Sustainable investing has seen significant growth in the last couple of decades, primarily driven by millennials throughout the 2010s. In 2020, millennials contributed $51.1 billion to sustainable funds, a marked increase from less than $5 billion five years prior. This trend is not limited to one generation; now, every generation is interested in sustainable investing, as evident from these statistics:
· Global flows into ESG funds have more than doubled compared to pre-pandemic levels, with an annual inflow rate of approximately 45%, according to Morningstar.
· Armando Senra, BlackRock's head of iShares Americas, predicts that ESG investments will become a $1 trillion category by 2030.
· Globally, one-third of all professionally managed assets, about $30 trillion, now adhere to ESG criteria.
· Between April and June 2020 alone, investors directed over $70 billion into ESG equity funds, highlighting the rapid growth and interest in this area.
· In 2021, 72% of U.S. adults expressed interest in sustainable investing.
· Bloomberg predicts that global ESG assets will exceed $53 trillion by 2025, accounting for over a third of the projected $140.5 trillion in total assets under management (AUM).
This underscores the significant role ESG investing is expected to play in the future of global finance. But which sector will attract the maximum investment from a sustainability lens? There is only one answer—technology. In this blog, Elaine Weidman-Grunewald explains the rising need for responsible AI in the context of ESG investment.
The Next Sustainability Frontier: Digital Transformation
The journey of technological connectivity has been remarkable. It took a century for telephones to connect a billion people, yet by December 2018, over half of the global population had internet access. The first chatbot, ELIZA, was created in 1967, and almost fifty years later, we had large language models (LLM) laying the foundation for programs like ChatGPT. We have more milestones to achieve in our quest for digital expansion, which will also present unprecedented opportunities for ethical and sustainable development:
1. Optimization and Innovation: Digitalization fosters optimization, creating new revenue streams and business models. It enables businesses to operate more efficiently and innovatively. For instance, IoT sensors and data analytics optimize production lines, reduce downtime, and improve quality control.
2. Transparency: With increased access to information, digitalization promotes transparency, allowing stakeholders to make informed decisions and hold entities accountable. The digitalization of government records has made it more convenient for citizens to access and manage their personal data online.
3. Decarbonization and Efficiency: Digital tools can drive decarbonization by improving material, energy, pricing, and logistical efficiencies. According to Renew, digital technologies could help reduce global greenhouse gas emissions by up to 15% by 2030 and boost the share of renewable energy in the global energy mix by up to 30% by 2030.
The potential for digital technologies is vast, and it is our duty to integrate them with sustainability initiatives. Emerging technologies like Web3, Artificial Intelligence (AI), blockchain, and decentralization are transforming traditional processes and increasing user participation. These innovations can accelerate sustainability goals, but we face the crucial challenge of mitigating the risks associated with digital transformation.
The Need For Responsible AI
Elaine has dedicated several years to understanding and addressing the risks associated with AI, machine learning, and data analytics. Her understanding reveals that AI technologies can inadvertently lead to significant issues like:
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Privacy Intrusion: AI systems can collect and analyze vast amounts of personal data, leading to privacy concerns.
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Discrimination and Bias: Algorithms can perpetuate existing biases and social inequalities, such as using zip codes in credit scoring, which may disadvantage low-income communities.
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Faulty Recommendations: Incorrect or biased recommendations can have severe consequences, particularly in sensitive areas like hiring or lending.
The above risks can severely impact the services that rely on AI technologies, which include:
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Credit Scoring and Banking: Algorithms can unfairly influence who gets loans.
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Hiring: Bias in recruitment tools can lead to the exclusion of qualified candidates.
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Insurance: AI can affect policy decisions and risk assessments.
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Content Moderation: Algorithms can censor or promote content in biased ways.
Accenture's 2022 Tech Vision research highlights the trust deficit in AI implementation - only 35% of global consumers trust AI deployment by organizations, while 77% believe companies should be accountable for AI misuse. This underscores the need for responsible practices to maintain stakeholder trust and prevent costly errors. By focusing on responsible AI, organizations can harness the benefits of technology while mitigating risks and fostering trust.
A Framework for Ethical AI and Risk Management
Organizations must prioritize ethical AI principles and risk management strategies before implementing the technologies. Here is what Elaine recommends companies must do to navigate the risky shores of AI:
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Establish Ethical AI Principles: Set up ethical principles surrounding the AI’s operations. Integrate them across all departments for a comprehensive approach to digitalization.
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Inclusive Risk Assessment: Train the AI to involve multiple perspectives and ensure the inclusion of relevant subject matter experts. The diversity in viewpoints can lead to a more thorough and accurate risk assessment.
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Incorporate Societal and Ethical Risks: Make the societal and ethical implications of data-driven solutions a core part of your company's risk framework. A proactive stance helps anticipate potential challenges and address them before they escalate.
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Transparency and Trust: Building stakeholder trust requires transparency. Companies should openly communicate how they are utilizing data. A significant 89% of consumers desire companies to take additional steps to protect their data, highlighting the importance of transparency.
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Explore Risk Identification Tools: Utilize methods and tools that can be integrated into frameworks to better identify and characterize data-driven risks. This integration aids in creating a robust system for managing potential threats.
The age of AI-driven services is coming, and nothing is wrong with it. Our collective responsibility is to ensure that AI meets our needs with minimal challenges. Adhering to the above recommendations can help organizations better navigate the complexities of AI ethics and risk management, fostering a trustworthy and secure environment for stakeholders and consumers.
About The Speaker
Elaine Weidman-Grunewald is a sustainability and social impact leader with extensive experience in technology and telecommunications. She co-founded the AI Sustainability Center and served as the Senior Vice President and Chief Sustainability Officer at Ericsson, focusing on sustainable business practices and corporate responsibility initiatives.
You can watch her full keynote at Keiretsu Forum here.
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