Taken from the April 2026 Marconi Society Executive Institutes Forum Report, we’re highlighting how participants focused on the pressing intersection of technology and energy, surfacing infrastructure sustainability and energy transformation from an operational challenge to a critical global priority. 

Energy Demand as a Strategic Constraint

Data center expansion, AI model training, and the proliferation of connected devices are driving energy consumption at a pace that existing grid infrastructure and energy policies were not designed to accommodate. Participants discussed the limits of continuing with current energy generation models and pointed to nuclear, fusion, and expanded solar as the main categories of alternatives that could support future AI growth. Across these ideas, the shared view was that sustaining AI growth will require both new energy sources and more serious planning around how generation capacity is developed and deployed.

Infrastructure Resilience and Physical Vulnerabilities

Digital resilience is only as strong as the physical systems that underpin it. Vulnerabilities in power grids, subsea cables, and national interconnection points represent compounding risks that are often addressed in isolation rather than as an integrated system. The consensus was made abundantly clear: physical and digital infrastructure must be viewed as a unified, interdependent ecosystem.

Security and resilience should be considered just as critically as efficiency. Highly centralized infrastructure can create attractive single points of failure, particularly when energy generation and computing resources are concentrated together. Participants noted that a more distributed model may potentially be better for resilience, even if it introduces tradeoffs in cost or efficiency.

Another important consideration was how efficiency gains need to happen across the full AI stack, not only at the model level. Hardware, packaging, system design, software, and operational practices were all discussed as areas where incremental improvements could produce meaningful aggregate impact. Sustainable AI growth will depend not just on building more power capacity but also on creating stronger incentives for efficiency, optimization, and resilience throughout the ecosystem.

The Role of Policy and Investment

The infrastructure and energy transformation agenda requires alignment across the private sector, government, and regulatory bodies. The pace of private investment in data centers and wireless infrastructure is outpacing public policy frameworks for energy planning, environmental impact, and grid modernization. Without proactive policy engagement, the sector risks creating infrastructure bottlenecks that could constrain technological progress.

Participants further explored the pace of AI expansion and whether the current rate of investment is sustainable. Deployment appears to be moving faster than historical computing efficiency trends, while usage remains supported by subsidized economics and large flows of capital. One concept the group suggested was “tokens per watt” as a more practical way to think about AI efficiency. Rather than measuring progress only through larger systems and higher throughput, participants reiterated the importance of understanding how much useful output can be produced per unit of energy consumed.

Ultimately, who should bear the cost of the supporting infrastructure? Participants underscored the need for AI companies and data center operators to contribute meaningfully to the costs of generation and grid upgrades associated with their growth. Grid modernization will likely require stronger public-private coordination, and future projects will depend in part on whether local communities believe the benefits and burdens are being shared fairly.

This session made clear that energy demand has become a strategic constraint for technological innovation. Data center expansion, AI model training, and the growing proliferation of connected devices are driving energy consumption beyond what existing grid infrastructure and energy policies were designed to support. As participants noted, to sustain our digital future, industry must look beyond current energy generation models and seriously consider transformative alternatives—including nuclear, fusion, and expanded solar capacity—to ensure infrastructure can keep pace with the demands of an increasingly connected and AI-driven world.

This is the work the AI Institute was created to advance. At the 2026 Marconi Awards Gala & Institute Forums this November 4-6 in San Francisco, global leaders from industry, academia, government, and civil society will convene to translate these complexities into concrete frameworks, partnerships, and standards while recognizing leaders championing innovative solutions.

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AI governance conversations are frequently framed either as technical compliance exercises or as abstract debates about future existential risk. In both cases, the people most affected by today’s AI systems are the ones least likely to have a seat at the table, especially those with little influence over how systems are designed or deployed.

Taken from the April 2026 Executive Institutes Forum Summary Report, we’re highlighting how participants considered building genuine trust in an era of unpredictable AI capabilities – shifting the paradigm from designing for humans to designing with them. By centering human-centered design, inclusive data stewardship, and institutional accountability, we can ensure that the technologies shaping our daily lives protect and empower the communities they serve, rather than treating them as secondary considerations.  

