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S U S T A I N A B L E B U S I N E S S 12 As with many tech solutions, artificial intelligence takes vast amounts of energy to create and implement but, given the many ways it can aid sustainability projects, do the pros outweigh the cons? Are AI and sustainability the perfect match? Applications abound for sustain- able AI and machine-learning. FarmGrow, for example, is a social enterprise established by the Rainforest Alliance and Grameen Foundation to support farmers in major cocoa-producing areas of the world. It coaches them in optimis- ing yields without negative envi- ronmental effects. Using Satelligence to combine sat- ellite imagery and AI, FarmGrow employs remote-sensing technolo- gies to track production and receive alerts about sustainability risks such as deforestation. At the other end of the food cycle is Karma, a food-waste app endorsed by President Obama, also underpinned by AI. Karma enables restaurants and supermarkets to list food that would otherwise be thrown away and sell it to the pub - lic at a discount. To date, the busi- ness has raised $16.7 million, res- cued 900 tonnes of food, saved two million meals and cut 1,300 tonnes of CO 2 . rtificial intelligence (AI) is hot, sexy even. Sustainability, by con- trast, can come across as rather book smart and earnest. Nevertheless, like the odd couple of tech for good, AI and sustainability are I has its critics and its issues. However, the argu- ment is not so much against AI in principle, more in practice. As with most datacentre tech- nologies, including blockchain, energy consumption is a valid concern. There are also question marks about the cost of AI as sus- tainable project spend. In addition, when seeking more holistic sustainability solutions, the potential arises for unintended con- sequences. An algorithmic model overly focused on a single metric, such as carbon, might ignore or even exacerbate other environmen- tal and ethical risk factors, such as child labour or ocean plastic. The rush to erase all trace of people from AI is wrong, argues chief executive of M&C Saatchi Performance Christian Gladwell. "Society relies on two key con- cepts of human interaction: nuance and context; and machines strug- gle with both," he says. "AI needs human insight to function to its true potential and support a sus- tainable society. Without that def- erence to humans for a final deci- sion, we run the risk of algorithms making, at best, insensitive deci- sions, at worst, dangerous ones." Instances of AI getting it wrong include Uber failing to override surge-pricing algorithms at the time of a London terror attack. When it comes to energy con- sumption, the starting point for unsustainable AI is bad data, says David Niki, chief technology officer at Innowire. "The most power-con- suming process is to train the AI and intelligent models need thousands of datapoints," he says. "However, increasingly being seen together in public. From precision agriculture to environmental monitoring or sup- ply chains to energy grids, the two of them are clearly in a relation- ship. It is complicated, however. Working with bytes of a differ- ent kind, London-based iSize Technologies specialises in deep-learning for sustainable video delivery. Ideal for data-intensive streaming services, its AI precoder software significantly reduces bitrate with no loss of visual qual- ity, but up to a four-fold reduction in energy. Putting such AI and sustainabil- ity benefits into perspective, the latest year-on-year figures show Netflix subscriber numbers grew 20 per cent in 2019, yet its energy consumption rocketed 84 per cent, amounting to enough to power 40,000 US homes. This, though, is the world of technology and tech does not stand still. In logistics, the focus is evolv - ing with the core technology itself, towards a fusion of the internet of things (IoT) and AI, known as AIoT, says chief architect at MindTree Rajamani Saravanan. "The original purpose may have been early pre- diction of faults or optimising usage patterns, but the large volume of data now available has opened up new avenues of exploration," he says. "AI has essentially enabled the creation of energy-efficient freight systems, while in pursuit of greater supply chain orchestration." Not all positive impacts of sus - tainable AI are environmental, though. There are societal bene- fits, too. Since it is easier to criticise and alter a machine than ourselves, algorithms and AI can help make us more conscious of our own biases, says Diana Xhumari, chief execu- tive of digital transformation com- pany Tegeria. "Distancing ourselves from the decision-making process is mak- ing us more logical and fair," she says. "For the first time, we are talking openly about unconscious biases and how to handle them, so we don't discriminate against any- one, either through technology or human interaction, especially in the workplace." A A For the first time, we are talking openly about unconscious biases and how to handle them, so we don't discriminate The most power- consuming process is to train the AI, which needs thousands of datapoints Jim McClelland A R T I F I C I A L I N T E L L I G E N C E On the one hand, AI supports and boosts sustainability; on the other, sustainability can be seen as a con- cern and even a liability for AI. So, are they the perfect match? There are arguments both for and against. not all data is good data, as data- sets tend to have duplicate data, out- standing data or even biased data. So, the real sustainability case is to find the proper data." Essentially, AI-based solutions are only as good as the algorithms they use, agrees José Manuel Benedetti, principal architect at IT integrator Insight. They must be coded to cope with specific-use cases and given best quality available data. The answer, however, is not sim - ply to throw money at AI and sus- tainability, inflating expectations and budgets. He concludes: "Over- ambition with AI is essentially run- ning with scissors: you might get to where you're going sooner, but trips along the way could seriously harm you or others. Ambition is admira- ble, but has to be accompanied by realism. A relatively modest project with achievable sustainable goals will give much better long-term results, even if it's less glamorous." The problem for AI and sustaina- bility is ultimately not AI itself, but bad applications of AI. In the end, no amount of smart tech can save a dumb decision. FOR AGAINST ISABEL INFANTES/Getty Images Food-waste app Karma enables restaurants and supermarkets to list food that would otherwise be thrown away and sell it to the public at a discount

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