+ THREE INSIGHTS FOR THE WEEK |
1. As the use of artificial intelligence has grown, so have concerns about the technology’s risks, including bias, data breaches, and misuse.
Given the scope and seriousness of the risk climate, a team of researchers created the AI Risk Repository, a database of 777 risks posed by AI, categorized by cause and risk domain. The project, led by MIT FutureTech, aims to provide industry, policymakers, and academics with a shared framework for monitoring and maintaining oversight of AI risks.
The repository can also aid organizations with their internal risk assessments, risk mitigation strategies, and research and training development. It has three components:
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The AI Risk Database, which captures 777 different risks from 43 documents, with quotes and page numbers included. It will be updated as new risks emerge.
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The Causal Taxonomy of AI Risks, which classifies how, when, and why such risks occur, based on their root causes. Causes are broken out into three categories: entity responsible (human or AI), the intentionality behind the risk (intentional or unintentional), and the timing of the risk (pre-deployment or post-deployment).
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The Domain Taxonomy of AI Risks, which segments risks by the domain in which they occur, such as privacy, misinformation, or AI systems safety.
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2. From liquid green fuels to air-cooled nuclear batteries, here are some of the new technologies aimed at reducing carbon emissions that were featured at the recent 2024 MIT Sustainability Conference:
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3D Architech offers a lithography-based metal 3D printing system that optimizes the design of heat-sink cooling systems for data centers.
- Allonnia uses proteins to recover rare earth elements such as copper, lithium, cobalt, and nickel from mine-impacted water.
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Emvolon offers a portable, modular chemical plant that can convert methane emissions into green methanol, for which there is an established market.
- Femto Energy is working with established nuclear reactor companies to develop a transportable, air-cooled nuclear battery that can operate for three to 10 years without refueling.
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3. Managers achieve better outcomes when they prioritize collaborative decision-making over powers of persuasion.
In “Why Influence Is a Two-Way Street,” published in MIT Sloan Management Review, leaders from BDO Management Consulting make the case that morale and productivity are enhanced when employees are persuaded that a plan or decision makes sense; these both suffer when people feel pressured by “sales tactics” from their managers to agree with a course of action.
The associates outline five strategies that help leaders foster an approach to influence that emphasizes joint problem-solving across their organizations:
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Expand job definitions and responsibilities so that they provide guidelines for cross-functional engagement.
- Replace traditional influence training with training focused on collaborative influence.
- Engage in cross-functional goal-setting and alignment of incentives.
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Rethink criteria for promotions to management and executive leadership, prioritizing collaborative behaviors that drive organizational performance, such as a willingness to embrace dissenting views, self-skepticism, and openness to conflicting perspectives.
- Model behaviors of collaborative influence.
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How to redesign work for the age of AI
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Leaders from Google, ING, and Capital One advise avoiding the “gold rush mentality” around artificial intelligence and following these steps instead.
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Across industry sectors, there’s growing excitement — and apprehension — about how roles and responsibilities will be shaped by artificial intelligence. With much at stake, organizations need to strike a balance between the race to innovate and a more thoughtful strategy for redesigning work patterns for AI.
“Generative AI shouldn’t go hand in hand with a gold rush mentality,” said Prem Natarajan, chief scientist and head of enterprise AI at Capital One, speaking on a panel at the 2024 MIT Digital Technology and Strategy Conference. “If this technology is truly enduring, it’s important to take your time. You have to build the scaffolding to bring everybody along.”
A comprehensive strategy for effectively redesigning work for AI should include the following, panelists advised:
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- Modernizing the data and technology infrastructure.
- Upskilling and educating talent.
- Embracing an ecosystem approach. Don’t work in isolation; instead, collaborate with a broad set of technology partners.
- Taking a test-and-learn approach.
- Keeping humans in the loop.
- Thinking beyond the hype. Rather than operating from a sense of urgency, calibrate AI development and deployment to the overall readiness of the company to embrace new ways of working.
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