AI: A World of New Opportunity and Risk - Deepstash
AI: A World of New Opportunity and Risk

AI: A World of New Opportunity and Risk

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How To Responsibly Adopt Artificial Intelligence

How To Responsibly Adopt Artificial Intelligence

We also see some negative, often unintended consequences of these technologies. They go from the rise of fake news and algorithms that favour the incendiary and divisive over the factual, to major privacy breaches and AI models that discriminate against minority groups or even cost human lives.

AI is a powerful tool, and it’s never been more important for C-suite executives to understand both how to leverage it for growth and innovation, and how to do so responsibly and ethically.

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The Long Term Impact Of A.I.

Leaders need an understanding of the long-term impact – both positive and negative – of the algorithms they build and deploy.

It’s by no means a charted path; success is as much about asking the right questions, keeping an open mind and being aware of the key issues at stake, as it is about finding the “right” answers.

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The Standard Guidelines

The World Economic Forum, with supporting research from INSEAD’s Hoffmann Global Institute for Business and Society, has created a guide for C-suite executives who are committed to adopting AI technologies effectively and responsibly.

This guide takes the form of questions that executives should be asking themselves as they build their AI capabilities. It also offers some possible answers to these complex issues.

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Building An Effective A.I. Capability

It’s tempting for executives to believe that AI will magically deliver new revenue streams or efficiency gains, but the truth is that AI initiatives ought to undergo the same rigorous business planning as any other project.

An AI initiative should, first and foremost, align with the organisation’s key strategic goals and directly contribute to moving the KPIs that buttress this strategy. 

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A Few Thumb Rules

The first rule of machine learning: Start without machine learning.

Executives must “know the why” for AI initiatives. An iterative development that starts with simple, explainable models is recommended. Investments in more complex solutions that may deliver marginal accuracy gains but are much harder to interpret or deploy at scale should be avoided.

The best rules of thumb to mine business value from AI are: “Don’t get caught in the hype” and “Start simple and test value.”

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Understanding The Key Stages

It is helpful for executives to possess a broad understanding of the key stages of an AI initiative and the technical risks at each of these stages. For instance, executives broadly underestimate the amount of data cleansing and preparation that are required for building viable algorithms.

Data scientists, on the other hand, are likely to focus on building the most accurate model possible using the latest techniques, without understanding the business context and many trade-offs.

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KPIs Of Leaders For A.I.

The most successful AI initiatives are close collaborations between data talent, business stakeholders and sponsors, engineers as well as end users who are available to test the solution and send feedback.

Building the right team and upskilling the organisation to enable this kind of collaboration is essential for success.

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The Risks Of A.I.

Running an effective AI capability, however, is more than simply leveraging these technologies to realise EBIT and market share gains. Now more than ever, executives must have a keen understanding of the new business risks involved in developing algorithms.

They must ensure that their organisations are pro-actively mitigating them and that they comply with upcoming regulations.

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A.I. Accountability

AI also raises questions of accountability. Who is responsible:

  • When a driverless car crashes?
  • In a lawsuit claiming unfair hiring informed by AI algorithms?
  • When the wrong medical treatment is prescribed because an AI diagnostic system contained errors?
  • For a large financial loss incurred by an algorithmic trading platform?

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Building Ethical Standards

The organisations that mitigate these risks best are those that build on their own ethical standards and gateways throughout the AI lifecycle, from how they collect and prepare data, to how they build, test and deploy models.

Those that adopt new data and AI risk management practices, processes and tools to both comply with upcoming regulations and to ensure customer trust.

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