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We-Robot-2021

Author Archive | We-Robot-2021

On the Practicalities of Robots in Public Spaces

Cindy Grimm

Cindy Grimm and Kristen Thomasen will present their paper, On the Practicalities of Robots in Public Spaces, on Friday, September 24th at #werobot 2021. Edward Tunstel will lead the 1:45pm – 3:15pm panel on Field Robotics.
There has been recent debate over how to regulate autonomous robots that enter into spaces where they must interact with members of the public. Sidewalk delivery robots, drones, and autonomous vehicles, among other examples, are pushing this conversation forward. Those involved in the law are often not well-versed in the intricacies of the latest sensor or AI technologies, and robot builders often do not have a deep understanding of the law. How can we bridge these two sides so that we can form appropriate law and policy around autonomous robots that provides robust protections for people, but does not place an undue burden on the robot developers?

Kristen Thomasen

This paper proposes a framework for thinking about how law and policy interact with the practicalities of autonomous mobile robotics. We discuss how it is possible to start from the two extremes, in regulation (without regard to implementation) and in robot design (without regard to regulation), and iteratively bring these viewpoints together to form a holistic understanding of the most robust set of regulation that still results in a viable product. We also focus particular attention on the case where it is not possible to do this due to a gap between the minimal set of realistic regulation and the ability to autonomously comply with it. In this case, we show how that can drive scholarship in the legal and policy world and innovation in technology. By shining a light on these gaps, we can focus our collective attention on them, and close them faster.

Edward Tunstel (moderator)


As a concrete example of how to apply our framework, we consider the case of sidewalk delivery robots on public sidewalks. This specific example has the additional benefit of comparing the outcomes of applying the framework to emergent regulations. Starting with the “ideal” regulation and the most elegant robot design, we look at what it would take to implement or enforce the ideal rules and dig down into the technologies involved, discussing their practicality, cost, and the risks involved when they do not work perfectly.

Do imperfect sensors cause the robot to stop working due to an abundance of caution, or do they cause it to violate the law or policy? If there is a violation, how sure can we be that it was a faulty piece of technology, rather than a purposeful act by the designer? Does implementing the law or policy mean a technology so expensive that the robot is no longer a viable product? In the end, the laws and policies that will govern autonomous robots as they do their work in our public spaces need to be designed with consideration of the technology. We must strive for fair, equitable laws, that are practically and realistically enforceable with current or near-future technologies.

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Smart Farming and Governing AI in East Africa: Taking Gendered Relations and Vegetal Beings into Account

Jeremy de BeerLaura FosterChidi OguamanamKatie Szilagyi, and Angeline Wairegi will present their paper, Smart Farming and Governing AI in East Africa: Taking Gendered Relations and Vegetal Beings into Account, on Friday, September 24th at #werobot 2021. Edward Tunstel will moderate the 1:45pm – 3:15pm panel on Field Robotics.

Laura Foster


Robots are on their way to East African farms. Deploying robotic farm workers and corresponding smart systems is lauded as the solution for improving crop yields, strengthening food security, generating GDP growth, and combating poverty. These optimistic narratives mimic those of the Green Revolution and activate memories of its underperformance in Africa. Expanding upon previous contributions on smart farming and the colonial aspects of AI technology, this paper investigates how AI-related technologies are deployed across East Africa.

Edward Tunstel (moderator)

Chidi Oguamanam


The creation of AI algorithms and datasets are processes driven by human judgements; the resultant technology is shaped by society. Cognizant of this, this paper provides an overview of emerging smart farming technologies across the East African region, situated within contemporary agricultural industries and the colonial legacies that inform women’s lives in the region.

Angeline Wairegi


After establishing the gendered implications of smart farming as a central concern, this paper provides rich analysis of the state-of-the-art scholarly and policy literature on smart farming in the region, as well as key intergovernmental agricultural AI initiatives being led by national governments, the United Nations, the African Union, and other agencies. This enables an understanding of how smart farming is being articulated across multiple material-discursive sites—media, government, civil society, and industry.

Katie Szilagyi

What becomes apparent is that smart farming technologies are being articulated through four key assumptions: not only techno-optimism, as above, but also ahistoricism, ownership, and human exceptionalism. These assumptions, and the multiple tensions they reveal, limit possibilities for governing smart farming to benefit small-scale women farmers. Using these four frames, our interdisciplinary author team identifies the key ethical implications for adopting AI technologies for East African female farmers.

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Robots in the Ocean

Annie Brett

Annie Brett will present her paper, Robots in the Ocean, on Friday, September 24th at #werobot 2021. Edward Tunstel will lead the 1:45pm – 3:15pm panel on Field Robotics.

Academics (and particularly legal academics) have not paid much attention to robots in the ocean. The small amount of existing work is focused on relatively narrow questions, from whether robots qualify as vessels under the Law of the Sea to whether robotic telepresence can be used to establish a salvage claim on shipwrecks.

This paper looks at how two major robotic advances are creating fundamental challenges for current ocean governance frameworks. The first is a proliferation in robots actively altering ocean conditions through both exploitative alteration, such as deep sea mining, and alteration with conservation goals, such as waste removal. This is best illustrated by The Ocean Cleanup, who defied warnings from scientists in deploying an ocean waste capture prototype that became irreparable merely six months into its voyage. The second is in observational robots that are being used, primarily by scientific and defense entities, to further understand of ocean ecosystems and human activities in them.

