AI demands a contract of trust, says KPMG

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The summer of AI UK is upon us. But only if we avoid the shadows of the data, and the icy winds of poorly designed algorithms, the most eye-catching studies, and the false belief in living savings that we described in the previous report.

But what practical steps can active decision makers take to reverse the positives and negatives? And so great, why bother?

The answer is that one is not only concerned with ethical behavior in the macro-vital environment, which is the global market, socially connected.

Leanne Allen is Partner, Data Services, at consulting and services giant KPMG UK. Speaking this week at the Westminster eForum on the next steps for AI, he explained that all organizations must use AI responsibly to make their businesses more responsive and responsive. In turn, other benefits result – both economically and socially. She said:

Consumers and investors in society are demanding much more from organizations, that is across all industries. Whether this is a mistake for better, frictionless consumer services, or the goal of the industry with greater personalization, or a desire for industries such as Financial Services, they should do more to help address inequality and promote sustainable finance.

Expectations for firms to innovate and drive real value from both data and new technologies continue to grow rapidly. And the adoption of advanced technologies that include machine learning [ML] and AI will give organizations an edge in responding to these demands.

In this sense, improving what Allen calls the “customer journey experience” is as important as the general desire for ethical behavior, because in the long run businesses will become more considered and sustainable, which suggests:

He makes better and faster decisions. That increases accuracy, which means better understanding the customer, and leads to improved products and services. These are such as the risk of pricing or products more precisely, and the degree of change in the performance of operations. And this, of course, was really useful for organizations in driving down internal costs.

So, in Allen’s opinion, “there is no dispute” about the benefits for organizations and customers to use big data analytics and AI. But users should avoid being taken away by all these new possibilities. That is where the danger is real, he warned them;

All that potential comes with new and increased risks. The reality is that without adequate monitoring and guidance in both the planning and use of advanced technologies, we are already beginning to see unintended damages.

Improper interest in the results of the decision-making model causes financial damage to consumers, and can cause damage to the reputation of organizations. Systematic company practices make pricing unfair [sic] to be shut out of insurance, and to remove that access to conduct risk.

Another example is the sale of incompetent or cheap services and services to clients, or targeted ads, dynamic pricing, and ‘purpose creep’ in the use of data, which have resulted in non-compliance with existing data protection laws.

These are just a few examples of the losses and challenges the industry faces.

There is quite a list of downsides. And the knock-on effect is the loss of trust between consumers/citizens and whoever is pulling their data. Such repercussions can have far-reaching effects on credit history and financial inclusion, for example.

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For this reason, decision-makers never – intentionally or otherwise – rely on peremptory risk in pursuing easy wins, said Allen:

Trust is a defining factor in a government’s success or failure. So, as firms move forward, transforming their business to be more data- and insight-led, they focus on building and maintaining trust.

We have now seen many organizations with their initiatives to build governance and regulation around the use of big data and AI. But the level of development varies.

We are typically with Financial Services leading the way, and those organizations define their own ethical principles. They carry out their operations and take a risk-taking approach, aligning with core principles such as fairness, transparency, accountability and accountability. Collectively, those who promote an active faith.

True North corporate ethics

However, in the deepening recession, struggling consumers thought the Financial Services Authority would lead the charge into a better society with a pinch of salt. But let us hope that she is sincere.

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For Ian West, Partner in another section of KPMG’s UK operation, such as Head of Telecoms, Media and Technology, trust is the “golden thread” of business. He added;

We need to ensure that businesses are ready to deploy AI responsibly. KPMG’s actions are necessary to point the organization of corporate and civil ethics towards the “true north”, by establishing five guiding pillars for the ethics of AI.

Mixing your metaphors! but the west (or is that the north?) remained;

First, the key is to prepare the customer now. The most immediate challenge to business with implementing AI is disruption to the workplace. But organizations can prepare to help employees effectively adapt to the role of technology in their workplaces early enough in the process.

There are many ways to do it. However, it is worth considering partnering with academic institutions to create programs to address the need for electronic skills. This will help train, train, and manage a new AI-enabled workforce, and will also help with mental health.

Second, we recommend developing strong oversight and governance. Therefore, it is necessary to start planning broadly about the deployment of AI, specifically around the use of data and privacy standards. And this goes back to the challenge of faith. AI stakeholders need to commit to business, so it is crucial that organizations fully understand the data and frameworks that underlie their AI in the first place.

Third, autonomous algorithms are ready for concerns about cybersecurity, which is one of the reasons why machine learning systems are an urgent priority. Firm security must be built into the creation of algorithms and data management. And of course we could have a bigger conversation about how many technologies we have in the medium term.

Fourth, the unfair study that can take place in AI without proper governance or regulation to mitigate it. Leaders should make an effort to understand the operation of sophisticated algorithms that can help eliminate that concentration of time.

Passions for the use of training algorithms must be relevant, appropriate for the purpose, and permitted. It is worth arguing that it has a team dedicated to this, as well as an independent review of critical models. Bias can produce adverse social impact.

Fifth, we need to increase business transparency. It is transparent to all previous steps. Don’t just be transparent with your work – of course it is – but also give your clients the clarity and information they want and need.

Think of this as a trust contract.

My take

Well put. So the main lesson is, don’t sacrifice user trust in your quest to gain a competitive advantage. Take customers with you on your journey together. Help them see how you can make their lives better, and give back to your business.

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