Who is the “human” in human-centered AI design? To answer this question, participants focused on AI trust through a human-centered design lens, challenging the default assumption of who counts as the “human” in technology design. Participants were asked to consider how do we move from designing for humans to designing with humans, embedding lived experience and participatory design into the technologies that shape daily life? Trust cannot be built only with system operators or enterprise customers; it must also include those whose information, opportunities, or outcomes are shaped by the systems, often without their active participation.

Participants repeatedly returned to the breadth of AI’s impact. Virtually everyone is directly or indirectly affected by AI systems, whether as users, data subjects, employees, or members of communities shaped by AI-enabled decisions. A particularly vivid example involved an online staffing marketplace breach in which highly sensitive applicant materials were exposed, including interview recordings, background-check information, and other personal data. The implications discussed went beyond a standard data leak: participants highlighted the risks created when voice samples, tax information, and identity data are combined into high-fidelity profiles that can later be exploited, risks that were taken on by particularly vulnerable people, those seeking employment. This case illustrates how trust is not an abstract principle; it is closely tied to data stewardship, security design, and the downstream effects of failure.

Human and Institutional Dimensions of Trust

Companies often invest heavily in training employees to recognize email spoofing and other traditional cybersecurity threats, while providing far less guidance on safe and appropriate AI tool usage. This gap becomes more serious when personal and professional uses of AI systems blur together, such as when employees use the same tools or environments for work research and sensitive personal questions. Many users do not understand how data is stored, separated, or reused across systems, making it difficult for them to make informed decisions about trust. This suggests that AI governance is not only a policy issue at the executive or regulatory level, but also an operational issue involving employee behavior, literacy, and everyday workflow design.

Policy and Governance Dimensions

Participants also explored the difficulty of governing AI because of its uneven and unpredictable capabilities. One concept that resonated was the “jagged frontier” problem: AI can perform at an expert level in some domains while failing in surprisingly basic ways in others. This unpredictability complicates oversight, because the boundaries of competence and failure are not always visible in advance. The issue became increasingly complex when considering whether governance should rely more on proactive testing, stronger certification mechanisms, and incentives for organizations to identify failure modes before public harm occurs. While some companies already have reputational incentives to reduce bias and hallucinations, the conversation suggested that market incentives alone are unlikely to be enough, especially when the effects of failure are distributed across people with little power to demand accountability.

The session closed with a practical challenge to participants: review their own organizations’ AI governance policies and ask two questions. First, do those policies genuinely represent the interests of all the populations affected by the organization’s AI-related activities? Second, if someone is harmed by an AI system connected to the organization, is there a meaningful path for redress, or only informal public complaint? This closing reframed AI governance as an institutional responsibility rather than merely a technical or theoretical issue, pushing participants to evaluate whether their policies are inclusive in design and accountable in practice. This is the work the AI Institute was created to advance.

At the 2026 Marconi Awards Gala & Institute Forums, global leaders from industry, academia, government, and society will convene to translate these complexities into concrete frameworks, partnerships, and standards while recognizing the innovators shaping what responsible AI looks like in practice.

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Artificial Intelligence (AI) may dominate today’s headlines, but it does not stand on its own. Models and compute often capture the spotlight, yet they are only part of the story. AI operates within a much larger system. And in that system, connectivity serves as the heartbeat that keeps everything moving and the nervous system that allows it all to work together.

Here is the way to think about it: AI needs data for training. Data relies on compute for processing. Compute in turn requires connectivity for data exchange and data transfer. When all three work together, AI reaches its full potential. Connectivity is more than a utility. In the AI-era, it is the lifeblood that sustains intelligence and the wiring that allows it to function seamlessly.

Looking Back: Networks as the Circulatory System

The history of the Internet underscores this point. Born as a research experiment in the 1970s, and 80s, early ARPANET links carried simple packets that enabled collaboration across labs. Data was scarce, compute rudimentary, and networks were narrowband, yet those early flows proved a new idea: intelligence could be shared across distance.