Edward Tunstel (moderator)

Annie Brett focuses on the regulatory grey area of international law implicated by robots with the capacity to actively alter ocean conditions. She also focuses on analogues in terrestrial environmental law and climate geoengineering literature to propose a mechanism for regulating robotic interventions in the ocean. Specifically, she argues for a modified form of environmental impact review that attempts to strike a balance between allowing innovation in ocean robots and providing a measure of oversight for interventions that have the potential to permanently alter ocean ecosystems.

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Being “Seen” vs. “Mis-seen”: Tensions Between Privacy and Fairness in Computer Vision

Alice Xiang

Alice Xiang will present her paper, Being “Seen” vs. “Mis-seen”: Tensions Between Privacy and Fairness in Computer Vision, on Friday, September 24th at 11:30am at #werobot 2021. Daniel Susser will lead the discussion.

The rise of AI technologies has caused growing anxiety that AI may create mass surveillance systems and entrench societal biases. Major facial recognition systems are less accurate for women and individuals with darker skin tones due to a lack of diversity in the training datasets. Efforts to diversify datasets can raise privacy issues; plaintiffs can argue that they had not consented to having their images used in facial recognition training datasets.

This highlights the tension that AI technologies create between representation vs. surveillance: we want AI to “see” and “recognize” us, but we are uncomfortable with the idea of AI having access to personal data about us. This tension is further amplified when the need for sensitive attribute data to detect or mitigate bias is considered. Existing privacy law addresses this area primarily by erring on the side of hiding people’s sensitive attributes unless there is explicit informed consent. While some have argued that not being “seen” by AI is preferable—that being under-represented in training data might allow one to evade mass surveillance—incomplete datasets may result in detrimental false-positive identification. Thus, not being “seen” by AI does not protect against being “mis-seen.”

Daniel Susser (discussant)

The first contribution of this article is to characterize this tension between privacy and fairness in the context of algorithmic bias mitigation. In particular, this article argues that the irreducible paradox underlying current efforts to design less biased algorithms is the simultaneous desire to be both “seen” yet “unseen” by AI. Second, the Article reviews the viability of strategies that have been proposed for addressing the tension between privacy and fairness and evaluates whether they adequately address associated technical, operational, legal, and ethical challenges. Finally, this article argues that solving the tension between representation and surveillance requires considering the importance of not being “mis-seen” by AI rather than simply being “unseen.” Untethering these concepts (being seen, unseen, vs. mis-seen) can bring greater clarity around what rights relevant laws and policies should seek to protect. Given that privacy and fairness are both critical objectives for ethical AI, it is vital to address this tension head-on. Approaches that rely purely on visibility or invisibility will likely fail to achieve either objective.

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The Legal Construction of Black Boxes

Elizabeth Kumar

Elizabeth KumarAndrew Selbst, and Suresh Venkatasubramanian will present their paper, The Legal Construction of Black Boxes, on Saturday, September 25th at 10:00am at #werobot 2021. Ryan Calo will lead the discussion.

Abstraction is a fundamental technique in computer science. Formal abstraction treats a system as defined entirely by its inputs, outputs, and the relationship that transforms inputs to outputs. If a system’s user knows those details, they need not know anything else about how the system works; the internal elements can be hidden from them in a “black box.” Abstraction also entails abstraction choices: What are the relevant inputs and outputs? What properties should the transformation between them have? What constitutes the “abstraction boundary?” These choices are necessary, but they have implications for legal proceedings that involve the use of machine learning (ML).

Andrew Selbst

This paper makes two arguments about abstraction in ML and legal proceedings. The first is that abstraction choices that can be treated as normative and epistemic claims made by developers that compete with judgments properly belonging to courts. Abstraction constitutes a claim as to the division of responsibility: what is inside the black box is the province of the developer; what is outside belongs to the user. Abstraction also is a factual definition, rendering the system an intelligible and interrogable object. Yet the abstraction that defines the boundary of a system is itself a design choice. When courts treat technology as a black box with a fixed outer boundary, they unwittingly abdicate their responsibility to make normative judgments as to the division of responsibility for certain wrongs, and abdicate part of their factfinding roles by taking the abstraction boundaries as a given. We demonstrate these effects in discussions of foreseeability in tort law, liability in discrimination law, and evidentiary burdens more broadly.

Suresh Venkatasubramanian

Our second argument builds from that observation. By interpreting the abstraction as one of many possible design choices, rather than a simple fact, courts can surface those choices as evidence to draw new lines of responsibility without necessarily interrogating the interior of the black box itself. Courts may draw on evidence about the system as presented to support these alternative lines of responsibility, but by analyzing the construction of the implied abstraction boundary of a system, they can also consider the context around its development and deployment.

Ryan Calo

Courts can rely on experts to compare a designer’s choices with emerging standard practices in the field of ML or assign a burden to a user to justify their use of off-the-shelf technology. After resurfacing the normative and epistemic contentions embedded in the technology, courts can use familiar lines of reasoning to assign liability as proper.

 

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We Robot 2021: Ten Year Anniversary — New Dates!

We Robot 2021 is proud to celebrate its 10th anniversary at the University of Miami School of Law. We have changed the dates to Sept. 23-25, 2021. We hope you will join us live, but we’re making plans for a virtual backup (or even perhaps….parallel….just in case.)

More info …. soonish….

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