Artificial Intelligence for Sustainability Innovation & Technology

Pursuing AI Responsibly:
A Business Technologist Perspective

SustainableIT.org has developed a framework and set of principles to guide responsible AI application deployment. While the guidance is uniquely informed by the business technologist perspective, it is pertinent to leadership from the boardroom to the C-suite in every industry.

Brand Provided | By Rick Pastore | Published 2 weeks ago | About a 6 minute read

Image: Google DeepMind

While it may be challenging for near-term generative AI (GenAI) use cases to live up to the hype of the past two years, the consensus is that the technology is a potential game-changer for business innovation, creativity, personalization, efficiency and new business models. It is also being touted — somewhat ironically, given its intensive use of resources — as a solution to many challenges related to climate change and sustainability.

AI’s risks and challenges — ethical, social and environmental — have been left out of the business hype for these tools. The risks have recently gained the attention of regulators and global institutions including the European Commission, White House and United Nations; but they’re still not front and center among business leadership and typical AI users. This has generated alarm among information technology (IT) executives, who are sensing a pattern with which they are all too familiar.

Like many widely hyped, new technologies (cloud computing, IoT, blockchain and robotic-process automation come to mind) GenAI has been hastily adopted — often without an enterprise strategy, business case or governing ruleset. According to the “Responsible AI” section of Info-Tech’s Tech Trends 2024 report, 35 percent of surveyed companies deploying AI lacked formal AI governance guidelines, and less than a third conducted AI impact assessments. Such shortcomings typically lead to disappointing results, costly or embarrassing misuses of the technology, and unacceptable risk.

It has typically been up to IT organizations to bring order to innovation chaos — ensuring that technology scales efficiently, securely, with proper integration, and ongoing monitoring. But compared to previous technology waves such as the cloud, the stakes for AI are higher. Among the business risks of poorly governed AI are the following:

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Undermined ethics: The use of GenAI’s creative abilities raises rights and ownership dilemmas for intellectual property and data. The misuse of GenAI, for deep fakes and other forms of misinformation, can cause significant business and societal harm.
Exacerbating the digital divide: Without broad inclusivity and equitable access to AI applications and their benefits, the technology could widen society’s digital divide.
Loss of integrity: AI decisions are susceptible to inaccuracies and discriminatory outcomes due to biases in data and prompts — aka “hallucinations.” Low-quality or biased outputs can harm business reputations and erode customer and institutional trust.
Resource intensity: The energy and resource use by the AI industry, including chip manufacturing and data center facilities, is staggeringly high.
- Since 2012, the most extensive AI training runs have been using exponentially more computing power, doubling every 3.4 months, on average.
- OpenAI’s GPT-3 training is estimated to have used 1.3 gigawatt-hours of energy (equivalent to 120 average US households’ yearly consumption) and generated 552 tons in carbon emissions (equivalent to the yearly emissions of 120 US cars).
- A user’s prompt or query on ChatGPT uses 10x the power of an equivalent traditional Google search.
Other societal risks: AI competes with agriculture and municipalities for finite energy and water resources. The recent industry push for nuclear power comes with its own long-term social and environmental risks:
- Job displacement: While AI can augment human work, there is also concern about large-scale job displacement — particularly in industries where routine tasks are easily automated.
- Security and privacy breaches: AI can be exploited for malicious purposes, such as creating fake identities or generating harmful content. Use of data to train and inform AI algorithms may inadvertently violate privacy laws or regulations.

The IT executive members of SustainableIT.org — a non-profit professional association dedicated to driving sustainability through technology — want to help businesses avoid these negative impacts while maximizing the transformational benefits of their AI deployment. To that end, in September, they developed and published a framework and set of principles to guide responsible AI application deployment. While the guidance is uniquely informed by the business technologist perspective, it is pertinent to leadership from the boardroom to the C-suite in every industry, and our goal is that it will be used for education.

The framework offers a simple yet comprehensive model that incorporates three stages and nine principles:

Stage 1 – Reflect

Consider intended uses and desired outcomes of AI applications — assessing potential positive and negative impacts to business stakeholders, strategies, goals and commitments.

Related principles:

Risk due diligence: AI applications are thoroughly analyzed before deployment at scale for their risk materiality to — and implications for — business operations, policies, compliance, goals and strategies.
Sustainability due diligence: AI applications are thoroughly analyzed before deployment at scale for current and long-term implications for ESG commitments, policies and regulations.
Ethical usage: AI application deployment and use are monitored for alignment to and compliance with the organization’s ethical standards and business values (e.g., equity and inclusion, nondiscrimination, transparency, safety).

Stage 2 – Reframe

Redevise governance rules, processes, roles and skill sets — as well as enterprise operations and architecture — to maximize AI benefits and avoid negative impacts.

Related principles:

Data optimization: Data used in AI applications are appropriate, transparent, secure, privacy compliant, consensual and as unbiased as possible — supported by appropriate data governance.
Trustworthy outcomes: Results, recommendations and decisions made or informed by AI applications are fair, reasonable, explainable, accurate and cause no harm to human health, safety or fundamental rights.
AI literacy: AI application deployment coincides with development of users’ understanding of AI operations, limitations and risks; and the knowledge to apply AI appropriately and effectively in their roles.

Stage 3 – Reimagine

Conceptualize new business applications, processes and experiences uniquely suited to AI’s ability to augment human capabilities through collaboration and automation.

Related principles:

Human first: AI deployment prioritizes enhancement and augmentation of existing jobs/roles, with upskilling and prioritized redeployment of displaced workers.
Inclusive benefits: The benefits of AI are equitably applied to, accessible and leveraged by the broadest range of targeted stakeholders; and do not intentionally or unintentionally exclude disadvantaged groups.
Responsible innovation: AI-dependent innovation goals and outcomes are subject to the same governing criteria for risk, sustainability and ethics that are applied to AI usage.

Join the change

This Responsible AI Framework is only a preliminary step. SustainableIT.org has formed a Responsible AI Working Group — open to all interested organizations — with the mission to inform and equip organizations worldwide with guidance and tools to govern the efficient, secure and sustainable implementation of AI. The group will research, curate and adopt the best existing tools and create new resources to fill gaps in areas including AI literacy; data confidentiality, integrity and accessibility; and AI cost-benefit models. Its output will be shared with global businesses, institutions and executives from all business functions; and will be provided to the United Nations to inform the Global Digital Compact and AI For Good initiatives.

It is imperative to establish or elevate responsible governance for GenAI, and organizations should turn to their IT leaders to drive it. Then, AI may indeed live up to its hype — transcending the boundaries of traditional computing speed and complexity to help humans create outcomes barely imaginable today.

Rick Pastore

Research Principal, SustainableIT.org

Rich Pastore develops and delivers research-based insights, tools, case histories, and other content tailored for IT leaders driving ESG strategies for their functions, enterprises and industries. He has over 25 years of experience working with CIOs and their teams to apply thought leadership and best practices to maximize business value from information technology.

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