The 1990s brought the World Wide Web (WWW), and suddenly data was everywhere” with webpages, e-commerce transactions, streaming media. Search engines and recommendation systems flourished, but only because connectivity moved bits from servers to compute clusters. Without Wide Area Networking (WAN), information would have remained trapped in silos.

The 2000s layered on two revolutions: cloud computing and the smartphones. Cloud services delivered elastic compute, while smartphones put connectivity in billions of pockets. Social media, ride-sharing and mobile payments were not just software innovations; they were network-driven, requiring real-time coordination between devices, users and clouds.

In the 2010s, deep learning brought GPUs and massive models. But GPUs alone did not make AI practical. What elevated AI from an experiment to a global utility were networks: high-speed interconnects moving training data across distributed clusters, and mobile broadband delivering AI-powered services to billions of devices. The design clusters of connectivity: throughput, reach, latency, cost per bit, and reliability, were what allowed the organism to grow.

From Circulation to Coordination

Connectivity’s role has expanded from circulation (moving bits) to coordination (enabling reflexes).

The Internet of Things (IoT) in the 2010s highlighted this shift. Billions of sensors became the “nerve endings” of the digital world, streaming telemetry from homes, factories, vehicles and cities. Yet most IoT devices were passive: they sensed and reported but did not act in real-time. This exposed a gap. Connectivity was not just circulation but also neural wiring, essential for interpretation and coordinated response.

Emerging concepts such as the Internet of Senses aim to close this loop. By fusing sensing and communications through ISAC (Integrated Sensing and Communications), networks can become context-aware fabrics, transmitting what they detect in real-time. Multisensory technologies: haptics, digital olfaction, even brain-computer interfaces (BCI), extend communication beyond sight and sound, turning the Internet into a medium of experience rather than just information.

But perception without reasoning is incomplete. Here, AI agents emerge as the next step. Unlike IoT endpoints, agents are not passive. They perceive, reason and act. Digital agents such as copilots, workflow orchestrators, trading algorithms, live entirely in software. Physical AI agents like autonomous vehicles, drones, industrial robots, bring intelligence into the physical world. Both require connectivity not just as a circulatory system but also as a nervous system: wiring cognition across devices, edge nodes and clouds.

Why Connectivity Defines AI’s Future

Today’s AI ecosystem demonstrates this dependency vividly.

At one extreme, foundation models contain trillions of parameters and run across thousands of accelerators in globally distributed data centers. Training such models requires high-bandwidth, low-latency interconnects like InfiniBand, Ethernet with RDMA, or emerging optical fabrics. Without these, multi-week training runs would be impossible.

At the other extreme, edge devices like smartphones, industrial sensors, medical wearables, now carry powerful NPUs and GPUs for on-device inference. Apple’s Neural Engine, Qualcomm’s AI Engine and Google’s Tensor Processing Units (TPUs) in phones enable AI agents to run locally. But their usefulness depends on staying in sync with the cloud and peers through reliable connectivity.

What ties these extremes together is connectivity as fabric, spanning the system end-to-end:

• Within data center: Ultra-fast interconnects bind GPUs, TPUs, and accelerators for distributed model training
• Across regions and continents: High-capacity optical backbones move vast datasets and inference outputs globally.
• At the edge: Wired and wireless access networks bring intelligence to people, machines and environments
• Beyond terrestrial limits: Satellites and high-altitude platforms extend reach to underserved or remote regions.

This layered fabric ensures that data flows to compute when needed, compute delivers insights back in time, and intelligence emerges as a system rather than isolated silos. Without connectivity, compute is stranded. With connectivity, intelligence becomes collective: distributed across clouds, edge, and devices worldwide.

Technical Challenges Ahead

If connectivity is the heartbeat and nervous system of the AI-era, then making it work at scale presents six major technical challenges.

