Co-authored by Nicholas Cousins, Solicitor and Dave Hu, Solicitor
Co-authored by Nicholas Cousins, Solicitor and Dave Hu, Solicitor
While our minds may associate artificial intelligence (AI) innovation with tech hot spots like Silicon Valley, the Asia Pacific’s (APAC) emergence as a pioneering AI force must not be discounted.
Propelled by the COVID-19 pandemic, the global adoption and implementation of AI continues at pace. AI’s real-time responsiveness and agility to customer and market demands are important for enterprises focused on staying competitive and relevant. Extracting the full value of data, boosting productivity and tapping into a previously un-tapped workforce are factors driving AI deployment across the APAC region.
APAC is the fastest growing AI market in the world. Excluding Japan, APAC’s investments in new technologies such as AI is expected to account for close to 40% of its total information communication technology (ICT) investments by the end of 2023, a growth trajectory expected for at least the next decade[i] and miles ahead of the rest of the world which sits at a rough growth rate of 22%.[ii]
This is no coincidence. Countries like China, Japan, South Korea, India, Taiwan, and Singapore, have all announced multiple-million-dollar national AI strategies to drive AI readiness and regulate AI deployment.[iii] Other APAC members including Australia, Thailand and Indonesia, have similarly established targeted initiatives and programmes in strategic sectors to drive the adoption of AI.[iv][v]
Amongst these nations, China’s growth is the most prominent, which in part can be attributed to visionaries in its private sector. China’s Kai-Fu Lee who currently oversees $2.7 billion of assets at Beijing-headquartered Sinovation Ventures has backed seven Chinese AI start-ups that have all become “unicorns”.[vi] Overall, APAC’s groundwork has set it up as a conducive environment through which innovative and competitive AI businesses can emerge and thrive.
In recent years some of the world’s most innovative AI projects have originated from APAC members. Chinese e-commerce giant, Alibaba, launched its Agriculture Brain platform in 2018, allowing farmers to increase yields and reduce the loss of profits through access to “smart” farming tools. The platform’s machine learning model processes various data, including satellite images, allowing farmers to predict natural disasters as well as visual and voice recognition to detect herd diseases.[vii]
In other sectors like insurance, APAC’s insurance heavyweights have recognised the utility of AI, reflected by conglomerates like Prudential Singapore and Sompo Japan automating aspects of their claim management cycle by incorporating machine learning to detect and reduce fraud and streamline assessments, freeing the capacity of human staff.[viii]
As consumer expectations for e-commerce sites like Tokopedia and Alibaba increases, due to the seamlessness and responsiveness of global frontrunners like Amazon, it is no surprise these marketplaces continue to rely heavily on AI in order to predict demand, operate their smart warehouses and dispatch goods. Not to mention their reliance on AI to process data analytics to identify buying habits, market trends and preferences help forecast consumer behaviour patterns in such ways where each consumer’s online experience is individually engineered and tailored.[ix]
Despite AI’s benefits, there remains considerable concern for the legal and ethical issues attached to AI implementation. For instance, recent global data breaches, has spread global trepidation and deterrence for housing data, and left many would-be users intimidated of entering unchartered technological territory. The issues arising in AI deployment and usage in APAC, reflect those around the world.
Data ownership, security and information privacy
AI is useless without troves of data, but enterprises holding AI processable data ought to ask a number of questions. As most data used by AI is stored in clouds, businesses must consider the security, support and maintenance capabilities of their cloud storage provider, whether they are housing personal information, whether data has been de-identified or anonymised, and data breach response plans. This is in addition to considering the ownership of such data, as well as the ownership of data outputs that contain proprietary rights. Algorithms are key and underpin all systems – with many companies increasingly relying on algorithms to make significant decisions. The potential for AI and algorithms to improve business and social welfare is accompanied by material ethical risks. Bias has been a feature of how some operate – and there are growing calls for the ethical implications of algorithms to be understood (whether through transparency and giving more information relating to how the machines are trained and how they operate). Australia has published, for example, voluntary AI Ethics Principles – aimed to ensure AI is safe, secure and reliable. Privacy laws fall short however of obliging companies to increase transparency or restrain decision making without human involvement although there are some calling for a ‘right to explanation’ of the ways that algorithms are working to enable humans to question automated decisions that affect them.
AI has challenged traditional notions of intellectual property (IP) and aroused many questions. Who is the owner of AI developed IP: is it the AI inventor or the AI machine itself? In instances where human authorship is required can this be achieved when the only human input is setting the AI parameters, but the AI independently develops the resulting work? What if AI infringes an IP right without any human input? Should the liability lie with the AI’s program developer or AI’s owner at the time of infringement? Recently, the Australian High Court dismissed an application from Dr Stephen Thaler for special leave to appeal the decision of the Full Federal Court, which had ruled earlier in 2022 that only humans can be an inventor under Australian patent law. The issue is similar in copyright law. Is a computer program just a tool which an author uses to record his or her works?
Questions of ultimate liability for the actions of AI are not only unique to the area of IP. Many businesses are now maximising profits by using pricing algorithms, setting pricing decisions based on competitors’ prices and other available data. As AI becomes more advanced, this poses antitrust risks of reaching anticompetitive agreements or otherwise lessening competition independent of any direct human interaction. Should companies be liable for the actions of AI that have an anti-competitive effect? Can AI robots collude? Some regulators suggest if the risks were known or at least not unlikely, companies should have built-in compliance measures within their AI at the outset, but issues of agency remain.
Of course, the above are only some of the primary issues we identified to get your businesses and technology procurement teams to think about. As these are fast evolving areas of law, it may be difficult for businesses to keep up with local regulatory changes. Increasingly Dentons are helping clients to conduct algorithm impact assessments – an idea whose time has come. With offices located in prominent technology hubs in APAC such as, China, Singapore, Australia and beyond, Dentons is supporting businesses with their AI related ventures.
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