Artificial Intelligence

Artificial Intelligence and Ethics Keynote: With Great Power, comes Great Responsibility. How do we move from principles to practice?

I was fortunate enough to hold the Global Azure Virtual Keynote for the Azure team in Cologne on 25th April. My decks are at the foot of this blog post and this blog forms a summary of the discussion.

How can we use AI responsibly? As an industry, we have had similar discussions before in the era of Trustworthy Computing, and now we are applying the same concerns to AI. For those of you who are unfamiliar with the term Trustworthy Computing, it was a term used to apply to computing systems that are inherently secure, available, and reliable. It is particularly associated with the Microsoft initiative of the same name, launched in 2002. At the time, National Research Council recognized that the rise of the Internet simultaneously increased societal reliance on computer systems while increasing the vulnerability of such systems to failure. This report reviews the cost of un-trustworthy systems and identifies actions required for improvement. So why is this concept important now?

AI is experiencing a renaissance, but do we need to translate every business challenge into an AI solution?

The future of work is changing. What can organizations do about it?
There is a paradox between the opportunities that the world gains from digitalisation and globalisation, but it also brings risks as well. According to the OECD, they can put policies and institutions in place in order to help mitigate risks while maximising the benefits.

Despite the many opportunities, much anxiety surrounds the future of work, according to OECD data. Doomsday scenarios are unlikely to materialise, but there are some real risks. Many are worried that the world of work is heading for a dystopian future of massive technological unemployment, precarious work, workers with little or no bargaining power, and important skills gaps as populations age rapidly. But the future of work will largely depend on the policy decisions countries make. With the right policies and institutions in place, the opportunities that digitalisation, globalisation and longer lives will bring can be seized, and the risks mitigated.

There is a need for empathetic leadership as jobs churn and organisations change structurally. The OECD note that women are more likely to be impacted than men due to their higher risk of unemployment, increased likelihood of working in low paid jobs, and decreased likelihood of working in high paid jobs. The OECD note that failing to address these diversity and inclusion disparities is likely to result in a future of work which is exclusive with deeper social divisions, which could have negative ramifications for productivity, growth, well-being and social cohesion. Prevention and early treatment are necessary, along with social discussion to help with barriers to training.

There is also some suggestion that AI can help decision-making, and this is another trend to watch. AI-based decision making could help leaders to be data-driven and insights-inspired; if they are not there already.  We live in a world where we have an unwieldy amount of data, tons of information, some knowledge, and a bit of wisdom. When you talk to people, you are competing with so much more data than you can imagine; news outlets, celebrities, fake news, old news, people in our inner and wider circles. Perhaps data can help us to cut through that assault on the data-sensitive antenna by helping to identify deepfakes, fake news, and spurious data sources and patterns.

In this case, AI will go from identifying trends to making intelligent decisions based on better data. From the workplace perspective, AI will be seen less as a threat and more as an enabler and AI will help work get faster as we automate more. Potentially, augmented reality will actually be the reality in the next decade at work.

That does not mean we need to delegate everything to AI, however. AI can help us to make decisions, but we should also be able to override it as we need.

Explainability in artificial intelligence can help businesses to understand how a particular decision was reached. A few examples of explainable AI include the SHAP and the LIME open-source efforts, and some of these initiatives are used in Microsoft AI technologies and help with the explainability piece that we see in Power BI.

Why are we doing this? If the current virus crisis has shown us anything, it has shown how we need to help one another, but how we also rely on each other too.

Let’s help one another to thrive by thinking about the data and advice from organisations such as the OECD, and get ourselves ready to cope with the future of work.

Technology moves so fast these days, but the ethics has to keep up with the hype cycle.

At the start of each project, we can take a pause to look at the project through different lenses so that our principles can carry through to our practice.

Any questions, get in touch!

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