1. Ultra-low latency and determinism: AI tasks such as autonomous driving, robotic surgery and industrial automation require sub-millisecond responsiveness with predictable guarantees. While 5G URLLC is a first step, AI-native networking must integrate sensing, scheduling and compute coordination far more tightly to ensure end-to-end determinism and real-time decision making.
2. Bandwidth, fabrics and data movement: Training trillion-parameter models produces exabytes of traffic, and today’s Ethernet based interconnects and memory hierarchies cannot keep pace. Accelerators scale faster than I/O, leaving compute cycles stalled waiting for data. Breaking this bottleneck will require co-packaged optics, silicon photonics, rack-scale integration, and memory disaggregation (e.g., CXL) to deliver multi-terabit-per-second throughput per node and move data as efficiently as it is processed.
3. Resilience and Security: As AI workloads become critical infrastructure, connectivity fabrics must ensure fault tolerance and adversarial robustness. Multipath routing and self-healing meshes provide continuity under failure, while zero-trust models and AI-driven anomaly detection secure operations across cloud, edge and devices. Supply-chain integrity and quantum-resistant cryptography will be essential to sustain trust at global scale.
4. Energy Efficiency: From hyperscale data centers to radio access networks, connectivity is energy intensive. AI-Native networks must be designed with energy proportionality and sustainability in mind, ensuring performance without compromising sustainability goals.
5. Orchestration and Interoperability: Just as TCP/IP created a common foundation for the internet, the AI era offers a chance to establish open protocols for agent identity, inter-agent communication, and workload orchestration. Today’s orchestration tools like Kubernetes for Cloud, MANO for NFV, O-RAN RIC for RAN, operate in silos. Moving forward, AI-Native systems can unity these into an end-to-end framework that spans cloud, edge, and devices, ensuring seamless interoperability and preventing fragmentation.
6. Trustworthy AI integration: As networks themselves become AI-Native, ensuring the reliability, fairness, and explainability of AI-driven decision is paramount. From spectrum allocation to closed-loop control, bias or opaque inference could undermine trust. Embedding verification, validation and continuous monitoring of AI models into network operations will be critical.

Lessons from History

History shows that breakthroughs in compute alone do not unlock progress: It is connectivity that turns isolated advanced into global transformations. The supercomputers of the 1990s were powerful but niche, and only when they were networked through the Internet, did the intelligence begin to scale across the world. The smartphone’s true success also came not from its hardware alone, but from the power of always-on connectivity that enabled entire ecosystems of applications and services.

AI stands at a similar moment today. Compute will continue to advance, but its full impact will only be realized when paired with robust, open and ubiquitous connectivity. With this foundation, AI can grow into a planetary-scale utility: resilient, inclusive and transformative for society.

Closing the Loop

When you step back, the patterns is striking:

• Data without compute is meaningless
• Compute without connectivity is stranded
• AI without both is nothing more than an idea

Connectivity has been there from the beginning: carrying packets, enabling mobility, linking machines. In the AI-era, it ensures that intelligence flows freely rather than remaining locked in silos. It synchronizes training across data centers, distributes inference to the edge, and coordinate agents acting in the physical world.

In summary, connectivity is not just the foundation of AI. It is the pulse that keeps the system alive and the nervous system that makes it intelligent.

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The Early Days

Federico opened the conversation by taking us back to the early days when groundbreaking inventions like the 4004 microprocessor he developed and Vint Cerf’s TCP/IP protocol were conceived. The rapid advancement of microprocessor technology impacts various applications, from calculators to control systems. The work to shrink transistors and double processing power over decades has led to the current era of trillion-transistor chips.

Vint recounted how the original purpose of creating a communication system for the Defense Department eventually evolved into a commercial and global phenomenon. He stated, “In 1983… there might have been 400 computers on the system.  Today, there are billions of machines on the network…” The rapid proliferation of search engines and the monumental impact of smartphones has brought unprecedented levels of connectivity and access to the internet.

Marty took the conversation even further back to 130 years ago to describe the work of Heinrich Hertz, and how the early field of electromagnetism research led to his innovations. Technological progress drove people to want to be more connected. He shared, “My colleagues and I at Motorola could see that the world was ready for personal communications.  People didn’t want to call a car. They didn’t want to call a house as they had been doing for 100 years. They wanted to call a person, and we created the very first personal portable telephone”.

Technology Meets Humanity

The Internet has democratized access to information. People now can access vast amounts of knowledge instantly, enabling self-directed education, research, and informed decision-making. This empowers individuals to become more knowledgeable and informed citizens.

