Critics and proponents of artificial intelligence (AI) now have our attention. Over the past few months, miraculous medical advances have been made through the exploration of the ‘genetic haystack’ as AI presents ‘extinction risks alongside societal scale risks such as pandemics and nuclear war’. Various analyzes and claims are pouring in continuously, all the way up to the discovery of ”
One thing’s for sure, AI didn’t just pop up out of nowhere, though the actions of hedge funds, other investors, and the economic press might put it in a different light. Like most major technological innovations, AI has been evolving for some time.
AI will prompt a rethinking of its relationship to sustainability, just as its impact on other aspects of economic and social life is increasing. While AI brings a range of benefits to society, it also brings with it the potential for significant disruption and risk.
What sustainability benefits can we expect from AI?
There are several key benefits to be gained from investing in and applying AI technology. They include:
- Integration of public health and environmental data. Continued degradation of biodiversity and associated aquatic and terrestrial ecosystems due to human activities will continue to degrade the environmental support systems (air, land and water) necessary for human life, making human health no longer possible. resulting in insufficient protection. The promise of AI and related digital technologies means that both natural and human infrastructures will become increasingly rich sources of data, with well-designed algorithms in databases empowering decision makers at all levels to the survival of a particular field. Being able to detect probabilities and status changes. At the site (ecosystem, city) and system level. These insights can create new opportunities for problem prevention and remediation.
- Build a new supply chain business model. Individual companies are generating complex supply chains that create significant structural barriers to the design of information reporting systems, timely access to data, and alignment of goals and indicators. At a more basic level, many customers do not know who their lower tier suppliers are. Companies envision new business models for supply chain management as they deal with new economic realities such as geopolitical risks, post-pandemic supply chain near-shoring, and accelerating climate change risks in the Asia-Pacific region. . A key element of this new mindset is investment in digital data systems, including enhanced AI with a more general data reporting platform centered around more consistent goals and metrics. Practical applications of such enhanced supply chain AI include analytics to optimize energy efficiency, water conservation, air quality, and safety performance in factories, warehouses, distribution centers, and ships. An integrated, data-driven supply chain business model enables electronic communication between suppliers and customers, delivering significant cost savings as well as significant operational efficiency gains.
- Realize opportunities for open innovation. Contamination from the continued increase in plastic production (9 billion tons to date, projected to reach 11 billion tons by 2025) has been detected in soils, crops and the seabed. There is growing scientific evidence that microplastics can be transported long distances through the air, where they can be absorbed into human lungs, and can alter cloud formation and composition, altering temperature and rainfall patterns. increase. The scale of the research task of developing more definitive data on these adverse effects dwarfs the capabilities of any single research institute, government agency, or industry sector. Open innovation research strategies can be developed to transcend traditional research agendas, but do so by allowing both government, corporate and foundation funders and stakeholders to abandon traditional silos. , should organize efforts to create universally owned and publicly transparent data. AI research and content development protocols are of particular importance in designing global microplastic research and modeling to better consider the distribution, concentration and impact of microplastics in the environment.
Key risks of AI related to sustainability
While trying to reap the benefits of AI technology, it is very important to be mindful of its risks. Key AI risks include:
- Mislead regulators, investors, consumers, or other stakeholders by inserting false data sets. Which data are most important today in assessing environmental, social and governance (ESG) risks, communicating the sustainability benefits of consumer products, and validating national emissions estimates to comply with international conventions? There is a lot of discussion about what The opportunities for generating unauthorized AI content in these and other applications are so great that additional data management controls should be put in place.
- Worsening inequality, diversity and inclusion. The results of many previous studies have concluded that facial recognition technology consistently underestimates, misidentifies, and/or distorts the characteristics of non-Caucasian populations. Other social research often underrepresents members of racial minorities. These and other deficiencies in current methodologies and technologies create many negative consequences, ranging from the challenges individual passengers face when boarding planes, to their access to credit and employment opportunities. The root cause of these deficiencies lies in the way researchers and their business sponsors design projects to optimize perceptions of existing human-managed processes that do not reflect population diversity. This ultimately leads to discrimination, increased automation of the human workforce, and loss of jobs.
- interfere with social behavior. Until this point, analyzes of AI impact have mostly focused on its ability to focus users’ attention as measured by clicks, online club participation, product purchases, and impact on political behavior. . Israeli historian and philosopher Yuval Noah Harari warns that a new generation of AI will shift the battlefield “from attention to intimacy.” As AI learns more languages, it can “build rapport with people and harness the power of intimacy” on subjects as diverse as human political dispositions, cultural and historical perspectives, food, and gender. It will even be possible to change opinions and worldviews. and religious preferences. Opponents of moving away from the internal combustion engine, connecting renewable energy production to the grid, using evidence-based risk assessments, and more are increasing the number of AI-designed arsenals at their disposal to confuse the public. . Disrupt government and corporate decision-making.
some proposed road rules
How can we see through the AI fog and extract what we need to make smarter decisions that drive sustainability? Build confidence and trust among multiple AI developers and consumers. Some practical measures to build are the logical place to start. They include:
- More proactive transparency practices. Making decisions more sustainable depends on having access to accurate and verifiable information. Given the rapid evolution of AI technology, those who develop new algorithms to guide AI applications will need to more clearly articulate their methodologies, identify the datasets they are collecting and analyzing, and rely on human intervention to implement them. Critical assumptions and values for mimicking or substituting behavior should be declared.
- Developing standards and certifications for AI data. This effort can coexist and support more effective AI surveillance on multiple levels. Individual industry sectors can create voluntary standards governing the development and use of AI technology, regulatory bodies such as the US and EU can develop and enforce minimum standards, international standard-setters can define best management practices, The authentication process should be optimized.
- Expansion of governance processes with multi-stakeholders.. Neither government agencies nor the private sector can effectively manage AI-related risks. Governments are reacting too slowly, and in some cases too politicized, to keep up with the rapidly evolving array of AI technologies. The private sector has historically been unsuccessful in balancing profitability with the public interest and protection of the planet. More hybrid, such as the recently launched Global Energy Alliance for People and Planet and the satellite methane data collection program managed by the Environmental Defense Fund to improve accountability for emissions from fossil fuel producers. Good governance examples show how key institutions can share. Powers and responsibilities for achieving a particular purpose. Similar opportunities await in the further evolution of AI technology.
Businesses and governments are rapidly investing in digital data technologies, including AI. Already in catch-up mode, the sustainability community is at a critical moment calculating how best to adapt to a new technological age that could potentially change both the planet and ourselves for better or worse. is.
