Artificial intelligence opens vast opportunities for enterprises of all kinds. But it’s crucial to ensure that the technology is used ethically and sustainably.
By Dr. Vedran Podobnik, Worldwide Data, Analytics & AI Practice Lead, HPE Advisory & Professional Services
Let’s start with the ‘why,’ and the potential benefits. Why do companies collect (or even buy) consumer data and subsequently analyse it using various advanced analytics or AI-based methods?
There are two main reasons: First, they want to provide a better customer experience to consumers; and second, they want to achieve or overachieve their business goals.
Personalizing the customer experience
If we focus on customer experience, let’s think how companies can make customers happy. They can boost customer experience by providing seamless personalised service to them. This is the opposite of the traditional mode of service delivery, where providers use the “same service fits all” approach.
For example, think about online retail. An optimal shopping scenario from the consumer perspective would be one in which, immediately after you visit an online retailer’s web shop, your shopping bag already has recommendations for items you might want to buy. It’s seamless and personalised for you.
Or even better, online retailers wouldn’t wait for you to visit their website before informing you about items that you might consider purchasing. Powerful. But online retailers can’t create such customer experiences without collecting lots of data about their customers, their historical purchases and other contextual information connected with age, location and so on.
Meeting business goals – and thinking beyond efficiency and profitability
Now, let’s move on to consider how companies use data to achieve their business goals. A typical business goal is to increase company revenues and profits. This means that companies are using and analysing consumer data to sell more (either by winning new consumers or creating new markets) and to provide services more efficiently from the cost perspective. This, for sure, is happening, and we cannot neglect that.
However, we are also seeing another emerging trend: Companies are turning to AI and analytics to accomplish a wider range of business goals, such as contributing towards environmental, social and governance (ESG) targets. I can give a first-hand example from my company, HPE, where we are heavily investing in both “sustainable IT” and “IT for sustainability”. In both of these areas, the decisions we are making are heavily data-driven. We wouldn’t be able to help our customers to quantify the sustainability benefits of their digital transformation journeys without having access to lots of their data (as well as data from the broader market to create a baseline).
Think, for example, about Generative AI, a technology whose potential some compare with the impact that the invention of the Internet and the World Wide Web had on societies and businesses. Training Generative AI technologies such as ChatGPT cannot be done without having access to vast amounts of data. It’s also very expensive, from the money- and energy-consumption perspectives. If it were possible to achieve the same or similar outcome from the Generative AI model by using 20% more data and 80% fewer training cycles (and consequently 80% less energy), wouldn’t that be a responsible thing to do from the ESG goals perspective?
Controlling the risks: The need for robust ethics policies
I’ve described some of the benefits, but what about the risks – in particular, the social risks? What about the “Big Brother” concerns?
From my reflections above, it should be apparent that I believe that the benefits of having access to data and using advanced analytics and AI to create new insights outweigh the risks. However, risk must be well understood, soundly documented to enable consumer awareness, and properly managed and governed. This is where regulation becomes so essential, and why I welcomed the opportunity to provide input for the UK Parliament.
At HPE, we recognise the critical need to govern the risk related to all of the AI-related projects we tackle internally, and for our customers. This is why we established our AI Ethics Advisory Board and AI Ethics Working Group in 2020. The AI Ethics Advisory Board is a pan-HPE group of leaders to provide oversight and make decisions that bridge the business and ethical considerations of AI. The AI Ethics Working Group is an operational group of experts who develop standards, guidance, and systems for preventing unethical outcomes, while assessing and advising on the AI we create.
Both groups are led by five ethical principles that guide HPE’s use and development of artificial intelligence (see HPE AI ethics and principles). AI must be:
1. Privacy-enabled and secure. It must respect the privacy of individuals and minimize the risk of errors and malicious use
2. Human-focused. Respect human rights and be designed with mechanisms and safeguards, such as human oversight, to prevent misuse.
3. Inclusive. AI should minimize harmful bias and ensure fair and equal treatment and access for individuals.
4. Robust. It should be engineered to build in quality testing, include safeguards to maintain functionality, and minimize misuse and impact of failure.
5. Responsible. Artificial intelligence must be designed to enable responsible and accountable use, to allow an understanding of AI, and to enable outcomes to be challenged.
Antonio Neri, HPE’s President & CEO, summed it up well: “Artificial intelligence holds enormous potential to advance the way people live and work, but we must ensure that we apply these powerful tools ethically and sustainably.”
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