The Internet of Things will improve the human experience by allowing us to be more productive. Artificial intelligence will analyze your behavior and find the right apps to make your life better. IoT can improve the efficiency of energy production and transmission and can help reduce emissions.

We’re truly on a precipice of exciting impacts of cellular technology beyond the ubiquity of the cell phone. This technology will improve the quality of life worldwide. For example, the sensors we use in phones can also measure a person’s pulse and blood pressure. We have the potential to sense a disease before it harms an individual with the goal of truly eradicating disease.

To tackle this complex issue, we need a multidisciplinary approach. It’s not just about the code or algorithms; it’s about understanding human behavior, societal norms, and the delicate balance between individual freedoms and collective responsibilities. Sociologists, psychologists, anthropologists, and legal experts play a crucial role in this process. They help us explore how we accept certain behavioral norms in exchange for the benefits provided by our systems, essentially defining the modern social contract.

Challenges in Academia 

Advancement in academia typically relies on metrics like the number of publications and citations, grants obtained, and teaching evaluations. While these metrics reward productivity and quality, they may not adequately recognize or reward true innovation, which might not manifest in immediate publications or measurable outcomes.

To foster innovation in academia, there is a growing recognition of the need to reform these incentive structures. Some initiatives include promoting open science, recognizing alternative forms of research impact, beyond publications and citations, providing dedicated support for interdisciplinary research, and encouraging collaboration with industry,

Vision for the Future

“There is no doubt in my mind that we could increase the capacity of the radio spectrum over the next 50 years by another million times. And that’s a good thing because we are thinking of new ways of using the collaboration of people to solve all of the big problems in the world.” – Marty Cooper

“We need to become smarter in the way we use technology. We need to be more creative. In the past, we had to brute force the entire system […] that strategy no longer works, so we have to become clever.” – Federico

“What we need as a society is to learn how to adapt to a more positive environment. Pay attention to our problems like global warming. Pay attention to smarter use of spectrum. […] You learn sharing when you’re in kindergarten. We should remember those lessons” – Vint Cerf

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Our expert panel shared their viewpoints about Internet business models and ideas of fair and equitable growth.

Lili Gangas set context for this critical conversation about new models and new governance for the tech sector because of its importance to the overall economy.  Employing millions of workers, this sector pays well and is creating a large part of the wealth and economic engine in the US and in other countries.

Yet we also see downsides, including the under investment in capital deployment l to  under-repesented  entrepreneurs, facial recognition bias and  the impact that has on policing surveillance, and a lot of the mis- and  disinformation that we continue to see in our everyday conversations through some of these platforms. Many of these issues are systemic and their harms are felt very disproportionately, depending on who you are.  Especially when we look at the impacts of technology  from a racial justice perspective. 

We need to question how we move forward and the key problems that we need to tackle.  How did we get here? Where do we go forward? Especially what are the key problems that we need to tackle? 

Siavash Alamouti:  Engineers decided to pay for services like search and social media with ads.  The byproduct is that data brokers take our data, resell it in the market and push more ads, or rely on a two-sided business model where they become a connector between people who want to buy and those who want to sell.   The data selling models have been disastrous because they focus on getting eyeballs by showing information that will keep people on the site.  This leads to the extreme getting more extreme.  This drives teen suicides, polarization, attraction to ISIS other negative outcomes.  The two-sided models are replacing efficient industries, such as taxis, with unprofitable monopoly businesses.  In fact, ride costs have risen by 92% and labor issues abound in the taxi market. We need to consider business what the business model is for any new technology before we get engaged.

Fran Berman:  We have not necessarily gone wrong, but we have not gone right.  Innovation lives in the private sector and business models are built around making profits.  A lot of the digital technologies that started out as private sector business models are now critical infrastructure.  During the pandemic, we did everything on zoom, for example.  That infrastructure is not supported or protected as public infrastructure is.  It is the public sector’s job to support the public interest.  Public infrastructure should be safe and non-exploitive, even to the exclusion of being innovative.  When public infrastructure lives in the private sector, it is subject to profit motive.  We need other public infrastructure models like public broadcasting, but we do not have those analogs in social media or other parts of the digital world.  To think differently, higher ed needs to teach tech literacy, social responsibility and soft skills to help make the world a better place.

