Prof Jingchen Zhao, Professor, NLS https://www.ntu.ac.uk/staff-profiles/law/jingchen-zhao
In the rapidly evolving world of technology and artificial intelligence (AI), obtaining explicit approval of AI applications from the community and stakeholders has become important. This approval, often in the form of a Social License to Operate (SLO), plays a pivotal role in ensuring AI's responsible and sustainable integration into companies. In this blog, I will discuss the significance of SLOs in the AI landscape and how they are shaping the future of corporations. The term SLO in the business arena has been defined and understood in diverse ways, not least owing to the constantly fine-tuning expectations and demands of stakeholders in the dynamic corporate settings, especially those with the participation of multinational enterprises, as “a cluster of corporations of diverse nationality joined by ties of common ownership and responsive to a common management strategy” and the global value chains they invest in and impact on. Focusing on its relationship with stakeholders, the SLO is defined by Black as “the negotiation of equitable benefits and impacts of a company in relation to its stakeholders, over the near and longer term”. More interactively, Thomson and Boutilier contextualise the SLO as existing when a project has continuing acceptance or approval within the local community and other stakeholders. More practically, a survey of mining industry professionals found that 90% of practitioners viewed the SLO as “an intangible, impermanent indicator of ongoing acceptance of a company's activities by communities”.
The award of SLO is believed to build consistent and trustworthy AI and stakeholder interaction. The loss of SLO will lead to higher costs and risks, and even hinder the companies from using AI for development and strategy due to a lack of trust and poor reputation of the companies and lack of approval from the community.
In recent decades, the term SLO has become increasingly prevalent, finding its way into various industries, such as oil and gas, and the financial sector. AI has been broadly used in these industries and its use in these sectors has generated complications in recent years. Therefore, apart from SLOs focusing on a particular industry, companies also need to pursue and maintain SLOs, in general and individual terms, for AI and its strategy and application. The widespread integration of AI-powered technologies across various sectors has significantly transformed traditional social norms and interpersonal dynamics.
It is important to for the AI-active companies to consider establishing and maintaining SLO to pursue more sustainable, trustworthy and accountable AI usage in a corporate setting. Stakeholders and society at large must accept that corporations that want to apply and develop AI in their business practice and decision-making can be trusted. Each stakeholder has a different background, voice, information, expertise, and set of demands, leading to variations in their understanding and level of trust. Therefore, the relationship between under-trust and over-trust appears to be ample for stakeholder-specific analysis, especially as local community members and employees will have significantly different experiences with using AI by corporations.
Strengthening multi-stakeholder partnerships that promote effective public-private and civil society partnerships through stakeholder participation and engagement will enhance the AI’s broad acceptance and promote accountable AI. This exploration will cultivate trust, enhance collaboration, and encourage coordination among various stakeholders, including AI scientists, initiatives, institutions, and the broader civil society.
Furthermore, from the technical perspective, the reasons driving design choices of AI may vary among projects. Consequently, no standard initial point exists from which designers are obligated to commence. Therefore, building and maintaining SLO for AI needs participation from different stakeholders who own different information about the company and represent the interests of diverse groups.
Companies must collaborate with policymakers to establish a most suitable ecosystem with AI. Combining human insight and AI will pave the way for remarkable achievements and success in the boardroom. The human directors should all build and maintain SLOs with the wider public so that companies can promote the more comprehensive use of AI while also managing its risks, such as data bias, and unemployment risks through increased automation. Keeping up with rapidly advancing AI technology, SLOs will help the co-regulation from public regulators and soft regulators (such as whistle-blowers), shape AI-related policies that balance innovation, encourage economic benefits, encourage sustainable development and encourage societal fairness. It is hoped that SLOs will lead to a mutually beneficial outcome for AI applications as corporations will thrive with AI that aligns with social expectations, and regulators will also have easier tasks to regulate AI with the support from softer applications of AI. While the business landscape is increasingly complex, SLOs will help boards use AI to process vast volumes of data, extract valuable insights smoothly and effectively, and, most importantly, stay ahead of AI.
Further Reading:
Detlev F. Vagts, ‘The Multinational Enterprise: A New Challenge for Transnational Law (1970) 83 Harvard Law Review 738.
Leeora Black The Very Seductive Social License to Operate – a Reality Check (2012)
Ian Thomson and Robert G. Boutilier, ‘Social Licence to Operate’, in Peter Darling, (Ed.), SME Mining Engineering Handbook, Society for Mining (Metallurgy and Exploration Inc., Englewood, CO (2011)
Jacqueline Nelsen, ‘Social Licence to Operate: Industry Survey’ (2005)
Detlev F Vagts ‘The Multinational Enterprise: A New Challenge for Transnational Law’ 83(4) Harvard Law Review 1970, 739-792
コメント