AI and algorithm use for government operations. The centre would start to raise awareness on AI itself and encourage conversations about people's level of comfort with using it in different situations.

AI can be used to enhance the accuracy and efficiency of decision-making and to improve lives through new apps and services. It can be used to solve some of the thorny policy problems of climate change, infrastructure and healthcare. It is no surprise that governments are therefore looking at ways to build AI expertise and understanding, both within the public sector but also within the wider community. To unleash the potential of AI safely, however, issues such as accuracy, human control, transparency, bias and privacy need to be addressed. So governments should be role-modelling the ethical use of AI, and educating their people on AI and how to be ready for the opportunities and challenges. One way countries could do this would be through setting up a body that is a visible focus for AI: a centre of excellence.

Data collection and analysis is inseparable from modern business. You'd be hard-pressed to find a medium or large-sized company that didn't gather data these days. As customer information plays a more prominent role in industry, though, data ethics becomes a more pressing concern. You'll hear more and more people talk about data ethics today. It's certainly getting a lot of press, but does it affect your company that much?

Essence, a global data and measurement-driven agency which is part of GroupM, today [29 April 2020] released a unique report on the future of advertising based on the predictions of experts across academia, business, marketing, technology, publishing, and advertising trade organisations around the world. The study evaluates the likelihood of 15 different scenarios occurring over the next decade and assesses the implications of each for the future of advertising. Each scenario tested explores the influence of a key dynamic or catalyst, from the use of biometric data to personalisation, privacy, artificial intelligence, virtual reality, regulation, payment models, and more. "As an industry we have lots of insight into how technology is likely to evolve over time," said Kyoko Matsushita (pictured below), Global CEO at Essence. "We conducted this study to provide more clarity about what that evolution will mean for advertising and marketing, to identify issues in need of the most urgent attention, and to help companies prioritise their innovation and marketing transformation investment decisions."