Dawn Song:  We have huge problems in this area.  Data is a key driver for the modern economy and its importance will only increase.  A lot of this data is very sensitive.  Users have lost control of their data and how it is used.  Even if data has been anonymized, that is not sufficient to protect user privacy.  Businesses are building very detailed profiles about consumers and monetizing them. Businesses are also having a difficult time.  They are grappling with scale data breaches and a lot of valuable data is lost in data silos due to privacy concerns.  ​These issues will only get worse over time.  It is not too late to fix the issue, but we really need to act now.  We have an urgent need for a framework to build a responsible data economy.  This framework has three key principles 1) establish and enforce data rights, 2) enable fair distribution of value from data and 3) ensure efficient data use to maximize social welfare and economic efficiency.  We need three key components for this framework: 1) technical solutions 2) business incentives 3) legal frameworks – GDPR is just a start.

Ian Veidenheimer:  I focus on the business models and physical infrastructure of the network, such as broadband.  There is simply not enough broadband in the US.  The business model relies on making money from selling services to end users and end users have different margins.   Costs are prohibitive in rural areas and public housing.  Until recently, the federal government was not willing to subsidize broadband infrastructure.  There is also a lack of market competition among providers.  This leads us to estimates that 20-120M people do not have access to broadband.  Fortunately the government is investing $65B in broadband infrastructure though the true cost to bridge the digital divide is estimated to be $200B.  We’ll need public / private partnerships with help from philanthropies to build the new public infrastructure.

Madisen Obiedo: At Welcome Tech, we serve the latinx immigrant community with a focus on people of Mexican descent.  The digital divide is very real among our 4M users, who are calling to get access to broadband for their kids to attend school.  About 60% of our user base is Spanish dominant and 30% do not have connectivity.  We provide a concierge and wrap around services to support this community that has been purposely excluded from the connected world.  For example, we have a program called ACP Para Me to help customers get home wifi.  Welcome Tech needs to be good stewards of our customers’ data and make services usable for them. 

Panelists went on to discuss strategies and models that are working and that we need more of, how to bring missing voices into the room and calls to action for investors.  Click here for a replay of this fascinating conversation.

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Our expert panel shared their viewpoints on the topic of how we can harness big data to serve society.  

Danielle Davis, Tech and Telecom Policy Council for the Multicultural Media Telecom and Internet Council (MMTC) – moderator

We’re going to discuss how digital transformation, automation and globalization have sparked radical shifts in society and have given rise to a new economy, driven by big data and the Internet of Things. The expansion of this digital economy has placed big data, machine learning (ML), artificial intelligence (AI), and data science at the center of the debate about the future of digital inclusion 

Internet access is poised to reach hundreds of millions of new individuals over the next decade, bringing services and opportunities to historically excluded populations. Big data offers great potential for data-driven decision making to inform individuals, businesses, and governments, but it will be crucial to ensure transparency, equity, and trust. 

Algorithmic decision-making increasingly affects everyday life and the benefits must be weighed against the potential to codify and amplify existing biases, as well as the potential for fraud or the invasion of privacy.  This raises the question that we’re going to be discussing today. How can we properly harness data and its value to maximize individuals’ and society’s welfare?

Jordana Barton-Garcia, Senior Fellow, Connect Humanity

We only get to digital equity in the big data economy when we have equity at the most basic level with fiber-based networks for all, including rural, BIPOC and low income communities.  Fiber-based broadband enables 4G and the real 5G – intermittent signals / low latency – which enables the Internet of Things that support big data and AI for all. We cannot miss this moment. We must engage everyone in key decisions and not lose precious time focusing on things like minimum outdated speeds. We have the local ISPs and community partners who are rolling up their sleeves and are ready to get underserved communities to where they really need to be.  

Broadband is critical because it is an intersecting issue – it provides access to healthcare and education, the ability to start and grow a business and the opportunity to be part of the labor market in the digital economy.  Broadband is key to upward mobility.  We used to be able to enter the middle class with jobs in manufacturing and the retail industry.  Now our entry-level jobs are more digitally focused and require efforts like training young people in designing, building and maintaining networks, coding, IT and the like. Broadband also helps us with both the supply and demand side of workforce development.  For example, we cannot attract business to the border area in Texas because we do not have the fiber infrastructure.  At the same time, our country faces a shrinking middle class because we are not preparing people for the labor market.

My hope for the future of digital equity is that we have ubiquitous fiber-based networks for all communities and that communities have brought solutions to the table to help them create the right types of partnerships and networks for their needs.  When we make these kinds of investments in local communities, we create an inclusive economy and opportunities for underrepresented groups to use, create, and own assets in the digital economy. That is how we address wealth gap and wipe out persistent poverty.  

Sarah E. Chasins, Assistant Professor of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley

My work is about ensuring that the power of programming computers and automating tasks is available to everyone, particularly teams that are doing very important work with low resources.  Being able to program computers lets people very quickly do some tests that would be tedious and time consuming.  I’m often working with teams who are trying really hard to change society for the better, but they have to do these automatable tasks very slowly and often manually with volunteer labor or using folks who are already stretched thin with their other responsibilities. I’d love for these teams to have the same access to programming and automation that high resource teams already have today. 

For example, we can help defense attorneys sift through police misconduct data in order to identify people who have previously lied on the stand and be able to make the case that those individuals should not be testifying against their clients. 

My hope for the future of digital equity is that we can give tools to the people who are already working to support vulnerable communities.

Laura Chioda, Director of Research at the Institute for Business and Social Impact (IBSI), at UC, Berkeley

Billions of people interact with the digital economy everyday.  Yesterday’s AI was prescriptive, like computers playing chess.  Today’s AI is interactive, accounting for nearly every variation in human behavior.  The digital economy has created huge amounts of information and we need to be able to process that data in a productive way that produces insights without jeopardizing privacy and security. 

AI can produce social benefits and improve lives in areas like weather tracking, determining where ICU beds are available and improving supply chain logistics.  AI can improve lives and financial inclusion. For example, I came to the US with no credit history.  AI can help us create gender-oriented models to account for the fact that women typically repay debt at higher levels than men.  This is a great example of the power of data to level the playing field. If we do not assess enough data for women, we will have inaccurate information about them and deny them opportunities that should be theirs.

My hope for the future of digital equity is that we use AI and machine learning to provide more opportunity and better options for people who are under-resourced and under-priviledged.

Tiffany Deng, Chief of Staff and Program Management Lead for Google’s Research Center for Responsible AI and Human-Centered Technology

I focus on technology from a responsibility perspective.  Just think about how ubiquitous things like AI are today and how we interact with it in so many different facets of our everyday life. It’s so important to ensure that there’s balance and that we’re thinking about the power that AI holds and the responsibility we have to understand how it affects different communities disproportionately.  We need to understand the toll it can take and have safeguards and guide rails there to ensure that technology is not creating an outsized burden on under-represented communities.  

AI is about using systems to mimic human behavior.  Machine learning (ML) is a subfield of AI that involves having systems that collect lots of data that is translated into models to predict behaviors.  This is where the term big data comes in and it’s important because it informs everything from credit worthiness to recommendations to college admissions and screening for job applications.  Because of this pervasive reach, people need control over their personal data and how it is used in all aspects of their lives.

The representation in the room where decisions are being made and models are being built is critical in bringing in diverse perspectives.  For example, if a company is deciding where to find new employees and there is not base equity in the room, AI can perpetuate skewed data.  

My hope for the future of digital inclusion is that we are successful with Google’s unified speech model, which will give people everywhere the opportunity to hold the power of the Internet in their hands.

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This decadal survey, the first of its kind, will challenge researchers to develop practical, actionable solutions to help bring the next billion people online.

Led by Chairman of the Board Vint Cerf, the decadal challenge aims to build consensus within the research community around the priorities for the coming decade. Join us as we discuss the challenges within artificial intelligence and machine learning.

This event is free and does not require registration. We hope to see you there!

Join the call: meet.google.com/scu-gdjj-ix

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